Brenna Lenoir, SVP of Marketing at CodeSignal, held a fireside chat with Cassie Kozyrkov, Google’s first Chief Decision Scientist, about the transformative role of AI in the tech industry. 

Known for founding the field of Decision Intelligence at Google, Kozyrkov now is Founder and CEO of a stealth AI startup and runs Data Scientific, an elite agency that helps world leaders and chief executives optimize their biggest decisions.

In this post, we’ll revisit some of the core themes from their conversation, including Kozyrkov’s advice on how business leaders today should approach AI, key AI literacy skills for leaders, and how AI is shaping the future of business. A recording of the fireside chat is available for viewing here. 

Cassie Kozyrkov on AI and business leadership: 6 key takeaways

1. Don’t adopt AI for the sake of adopting AI. Kozyrkov warned business leaders about adopting AI solutions without first determining that AI is needed to solve a business problem. When leaders are thinking about adopting AI, Kozyrkov suggests, the best questions to start with are: ”What are my business objectives? And how can AI help me meet those?”

When using automating processes, for instance, AI should only be used when traditional automation methods, which give engineers greater control, aren’t up to the task. Adopting AI should be an “act of desperation,” Kozyrkov explains, that leaders opt for when tasks are too complex for traditional automation 

2. Domain knowledge (still) matters. Delegating your decision-making entirely to AI isn’t feasible—or advisable, says Kozyrkov. To make sense of your data and employ AI in ways that align with your business objectives, you need people who know the context of your business and who can evaluate the quality of the data. Poor quality data will lead to poor quality AI outputs.

3. There are 3 questions leaders need to ask themselves when adopting AI. Also known as the “Kozyr criteria,” these are 3 questions that Kozyrkov says will help leaders understand how AI works in a way that matters for their business. They are:

1. What is the objective of the AI system? Knowing what the system is optimized for, and what “success” looks like, is the first step to understanding how it works.
2. What data set are you going to use? AI systems are built using huge collections of data, from which they learn to identify patterns and connections. Understanding the quality of the data and where it came from is the next step toward understanding an AI system.
3. How will you test it? This is where the domain expertise comes into play. It’s important to have people on your team who can critically evaluate the outputs of your AI system and ensure that it’s working as it’s meant to.

4. AI presents new challenges for managing talent. As AI opens up a new world of possibilities for automation, many of the manual tasks that make up employees’ day-to-day activities may soon become obsolete. A key challenge for leaders will be figuring out how to manage talent and measure employees’ value on the basis of the non-automatable thinking work they do. 

The solution, Kozyrkov explains, cannot be simply asking employees to do 40 hours of thinking a week once all their other tasks are automated—humans can’t spend that much time just thinking. How leaders will manage and measure work products is a pressing and complex problem to be solved. 

5. The core AI literacy skills that leaders need aren’t technical. In fact, Kozyrkov says, leaders really don’t need to get into the weeds of how AI systems work technically at all. Instead, it’s much more important that they understand the objectives of the system (the Kozyr criteria outlined above), and hone what she calls “hard-to-automate skills.” These include decision-making skills, creativity, social skills, trust, and collaboration.

6. Moving forward, the tech industry needs more nuance in its approach to AI. AI’s power to automate processes, personalize outputs, and make sense of data in ways never before possible can be tantalizing for tech leaders—it’s easy to want to adopt AI-powered solutions for every problem. But, leaders need to think carefully about the problems they’re solving and how important it is to get it right when adopting AI. 

Think of the implications of an AI system that analyzes brain tumors, Kozyrkov suggests. What are the ethical implications if the system skews toward misidentifying benign tumors as malignant? Or misidentifying malignant tumors as benign? These are the types of nuanced, high-stakes ethical questions leaders need to be thinking about as they integrate AI into their business decision-making. 

Take the next step

The AI future is already here. Want to dig deeper into the technical skills needed to use and even build AI systems? Master these skills with CodeSignal’s learning paths in business-critical AI and ML skills. Sign up to get started for free.

The post What business leaders need to know about AI appeared first on CodeSignal.

Whether you’re a recent graduate, an IT professional, or contemplating a career change, this article will walk you through what it takes to become a cloud engineer.

According to Indeed, cloud engineers earn an average salary of $120,512 per year. However, at big tech companies like Google and Meta, salaries soar even higher, with Meta offering an average of $252K.

High pay reflects the high demand for these roles. Organizations of all kinds rely on the cloud for various purposes, from data backup and software development to running large web applications. If you’ve played Fortnite with friends or marathoned cooking shows on Netflix, you’ve experienced the power of the cloud.

In this guide, we walk you through the essential concepts of cloud engineering, the skills needed to launch your career, and the best ways to learn them. If you’re eager to start right away, CodeSignal Learn offers free courses in a variety of technical topics, including cloud engineering. For a broader look at becoming a software engineer, check out this guide.

Buckle up as we head for the clouds.

Jump to a section:

• Roles and requirements 
• Skills you need to become a cloud engineer
• Roadmaps to proficiency in AWS, Google Cloud, and Azure
• How to launch your cloud engineering career with CodeSignal Learn

What does a cloud engineer do?

At its most basic, the cloud infrastructure refers to the servers accessed over the internet and the data that lives on those servers. Cloud engineering lets companies and organizations move business applications and processes back and forth across private, public, and hybrid cloud systems. 

The ones in charge of the show and making sure everything runs smoothly? You guessed it—the cloud engineers. They maintain the cloud infrastructure and systems, and manage many of the operations underneath. 

• They plan, develop, and design cloud-based software and applications, often working with vendors like AWS or Google Cloud Computing Services.
• They oversee data migration to cloud-based applications.
• They monitor systems, solve problems, and debug throughout development.
• They collaborate with other teams, especially cybersecurity and IT, to assess and protect the cloud architecture.
• They’re responsible for system maintenance and stay up to date on developments.

Today’s forecast: cloudy with a chance of more jobs

Cloud engineering jobs aren’t just found at tech giants like Nvidia or Google. We’re in a cloud revolution—industries like healthcare, finance, and retail are constantly on the hunt for more engineering experts. High demand makes it a versatile and lucrative career choice.

According to the U.S. Bureau of Labor Statistics, employment of computer and information research scientists is projected to grow 23 percent from 2022 to 2032, much faster than the average for all occupations.

Cloud engineer role requirements & responsibilities

From technical skills to communicating with the rest of your organization, cloud engineers have a range of duties on their plate. Some of these include:

• Manage cloud environments. The core part of your job. You’ll oversee the configuration, deployment, and maintenance of cloud infrastructure. This starts with discovery—what are your org’s specific needs? What provider will you use? From optimizing resources to managing costs, you’re a key player in keeping the ship running smoothly.

• Stop working in silos. Cloud engineers work closely with system administrators, cybersecurity teams, and business stakeholders. You’ll want to align development strategies with team objectives and wider goals. This comes into play especially when a business needs to scale.

• Automate cloud deployments. “How can we streamline this process?” is on everyone’s mind. When it comes to the cloud, you’ll be asked to provide answers. Cloud engineers write code to increase cloud efficiency, accelerate application delivery, and reduce human error. Thinking holistically, with a deep knowledge of engineering solutions, will be your biggest asset here.

• Handle scalability and reliability of applications. If you’re at a small gaming company that suddenly attracts millions of users, you’ve got scaling problems that need to be solved fast. Cloud engineers design frameworks that support horizontal and vertical scaling, and balance the load to prevent crashes. 

• Data security and compliance. Confidentiality and protecting user data are fundamental to any organization. Cloud experts need to have their eye on the ball when it comes to security. This includes understanding encryption, access controls, and network security. 

What are the typical qualifications you need to become a cloud engineer?

A college degree is the most common qualification needed to get your foot in the door. But online learning platforms are hacking new ways forward to unlock access for more future engineers. 

Let’s cover the primary ways to land your first cloud engineering job.

• Bachelor’s degree in Computer Science. A B.S. or B.A. is the traditional way to entry-level cloud engineering positions. Choose a computer science, computer engineering, software engineering, or IT program and learn coding, database management, and cloud computing. But that typically means four years of full-time study.
• Certifications (AWS, Azure, Google Cloud). The big players offer their own certification programs that are industry-recognized for teaching fundamental skills critical to the job. These courses will build your confidence without breaking the bank. 
• Hands-on experience. No one can deny the importance of hard-fought skills you learned on the job. If you’ve been in charge of cloud infrastructure, say at a startup or small organization, you’ve been around the block. Highlight this to employers.
• Have an understanding of DevOps practices. If you’ve been part of a development team, you’ve seen the process of deploying products or updates amidst the chaos. This experience could be your stepping stone into the world of cloud engineering.

Can you become a cloud engineer without a degree?

Yes! It’s definitely possible. 

Don’t have a bachelor’s degree on your resume? Don’t panic. No career path is the same. Many cloud engineers have transitioned or found jobs through bootcamps, online courses, by being self-taught, and obtaining certifications. And who can deny the value of previous practical experience? 

If you’re looking to gain proficiency, CodeSignal Learn offers a series of courses in cloud engineering that are actually fun to do. Bite-sized lessons and hands-on practices, plus an AI tutor named Cosmo who helps you when you get stuck, keeps you motivated from one class to the next. Once you skill up, you’ll feel more ready to start contacting recruiters and applying for jobs.

What kind of companies employ cloud engineers?

Cloud engineers are in high demand across various sectors. Tech startups, multinational corporations, and cloud service providers are your best bet when job hunting. These companies need professionals to manage and optimize their cloud infrastructure.

What skills do you need to become a cloud engineer?

• Technical foundations. Like roots supporting a tree, you need a solid foundation for a strong career in cloud engineering. Skip a few steps and the whole thing could fall apart. Your base should include proficiency in programming languages, operating systems, and networking concepts.
• Programming languages. Cloud engineers should be proficient in Python, Java, Ruby on Rails, JavaScript, and SQL. These are the most common languages for cloud development and automation tasks.

Learning tip: CodeSignal Learn offers an extensive library of courses to help you master these languages, even if you’re a beginner.

• Linux operating systems. Love Linux? Then you’re off to a good start. Cloud engineers only look stronger with a solid understanding of Linux operating systems, since many environments are built on them. Key concepts include command line proficiency, system administration, network configuration, and security enhancements.
• Understanding of networking concepts. A core part of your job is making sure data flows smoothly and securely—and that your chosen cloud service works reliably and without any hiccups. You’ll need to have a strong grasp on concepts like TCP/IP, VPN, cloud networking, firewalls, and network protocols. 

Knowledge of key cloud computing platforms and features

When it comes to providers, cloud engineers usually have their favorites. Below is a list of the big players in the space. It’s worth knowing and mastering at least one of them. Scroll down further to learn how to get certified.

• Amazon Web Services (AWS)
• Microsoft Azure
• Google Cloud Platform (GCP)
• Oracle Cloud
• IBM Cloud

Along with understanding the nuances of a provider, you’ll also want a general understanding of virtual machines (VM). 

Know the fundamentals of cloud deployment technologies like Docker and Kubernetes

Beyond languages, you’ll also want to know two specific tools relevant to cloud engineering: Docker and Kubernetes. Docker is a platform that allows you to package applications into lightweight, portable containers that run across various environments. Kubernetes automates the deployment, scaling, and management of these containers. The platform simplifies handling complex microservices architectures. 

Role-specific skills

Even when they’re Individual Contributors (ICs), cloud engineers are more than just coders. They need a broad range of skillsets to thrive.

• Management and leadership skills. Anyone can lead from any position. Being a great cloud engineer means taking the initiative, seeing beyond your own scope of responsibilities, and committing to continuous improvement and collaborative success. 

• Compliance management. Compliance is an ongoing process that includes regular assessments, audits, and updates to make sure the services you’re running follow regulations, which change frequently. Know the nuances of your specific industry—because different standards apply depending on your customers and workflow.

• Risk assessment. If you hear about security breaches happening on the internet, you’re on the frontlines to see if your organization was impacted. Cloud engineers need to be vigilant and alert with any potential risks to the business. 

Communication skills

Although your happy place might mean staring at lines of code and debugging from your desk while blasting EDM, cloud engineers need to be able to communicate with others. That might mean over Slack, over email, or in person. Here’s what to know.

• Know how to collaborate with a team. Engineers generally work in teams, and cloud engineers follow suit. You’ll likely work with other software devs, project and product managers, security specialists, cloud architects, and leaders on the business side. You may report to the CTO, or meet with them frequently. Become a team player, know everyone’s roles, and go further together.
• Learn the basics of technical documentation. As a cloud engineer, rock-solid technical documentation translates complex cloud jargon into plain English for everyone else. Writing the roadmap keeps knowledge accessible, makes onboarding smoother and troubleshooting easier. Tech writing has its own style and format, and there are courses to help you learn them. 
• Develop your presentation skills. Every so often, you may be called up to share work or updates to the entire company. Don’t expect to deliver a 20-minute TED talk on cloud infrastructure. Picture a short segment where you’ll share developments, improvements, or news related to the cloud. Know your content, practice it, and ditch the tech jargon for simple language.  
• Stakeholder management.  Since your area of expertise crosses paths with multiple departments, you’ll likely interface with many leaders across the organization. Come ready to handle different perspectives and concerns as you propose ideas related to cloud management. Say you want to switch providers from AWS to GCP. You’ll need to provide a strong rationale, structure, and secure buy-in from your team.

Routes for becoming a cloud engineer in 2024 and beyond

We’ve covered the responsibilities and skills part of the job. Now let’s learn how to get there. Ready to steer your own career into the cloud? Here’s how to do it quickly and effectively.  We’ll explore three routes to get there—and none of them require four years of college. Yay to that. 

Gain proficiency in one or more cloud computing platforms

Trying to master every single platform is a Herculean task. A smarter way? Get well-versed in one platform. You might even discover there’s some overlap from one provider to the other, making it easier to expand your depth of knowledge as you progress.

Here a few ways to accomplish this:

• Platform certifications. Such as those offered by AWS, Google or Azure, which validate you as an expert in the field.
• Cloud workshops. Hands-on training or webinars which keep your skills current.
• Real-world applications. Practical experience, or learning on the job.
• Advanced configurations. Learn how to set up and manage Virtual Private Clouds (VPCs), configure auto-scaling and load balancers, and build other customizations to optimize network architecture.

Earning cloud computing certifications & credentials

Completing a cloud engineering course from a specific provider is like getting your driver’s license, but only for one specific car. But the good news—you’ll graduate as a professional who’s able to hit the road with a tank full of JavaScript.

• AWS Certified Solutions Architect
• Microsoft Certified: Azure Fundamentals
• Google Cloud Professional Cloud Architect

Heads up: Some certifications are only valid for a fixed amount of time, like 2 or 3 years. When that expires, you’ll need to repeat the course to keep your certification current.

Navigating into specialized roles within cloud engineering

Let’s drill down further and look at what it takes to find your way into the cloud, one provider at a time.

How to Become an AWS Cloud Engineer

Transforming yourself into an AWS cloud engineering expert means diving headfirst into the AWS ecosystem and swimming in its ocean of management tools and certifications. Here’s how you can get started:

• Understand the AWS Ecosystem. Get familiar with key AWS services: EC2, S3, RDS, and Lambda. Know how the AWS components interact with each other to build cloud solutions. Pay attention to big developments or releases.

Learning tip: CodeSignal offers a free engineering learning path tailored specifically to AWS and learning Python. By the end, you’ll be proficient in automating and building cloud solutions using AWS services.

• Know the AWS Management Tools. Play around with tools like AWS Management Console, your dashboard for all things AWS. 
• Obtain AWS Certifications. Start with the AWS Certified Solutions Architect—Associate certification to build a solid foundation. As you advance, you can add more certifications to your toolbelt, like the AWS Certified DevOps Engineer.
• Get hands-on experience: Work on real-world projects using AWS services and see the systems in action.  

How to Become a Google Cloud Engineer

The other big cloud kahuna is Google. Here’s how to become an expert with them.

• Learn Google Cloud Platform services: Get familiar with core GCP services like Compute Engine, App Engine, Kubernetes Engine, and BigQuery. These services form the backbone to GCP solutions.
• Focus on Data Management: Sharpen your skills with tools like Cloud Storage, Bigtable, and Dataproc. 
• Explore AI and Machine Learning tools: Google Cloud offers AI and machine learning tools like TensorFlow, AI Platform, and AutoML. Become an AI whiz (with the help of CodeSignal Learn’s courses in TensorFlow and other AI tools) and get an edge over other job applicants.
• Earn your GCP Certifications: Get started with the Google Associate Cloud Engineer certification, then tack on more advanced ones like Professional Cloud Architect and Professional Data Engineer.
• Get social with the Google Cloud community: Join the online community through forums, meetups, and conferences. 

How to Become an Azure Cloud Engineer

To become an Azure cloud engineer, shift your focus on Azure infrastructure, hybrid cloud environments, and Microsoft certifications. Behold the roadmap:

• Learn the Azure infrastructure: Start scrolling and get familiar with their core services like Azure Virtual Machines, Azure Blob Storage, Azure SQL Database, and Azure Functions.
• Understand the Azure hybrid cloud feature: The Azure Hybrid benefit allows you to use your on-premises Windows Server licenses to pay for Azure Virtual Machines. It can save up to 40 percent on your Azure VM costs. If a prospective company hosts with Azure, get up to speed right from the source.
• Leverage Azure DevOps: Level up your skills in Azure DevOps and deploy like a boss.
• Earn your Microsoft certs: Start with the Microsoft Certified: Azure Fundamentals cert to build up a base. Then advance your way with further credentials.
• Focus on enterprise: Want to join a large company? Learn how to integrate Azure services with enterprise systems like Active Directory and Office 365. 
• Get social: Join and participate in Azure communities, forums, and events to stay on top of the latest Azure happenings and best practices. 

Accelerate your career in cloud engineering with CodeSignal Learn

Cloud engineering jobs are in demand in today’s digital world. It’s a high-paying career with lots of room for advancement as you gain experience and deepen your bench of knowledge. The role is brain-intensive, never boring, and develops a skill set that goes far beyond coding. 

Taking that first step may feel daunting—that’s where CodeSignal comes in. 

• Courses to the moon. We have a full suite of courses that will turn you into a cloud engineering expert. From learning data structures to Python programming, the platform builds the foundation you need to become more skilled and sought-after in today’s fast-moving market. 
• Study at your own pace. Learn on your terms with bite-sized, practice-based modules that simulate on-the-job experience. 
• Get help from a space corgi. Our built-in AI tutor, Cosmo, sits by your side as you figure out challenges and hit your learning goals. If you’re stuck, he’ll give you personalized feedback on your code.

 Want in? Sign up for free to get started!

The post How to become a cloud engineer: Skills & career guide appeared first on CodeSignal.

JavaScript is a powerful and versatile programming language that has become an essential tool for web development, interactive applications, and server-side scripting. By learning JavaScript, you’ll be able to build dynamic websites, create sophisticated web apps, and even develop mobile apps using frameworks like React Native. 

This guide is designed for anyone interested in mastering JavaScript. Whether you’re a student, a career changer, or an experienced developer looking to expand your skill set, you’ll find valuable insights and practical advice tailored to your needs. 

We’ll start by addressing common concerns and set realistic expectations for your learning journey.

Jump to a section:

• Is JavaScript hard to learn?
• What are the benefits of learning JavaScript?
• What do I need to start learning JavaScript?
• What’s the best way to start learning JavaScript in the age of AI?
• What should I focus on for learning JavaScript for web development?
• The biggest mistakes people make learning JavaScript and how to avoid them
• Accelerate your JavaScript learning with CodeSignal Learn

Is JavaScript hard to learn?

Learning any programming language can be challenging, especially if it’s your first one.  But JavaScript is often considered beginner-friendly for these reasons:

1. Real-time feedback: When you write JavaScript code, you can see the results instantly in your browser. Modern browsers come with developer tools that allow you to inspect, debug, and modify your code without needing a compiler. This fast feedback loop is great for learning. You can see the direct impact of your code changes instantly.
2. Built-in browser support: JavaScript is built into all modern web browsers, so you can start coding without installing additional software. This lowers the barrier to entry and allows you to begin coding and seeing results sooner. 
3. Familiar programming concepts: Basic programming fundamentals like variables, loops, functions, and conditionals exist in JavaScript. If you have experience in another language, these concepts can make the learning curve less steep. 
4. Widespread applicability: JavaScript is essential for web development but can also be used for mobile app development (React Native), server-side development (Node.js), game development, and more. Its versatility is another bonus for beginners.
   

What is a realistic timeframe to become proficient in JavaScript?

“Proficient” is a relative term, so setting an exact timeline to master JavaScript is difficult. Bootcamps teach the basics of JavaScript in about three months, but then bootcamp graduates continue learning the language during their first job and beyond. The time it takes to become skilled at JavaScript depends on your prior programming experience, desired learning pace, and dedication to practice.

As a rough estimate, expect it to take 3 to 12 months to get comfortable with the language fundamentals. However, proficiency is a continuous journey, and even experienced developers keep learning and improving their skills. Set realistic expectations, track your progress, and celebrate milestones along the way. Focus on consistent practice and work on side projects that challenge you. Everyone learns at their own pace, so don’t compare your progress to others. 

How does JavaScript learning difficulty compare to other programming languages like Python and C++?

Learning a new programming language is challenging, but the degree of challenge varies depending on the language’s syntax, paradigms, and concepts, as well as your previous experience. JavaScript and Python are generally considered more beginner-friendly than C++. 

It also depends on where you’re starting from. If you’re coming from a statically typed background (e.g., Java or C++), you might find the dynamically typed nature of JavaScript too loose and confusing at first. Compared to C++, JavaScript has a more flexible syntax and a gentler learning curve. 

Python is known for its simplicity, readability, and extensive standard library, which makes it an excellent choice for beginners. Starting with Python might be a better choice if your goal is data science, machine learning, or scientific computing. Check out this Python interview guide if you want to level up your skills.

On the other hand, if you’re primarily interested in web development, you should learn JavaScript because it’s the native language of the web. While Python can be used for web development with frameworks like Django and Flask, JavaScript’s seamless integration with web browsers makes it an indispensable skill for web developers today. 

Consider your goals, prior experience, and learning style when choosing a language to learn. JavaScript generally offers a gentler learning curve to C++ , but it still requires dedication to master its quirks. Learning Python makes sense if you’re aiming for the data science world. C++ requires you to deal with advanced concepts like pointers and memory management from the start, but that might be good if you aim to get into game development, digital signal processing, or low-latency finance applications. For those looking to practice their C++ skills, CodeSignal Learn offers a learning path designed to help you prepare for technical interviews in C++. 

What are the benefits of learning JavaScript?

Learning JavaScript opens up a world of opportunities in web development, full-stack engineering, and more. As the language of the web, JavaScript enables you to build dynamic, interactive websites, and web applications. Its versatility is a strong benefit—you can develop user interfaces and responsive designs with JavaScript. With the rise of Node.js (a JavaScript runtime), you can write server-side code, which means you can develop full-stack applications in the same language. 

JavaScript’s reach extends to mobile app development through frameworks like React Native, allowing you to build cross-platform mobile apps for iOS and Android. And, the demand for JavaScript skills in the job market is consistently high. According to the Stack Overflow Developer Survey 2023, JavaScript has been the most commonly used programming language for the eleventh consecutive year, cementing its position as a critical skill for developers. By learning JavaScript, you position yourself to take advantage of abundant career opportunities in web and cross-platform development. 

Benefits for students and recent graduates

Learning JavaScript is a natural way for students and recent graduates to kickstart their careers in web development. Demonstrating your proficiency in JavaScript will make you an attractive candidate for internships and entry-level positions. 

You can apply your JavaScript skills to various academic and personal projects—like building real-time chat applications or responsive e-commerce sites. Incorporating JavaScript into these applications will enhance their functionality and user experience, as well as showcase your practical problem-solving skills. 

Building a solid portfolio is key to catching the attention of potential employers. If you’re applying for web development positions, recruiters will look for evidence of your skills. Participate in JavaScript hackathons, contribute to open-source projects, or create your own libraries or extensions to showcase your skills in JavaScript.

Benefits for career changers 

Learning JavaScript can open up new career opportunities for professionals looking to transition into the tech industry. If you’re a data analyst, for example, your existing skills combined with JavaScript coding ability will enable you to build interactive data visualizations and create engaging reports. This can open doors for you as you change roles.

JavaScript’s versatility and high demand also provide a degree of flexibility and marketability for career changers. As you upskill and gain proficiency in this language, you’ll become better positioned to take on front-end developer, full-stack developer, or even product manager roles. 

Benefits for developers

Experienced developers can expand their skill set by learning JavaScript. Backend engineers can become full-stack engineers by using JavaScript to write client-side code. Even if you’re unlikely to spend most of your time developing user interfaces, gaining fluency in JavaScript can be helpful to your team. JavaScript skills can be helpful for solving bugs, remediating incidents, or collaborating with your front-end engineers more effectively. 

JavaScript boasts an extensive ecosystem of popular libraries and frameworks such as React, Angular, Node.js. Familiarity with these tools can open doors for seasoned developers to work on cutting-edge web development projects and advance their careers. 

What do I need to start learning JavaScript?

You don’t need much to get started learning JavaScript. There’s no need to install expensive software or buy fancy tools, and you don’t need a mathematical or computer science (CS) background. You only need an internet connection, a web browser, and commitment to learning. 

More important than a CS background are strong problem-solving skills. These skills can come from a wide range of experiences: solving puzzles, playing strategy games, playing sports, and managing projects. Writing code involves breaking down problems into smaller manageable parts and working to find solutions. So if you enjoy challenges and have a curious, analytical mindset from whatever background, you’ll be well-suited to learning JavaScript. 

A basic understanding of programming concepts such as variables, loops, and functions will be helpful. For example, knowing that functions are reusable blocks of code will give you a head start. But if you don’t know those yet, you can learn them along the way! Familiarity with HTML and CSS is beneficial because JavaScript is often used to manipulate web pages, such as responding to what users type on a website or fetching information from external websites.

For tools and software, you’ll need a text editor and a web browser. A host of popular text editors and modern browsers are free to download and easy to set up. Because JavaScript is very popular, there are plenty of excellent resources online to help you learn. Some examples include: 

• JavaScript.info: This resource offers modern and extensive JavaScript documentation for introductory and advanced concepts.
• MDN JavaScript Guide: This comprehensive guide covers JavaScript fundamentals, advanced concepts, and best practices. It’s used by both beginners and experienced developers. 
• JavaScript Programming for Beginners on CodeSignal Learn: This series of courses develops learners’ mastery of JavaScript syntax and core programming concepts through realistic practice exercises.
  

Step 1: Set up your development environment

You’ll need a development environment to start learning JavaScript. This typically involves installing a code editor or an integrated development environment (IDE), which provides a streamlined interface for writing, debugging, and running your JavaScript code.

Visual Studio Code (VSCode) is one of the most popular code editors. It offers excellent syntax highlighting, intelligent code completion, debugging tools, and a vast library of extensions that enhance its functionality. Other editors like Atom, SublimeText, and WebStorm are good choices too. 

In addition to a code editor, you’ll need a web browser with a built-in JavaScript console. Modern browsers like Google Chrome, Mozilla Firefox, Apple Safari, or Microsoft Edge come with developer tools that allow you to interact with JavaScript directly in the browser. To access the console, right-click on a web page, select “Inspect” or “Inspect Element,” and navigate to the “Console” tab. This is where you can test JavaScript code snippets, debug your scripts, and see the output of your programs. For an example of what you can do here, check out Google’s DevTools. 

When you’re ready to explore server-side JavaScript development, you’ll need to install Node.js, a JavaScript runtime built on Chrome’s V8 JavaScript engine. Node.js allows you to run JavaScript on the server, enabling you to build scalable, high-performance web applications. To install Node.js, visit the official Node.js website and follow the installation guide for your operating system. Once installed, you’ll have access to the Node.js package manager (npm), which allows you to install and manage JavaScript libraries and tools.

Step 2: Learn the basics of HTML & CSS 

Before learning JavaScript, it’s helpful to have a solid foundation in HTML (which stands for Hypertext Markup Language) and CSS (which stands for Cascading Style Sheets). HTML defines the structure and content of a webpage, while CSS controls its visual presentation and styling.

Learning HTML and CSS will help you understand how to create the basic building blocks of a website. The Mozilla Developer Network (MDN) offers comprehensive documentation on HTML and CSS. 

While it’s possible to create web pages using only HTML and CSS, integrating JavaScript adds interactivity and dynamic behavior to your website. To get started with HTML and CSS, you can follow online tutorials, take courses, or refer to the official documentation. 

Learning tip: CodeSignal’s Comprehensive Introduction to Front-End Engineering learning path takes you through the basics of HTML and CSS, as well as an introduction to developing responsive websites using JavaScript.

Step 3: Start with JavaScript fundamentals. 

Once you have a basic understanding of HTML and CSS, you can dive into the core concepts of JavaScript. If you are new to programming, you must learn the basic programming fundamentals—variables, data types, if/else statements, loops, and functions. These exist in nearly all languages, but each has its own implementation, syntax, and quirks. 

After learning those, you should progress to learning JavaScript-specific concepts such as how data structures like arrays and objects work in JavaScript, and how DOM manipulation and event handling work. Practice small coding challenges and exercises here to reinforce your learning. Don’t be discouraged if you run into difficulty–this is how everyone learns! Problem-solving is an essential part of the learning experience. 

Learning tip: Get started with our JavaScript Programming for Beginners path. It contains seven courses to help you improve your JavaScript skills. 

What’s the best way to start learning JavaScript in the age of AI?

The fundamentals of learning JavaScript remain the same in spite of recent AI developments. It’s best to learn and master the basic concepts so you have a solid foundation in the language’s syntax, data structures, and data types first. AI-based tools can complement and enhance your learning journey, but if your goal is to learn JavaScript, you shouldn’t skip learning the basics. 

There are many ways to learn JavaScript, including self-study, online courses, and bootcamps. You can use books, tutorials, and online documentation to learn on your own, or you can learn alongside others with structured curriculum in courses and bootcamp programs. 

Step 1: Understand JavaScript basics and choose a learning path

Before diving into a structured learning plan or choosing an online course, you may want to explore introductory material that provides an overview of JavaScript and its capabilities. Mozilla Developer Network’s What is JavaScript? guide is an excellent resource for understanding the fundamentals of this programming language and what it can do. 

Once you understand what JavaScript can do, you can design a structured learning plan. Self-study can be a practical option, combining resources like online tutorials, coding challenges, and practice exercises. Consider how you learn best (e.g., books, video tutorials, self-paced, or group-based online classes) but the most important thing is dedicating time to writing JavaScript code. Alternatively, you can enroll in an online course from platforms like CodeSignal Learn, which offers structured curricula tailored for beginners.

Step 2: Read books and listen to podcasts to keep learning JavaScript

Supplement your chosen learning path with additional materials like books, YouTube tutorials, podcasts, and blogs. Here are some examples to get you on your way:

• Books:
  • Eloquent JavaScript by Marijn Haverbeke
  • You Don’t know JS by Kyle Simpson
• YouTube channels:
  • JavaScript Mastery
  • Traversy Media
  • Wes Bos

Accelerate your learning with personalized learning pathways and AI-assisted tools

AI-powered code completion tools like GitHub Copilot and IDE-code completions can help you write code more efficiently. They can save you time and reduce the cognitive load of remembering syntax, so you focus more on problem-solving and logic. However, it’s crucial to understand the code suggestions and not blindly accept them. These tools are based on large language models (LLMs) and can generate incorrect code, so blindly accepting their suggestions can lead to bugs in your codebase. 

However, you can use LLMs to learn JavaScript if you take the time to understand the code. You can ask ChatGPT and Claude to explain code snippets at different levels of complexity (e.g., “Can you explain this code to me like I’m five?”) until you grasp the underlying concepts. You can keep doing this repeatedly until you gain a true understanding of code blocks. 

Another place AI can really help you learn JavaScript is through personalized learning pathways. At CodeSignal, we have an AI tutor called Cosmo who is integrated into our CodeSignal Learn platform. Cosmo adapts to your unique skills journey and meets you where you are. A few reasons why you might enjoy working with Cosmo:

• 1-on-1 support: Cosmo provides refreshers on technical concepts and can help debug your code if you get stuck.
• Personalized feedback: Cosmo gives you feedback based on your performance completing JavaScript exercises. This approach will help you focus on the most necessary parts of your JavaScript learning journey. 

Learning Tip: Want to learn the skills needed to pass technical coding interviews in JavaScript? The Mastering Algorithms and Data Structures in JavaScript learning path will help you understand tricky concepts and ace those interviews. 

Step 3: Practice, practice, practice 

Consistent, deliberate practice is the key to mastering JavaScript. As you progress through your chosen learning path, you should regularly solve coding challenges and work on small projects. Websites like CodeSignal and LeetCode offer a variety of programming problems for you to solve. Don’t be afraid to experiment with different approaches. The more you practice, the more comfortable you’ll become with JavaScript.

As you gain more experience, you might consider exploring popular JavaScript frameworks like React, Angular or Vue.js. These tools can help you build more complex and interactive applications, but it’s essential to have a solid grasp of JavaScript fundamentals before diving into them. You can even experiment with building the same apps in Vanilla JS (without any frameworks) and with frameworks to deepen your learning. 

Step 4: Build a project portfolio

Building side projects and documenting your development journey is an excellent way to showcase your skills. You should consider building small applications that solve problems for you or projects that encourage you to learn new APIs, new design paradigms, or new UI patterns. Documenting your process and showing how you overcome your technical challenges will make you attractive to potential employers. 

Find open-source projects that are aligned with your interests. Contribute small fixes to those repositories and analyze how more experienced engineers write their code. Host your own code on GitHub and invite people to contribute to them. Over time, this process of building small apps, writing code, debugging, finding fixes on Stack Overflow, learning new techniques, and getting feedback will accelerate your growth. And by the end of it, you’ll have a portfolio of projects you can describe to potential recruiters to highlight your technical skills. 

What should I focus on for learning JavaScript for web development?

If your goal is to learn JavaScript for web development, it’s crucial to understand how JavaScript manipulates the elements of web pages. This is called DOM (which stands for Document Object Model) manipulation. You’ll want to learn how to select, create, modify, and remove HTML elements using native JavaScript methods like `getElementById()`, `querySelector()`, and `createElement()`. Mozilla Developer Network (MDN) provides great documentation for this, and onCodeSignal Learn, we offer a course on mastering the DOM. 

Once you’ve learned how to manipulate DOM elements, you should learn how to handle events. Events are actions that happen in the browser, such as a user submitting a form, or clicking a button, or scrolling the page. JavaScript allows you to respond to these events in custom ways you define. For example, when a user submits a form, you can use JavaScript to decide what happens next. If they scroll further down the page, you might want to fetch more information (e.g., images, videos, text) to show the user.

Event handling and DOM manipulation are two essential concepts to master if you want to use JavaScript for web development. While frameworks like React and Vue make these tasks easier, understanding the fundamental principles behind them will always give you an advantage and a better understanding of how things work under the hood. 

Learning tip: Comprehensive Introduction to Front-End Engineering, a learning path in CodeSignal Learn, includes a course on mastering the DOM API—plus, all you need to know to get started with web development using JavaScript.

The biggest mistakes people make learning JavaScript and how to avoid them

One common mistake when learning JavaScript is jumping into frameworks or libraries too soon without mastering the language fundamentals. While frameworks like React or Angular are powerful, they are built on top of JavaScript and abstract away key concepts. The JavaScript ecosystem is dynamic, so there will always be new frameworks and libraries emerging. Focusing on the fundamentals first will make it easier for you to learn new framework APIs or concepts in the future. You will also be more resilient and adaptable, enabling you to easily switch between different frameworks instead of being too locked down in one framework’s ecosystem.

Another mistake is focusing too much on isolated coding problems (e.g., Leetcode-style problem sets) without applying the knowledge to practical projects. Coding challenges are helpful for reinforcing concepts and preparing for interviews, but they don’t always translate to real-world development. Balance your learning by working on small applications or websites alongside coding exercises. This way, you’ll gain exposure to important development best practices like debugging an application, troubleshooting, working with external libraries, and maintaining a codebase.

Accelerate your JavaScript learning with CodeSignal Learn

Learning JavaScript can seem daunting at first, but with the right approach and resources, you can become proficient in this powerful language. In this guide, we suggest learning the basics of HTML and CSS, diving into JavaScript fundamentals, practicing consistently, building a portfolio of projects, and taking advantage of online resources—books, tutorials, podcasts, and AI-assisted tools—to supplement your learning. 

At CodeSignal, we’re committed to helping you learn JavaScript on our Learn platform. Our learning paths are designed to take you from beginner to proficient with real-world practice exercises, mastery-based learning,  and personalized feedback from Cosmo, our friendly AI tutor. Sign up for free and take the first step towards an exciting career in web development. 

The post How to learn JavaScript: A step-by-step starter guide appeared first on CodeSignal.

By becoming handy with data viz, as it’s often called, you’ll have a valuable new tool to apply to fields such as design, engineering, journalism, or data analysis. Someday, you might even be building and designing charts and other visual representations as a full-time job as a data visualization designer or engineer.

In this guide, we’ll walk you through the important concepts in data visualization, the tools of the trade, and how to learn them. Spoiler alert: CodeSignal Learn offers data visualization lessons for free. You can also find courses on the fundamentals of Python, which you’ll need to use to navigate the most common data viz libraries.

Hold onto your X-axis as we chart your way to data viz success!

Jump to a section:

• Introduction to data visualization
• Essential skills for breaking into data visualization
• Python tools and frameworks for data analysis and visualization
• Tableau and other software tools
• D3.js and tools for interactive visualization
• Where to learn data visualization online
• Accelerate your career in data visualization with CodeSignal Learn

Introduction to data visualization

Famed data visualization thinker Edward Tufte said, “The commonality between science and art is in trying to see profoundly—to develop strategies of seeing and showing.”

Data visualization does more than convert numbers into lines and shapes. Thoughtfully crafted charts can change minds, influence budgets, and inspire movements. Less dramatically, they’re a more efficient, easier-to-digest way to look at data than tables.

While statisticians and others have been visually representing data in print for over two centuries, the practice has shifted to computers and the internet. Today, there are three main ways to visualize data:

• Spreadsheet apps like Excel and Google Sheets make serviceable charts, and with some practice and a few tricks, you can customize them to some extent.
• Business intelligence and data analysis platforms, such as Tableau or Microsoft’s Power BI, offer many ways to manipulate and visualize large amounts of data from different sources without learning code.
• Code frameworks, including Matplotlib and Plotly for Python or d3.js for JavaScript, are the most flexible and powerful.

What is data visualization?

Data visualization transforms information into images. It’s usually based on numbers, whether tracking single data points (say, the daily high temperature in a certain place) or counts (for instance, the number of babies in Canada given the name Michael every year). 

Scottish engineer William Playfair introduced the world to line, bar, and pie charts nearly 250 years ago. Other types of charts came about in the 19th and 20th centuries, and in the 21st, interactive computing and an explosion in data have combined to make all sorts of visualization possible.

In this guide, we will focus on modern tools and techniques that use code or specialized platforms to create highly customizable and often interactive visualizations.

Why is data visualization important?

A picture may be worth a thousand words, but a chart can tell the story of millions of data points. Charts, maps, and even well-formatted tables can turn numbers into stories. Persuasive presenters use them to back up bold assertions. Curious investigators uncover patterns or anomalies in the lines, shapes, and shades. 

The science and art of data visualization have coevolved with the internet and Big Data. Enormous datasets and interactive charts open up so much of the world to exploration and understanding.

In dynamic situations, from monitoring company-wide sales to a region’s incidence of disease, decision-makers often rely on dashboards with many charts to stay on top of dozens or even hundreds of variables at once. In more static situations, such as looking at combinations of historical data to inform strategic choices, charts can be even more customized, with annotations and other design choices specific to the story to be drawn out of the data.

All indications are that data viz will continue to be an important part of any role that requires understanding or communicating around data.

Is data visualization easy to learn?

Whether you’ll find data viz easy to learn depends on your current skills, your aptitude for code, and how far you want to go.

You should be able to grasp the basics of no-code business intelligence (BI) software, such as Tableau or Power BI, within 10 to 15 hours of concerted study. Getting certified with BI software could take weeks to a few months.

To use Python data visualization libraries, you’ll need to learn the fundamentals of the Python programming language if you haven’t already. That can take a few months, but don’t stress—it’s become such a common language in part because it’s easier to learn than most others, and CodeSignal Learn can help get you there. Once you know Python, learning how to use the individual libraries is a matter of practice: you can follow our learning paths quickly, but mastery takes time.

If you’re not already up to speed with core statistics concepts, expect the process to go a bit slower. You’ll need to either familiarize yourself at the outset or look up terms as you go along.

What is the difference between data analysis and data visualization?

Data analysis and data visualization are very close friends. In fact, most data analysts end up doing some data visualization regardless of their particular training.

Data analysis covers all the tools and techniques for making sense of data. Much of data analysis involves manipulating data: sums, averages, comparisons, regressions, and the like. The results end up as one or multiple data points, which the analyst may display as a chart—and that’s where visualization comes in. The overlap between the two is in the choices about how to display data, such as the type of chart, what data to include and exclude, and how to scale the axes.

We enter the realm of pure data viz with aesthetics and interaction design. This includes choices made to improve understanding, such as colors and labels, and ways users can engage with charts, such as hovering, clicking to expand, and searching across particularly large data sets.

Essential skills for breaking into data visualization

As we’ve mentioned, data viz is a balance of art and science. We’ll start by discussing design considerations such as which type of chart to choose. Then we’ll look at the technical options that are commonly used in data visualization today.

Understanding the different chart types and how to choose between them

What type of data are you displaying and what story do you want it to tell? Are you looking at how a single factor changes over time? Are you comparing different data series? Is the data geographical? Does it follow a multi-step process? This analysis is the first part of your chart selection strategy.

Let’s examine some of the most common types of charts and when to use them.

Line charts

Line charts show points in a data series connected by a line. The Y-axis—the vertical one—represents the value of the data, while the X-axis—the horizontal one—specifies each point in the series. Most commonly, the X-axis is time, but it can be some other unit, such as price or size. A line chart can contain multiple lines, allowing the comparison of one or more data series.

Bar charts

Bar charts are used to compare the values of categorical variables, representing clear demarcations such as blood type or political party affiliation. (They can also be used for ranges like income bands or years.) A bar chart can compare one or multiple data series; usually, color is used to represent different types of data.

Pie charts

Pie charts show the components of a whole, whether on a percentage or absolute basis. A donut chart is simply a pie chart with a hole in the middle; which one to use is purely an aesthetic decision.

Histograms

Histograms look similar to bar charts, but they show the distribution of values within a given data series. A large data set is broken into chunks—say, household income in a country, broken down by $5,000 increments. Histograms show more nuance than basic statistical features like average and median.

Scatter plots

Scatter plots let you show every single piece of data in a series at once. You’ll often see these when comparing a sizable but manageable number of data points, such as statistics across countries of the world or the sports teams in a league. Often, they’ll have a best-fit line, which is a mathematical function that represents the overall trend of the data.

Bubble charts

Most bubble charts are scatter plots with variously sized circles adding another dimension of comparison. For instance, when comparing countries’ life expectancy, the circle size could represent the amount spent on healthcare per person. Color can denote a category, such as continent. There are also linear bubble charts, which are an alternative to line charts, and bubble clouds, which have no axes but simply represent labeled data points in proportionally sized circles.

Heatmaps

Heatmaps represent data values using color. These are particularly useful for showing data within a spatial context, such as on a map. They can also be used on a grid to highlight patterns that a simple line graph may not highlight as clearly, such as phenomena more likely to happen on a certain day of the week. In other cases, they simply make it easier to see the range of values in a large number of datapoints at a glance.

Treemaps

Treemaps are like pie charts, but square. They’re better when there are many parts to the whole, for a few reasons: squares are easier to compare than wedges, you have more space for labeling, and you can more easily cluster data points to show categories within the whole. Treemaps are often used for complex breakdowns like the components of a country’s economy. 

Choropleth maps

Choropleth maps use one or more gradients of color to represent the value associated with a geographic region, such as a neighborhood, state, or country. These can be visually stunning, but it can be hard to see the values of smaller geographic units.

Sankey diagrams

Not all data viz is done in charts that plot data. Sankey diagrams, named after the Irish captain who invented the concept, are a compelling way to show the magnitude and direction of flows between stages, such as data, money, or energy. 

Gaining a good grasp of the data visualization process

Successful data viz is more than knowing how to put points on a chart. You need to understand the data, the audience, the presentation format, and the capabilities and limits of the technology in your toolkit. You also need patience and persistence, as you rarely get the chart you want on the first try. Let’s walk through some visualization best practices.

Understanding the goal of the visualization

The first step is to understand what you’re trying to achieve. Consider the following:

• Argument. What are you saying with this chart? Is the takeaway clear? If you don’t have an opinion, what range of conclusions could you expect a viewer to take away? 
• Audience. Who’s going to see this chart? If multiple types of audiences, who’s most important? What do they already know? What’s important to them? What types of design, words, and information density will engage or repel their interest?
• Context. Where will this chart be seen? Is there text or other content nearby? Will it be near other charts? Can we expect our audience to linger on the chart, or will it have to make a clear point quickly?
• Medium. Will this be interactive or static? Printed or on a digital slide? How might it meet accessibility standards? Will it load quickly enough to maintain interest?
• Effort. Is this worth a lot of time to make excellent, or is quick and dirty good enough? Could you repurpose templates or other charts? 

Ability to analyze and understand the dataset

Open up the data, take a look at the labels of the columns, and peek into random rows to get a feel for the details.

In many cases, what you start with isn’t enough. You might need to do some pre-processing, such as handling missing data or performing calculations such as averages. Sometimes you’ll need other datasets—for instance, if your raw data uses a two-letter code for each country, you might need a table that lists both the code and the full name. If you do this, be sure to save all your work in a separate file; don’t write over the original table!

If the data is dynamic, meaning it’s updated on an ongoing basis, you’ll need to understand the frequency and nature of the updates. Then, you’ll want to think through how to display the data: for instance, will you show it for all time or a certain span, or will you make it interactive so that the user can choose?

You also might want to create preliminary charts to understand how the data in various columns look. It’s quick and easy to play around this way.

Technical skills to create a preliminary design

Some data viz pros like to first sketch their vision by hand or in a visual design app. This is an especially smart idea if you need other people’s input—you can save a lot of time by agreeing on how it should look before you build it.

Once you get to building the chart, that’s where your technical skills get put to the test. We’ll go over the major libraries and frameworks you might want to learn in the next section.

Visualization design skills for crafting the final product

Once you’ve built out your chart, it’s time to tweak it. Try out different color schemes, labeling approaches, and dynamic features such as what happens when you hover on or click a certain point. 

At this point, you’ve spent a long time with the data and chart. Someone with a fresh set of eyes can suggest improvements and catch errors that you wouldn’t have. If possible, find someone representative of the target audience.

Python tools and frameworks for data analysis and visualization

If you want to present yourself as serious about data viz, your best bet is to become proficient with some Python libraries. Of course, if you don’t know Python yet, you’ll need to get up to speed on that first, but don’t be daunted. More on that later.

A library is a collection of pre-written code, created and tested by others, that you can easily load and use. Most Python libraries, including the ones mentioned here, are open source and free.

There are several data visualization frameworks and libraries for plotting, a term borrowed from physical printing devices that draw lines directly on paper. Unless you have a specific reason to learn another library, we recommend starting with the most common: Matplotlib for static graphs and Bokeh or Plotly for dynamic ones.

Matplotlib, which claims to “make easy things easy and hard things possible,” has long been the standard for static charting with Python. It’s powerful and flexible, offering hundreds of types of charts with myriad ways to customize. It’s also the basis of many other data viz libraries.

Learning tip: Get hands-on practice with Matplotlib and Seaborn in the Intro to Data Visualization with Titanic section of the Journey into Data Science with Python learning path from CodeSignal Learn. 

For exploring datasets, you can use Seaborn, a library built on top of Matplotlib that “lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them.” It provides what it calls “opinionated defaults,” so you can quickly plot nice-looking charts without making design decisions, while allowing you plenty of customization. In fact, you can use Matplotlib parameters to edit a Seaborn chart. (Check out the lesson on Mastering Scatter Plots with Seaborn in Python from the Intro to Data Analysis path in CodeSignal Learn.)

If you want to create interactive graphs in Python, learn Bokeh or Plotly. They can both create stunning and complex graphs that load quickly. You’ll likely want to choose Plotly to make interactive dashboards, quick prototypes, or 3D plots. Bokeh shines for real-time data with live updates and allows for more customization and integration.

Not all tables will be ready for plotting, so you’ll need to know how to manipulate them. Going a step beyond Excel or Google Sheets, Pandas is the standard for cleaning, analyzing, and transforming data in Python. While plotting libraries can do some on-the-fly transformations like finding averages, most of the time, you’ll want to use Pandas to make data ready for graphing. (You guessed it, we’ve got a lesson for that too.)

Tableau and other software tools

In addition to those code-based libraries, there are several no-code tools in a category called business intelligence (BI). That term rightly hints that these were developed for the needs of the enterprise—tracking sales and other business-critical data—but they can be used for fields such as healthcare and public policy.

The leading BI platforms are Tableau, Power BI, and Looker. 

• Tableau is the most visually focused, with a drag-and-drop interface that allows a lot of customization.
• Power BI is part of the Microsoft universe, so it’s most often seen at companies that already use Office.
• Looker is the most analytically powerful, and least graphically-oriented, of the three.

Think twice before training on a particular platform before you need to. Typically, a company or organization that uses business intelligence software will commit to one platform, and they work quite differently so the skills aren’t fully transferable. Unlike Python libraries, they charge for access, so let an employer pay for the software (and the time you spend to learn it!).

That said, if you have your eye on a particular industry and you glean that one or another of these tools is dominant, it may be worth the effort to learn or even get certified. 

D3.js and tools for interactive visualization

JavaScript is a programming language mainly used to make web pages interactive. Just as there are Python data viz libraries, there’s one major one for JavaScript, known as D3.js.

D3.js is extremely powerful but can take time to learn, particularly if you aren’t already proficient in JavaScript. Fortunately, there are libraries built on top of D3.js, such as Plotly.js, that are easier to pick up. (Yep, the same Plotly you can use with Python.) For an example of a tool built on D3.js, check out SankeyMATIC—and play with making your own Sankey diagrams.

While D3.js is today’s standard, it’s not the only one. Chart.js, Vega, and Vega-Lite are alternatives that are worth considering if you’re a JavaScript person.

How can I practice my data visualization skills effectively?

Once you’ve become familiar with the tools, it’s time to put them to use.

Ideally, you can find a way to apply these skills in your job or schoolwork. Otherwise, come up with a project on a topic that interests you and see how you can put data to work.

Cost is no issue: the Python libraries you need are free, and there is an abundance of freely available data on just about every topic available online. You’ll also find active and friendly data viz communities around the internet with people who are happy to critique and provide guidance—just make sure to be respectful of people’s time by first searching for answers that may already be documented.

Where to learn data visualization online

The best way to learn data visualization is to get some guidance on the fundamentals and then learn through doing. All of the libraries we’ve discussed are thoroughly documented and discussed online.

Best online courses for learning the fundamentals of data visualization

Data science, including data viz, lends itself to self-paced online coursework. You can apply the skills you’ve just learned directly in the browser, and if you’re a tinkerer, you can experiment to your heart’s content.

Use CodeSignal Learn to acquire and refine essential Python and data visualization skills

While some courses, such as the ones offered by CodeSignal Learn, are well-templated, so you can get through them without knowing a lick of code, you’ll learn much more—and be better prepared for professional application—by learning the fundamentals of Python first. You don’t need to become an expert, but you should understand enough that you know how to look up what you don’t know.

As you look at online courses, inspect the credentials of the instructor or organization you’ll be learning from, especially if they’re charging a hefty fee. In fact, there is excellent instructional material available for free on just about any data viz topic.

Learning tip: CodeSignal Learn can teach you Python Programming for Beginners and Intro to Data Analysis with Python. It’s free to sign up, and you can get started anytime.

Practice with real-world data and build a portfolio website that showcases your skills

Once you’ve gotten a handle on the basics, the fastest and most effective way to bulk up your data viz skills is to practice.

The best way to stay focused and motivated is to create a portfolio. Start by thinking through what lies at the combination of what interests you and the visualizations you’d like to become good at. Find relevant data sources, and get plotting! Expect to run into challenges; by troubleshooting and seeking answers, you’ll learn through experience.

Other resources to enhance your skills and connections

In parallel to hands-on practice, broaden your perspective and sharpen your technique by studying what others have done. Consider a book by Edward Tufte, who is well known for having strong opinions and a perspective that spans centuries. Another popular data viz thinker, who brings a journalism perspective, is Alberto Cairo.

There are so many communities to join and thinkers to follow. Find inspiration in the r/dataisbeautiful subreddit, expert analysis of great charts from FlowingData, perhaps even join and attend events from the Data Visualization Society. When looking up local groups, broaden your search to include data science, as there is a large overlap of interest. 

Accelerate your career in data visualization with CodeSignal Learn

Data visualization is a worthwhile competence for data-minded visual storytellers (or story-minded data analysts!) to pursue. While there’s a bit of a learning curve, especially if you’re new to Python, the ability to create beautiful and useful data visualizations is always in demand.

Whether you have a clear vision of a long and prosperous career as a data visualization expert, or are wondering if it’s a skill worth learning, the first step is simple and free: take some CodeSignal Learn courses. You’ll be generating charts within minutes!

The post How to learn data visualization to accelerate your career appeared first on CodeSignal.

Our latest learner story is from Faith Yim, a recent college grad who switched to a career in software engineering after earning a degree in English. Faith enrolled in a coding bootcamp after graduating and now works as a backend Java developer focused on identity authentication. She uses CodeSignal Learn to deepen her understanding of Java and Java frameworks to advance her skills and her career.

What are your learning goals?

“I want to continue to deepen my understanding of backend development, particularly in Spring Boot and Java so that I can further align myself with my role as a software engineer. Beginning this career coming from a nontraditional technical background means that I have a lot to catch up on while staying updated with the best practices and technologies. I eventually want to bring myself to a point where I can make an even more impactful contribution to my team’s success.”

How do you like to learn? How do you make time for learning?

“I personally enjoy reading, watching videos, and note taking to learn, but since entering the tech field, I’ve had to get more comfortable with live practice as my main form of study. 

Leaving a little bit of time each day to learn is very important for me to continue bringing myself up to speed on concepts, so whether it is after dinner or on my commute, I make sure to get in however much time I can to study. Different types of learning can also be done at different times. Maybe I’ll be reading on a bus, listening to a podcast while walking, or live coding at home—everything counts.”

What are you learning right now on CodeSignal Learn?

“Right now, I am learning more about hashing, dictionaries, and sets in Java. I have wanted to dive deeper into integral topics like data structures since I was only able to briefly touch on these during my training. These topics have been hard for me to grasp, so it’s very helpful to have detailed explanations that actually make sense to me.”

What has it been like using Learn and interacting with Cosmo?

“Learn has helped me to get access to personalized training that I would not have been able to get without another person’s help. I don’t need to rely on a tutor or hope my Google searches come out successful, Cosmo knows exactly what I need for the lesson. The course moves along naturally and at a good pace for me—I don’t feel rushed or get lost on new concepts. I especially love the options you have as I am able to choose what angle I want to take for a topic to best understand it. Whether I want to read a portion or watch a video for it, my level of interactivity can vary depending on my choices. Cosmo’s adorable character is certainly a plus!”

What advice would you give to other learners?

“It doesn’t need to be a perfect moment to study—take every opportunity you can get. Whether it’s an hour or five minutes, every moment will add to your development. If you ever feel intimidated or insecure about your learning, know you are not alone! Just keep going.”

What’s next for you?

“I hope to sign on as a full-time employee once my contract ends so that I can prove that it’s never too late to learn something new. Backend development comes with tons of skills that I can always continue to learn about. This journey has been filled with both success and challenges, and I want to continue building my knowledge of technical concepts.”

Get started

Ready to learn in-demand skills like AI, data science, and Python programming? Sign up for CodeSignal Learn to get started for free.

Did CodeSignal Learn help you learn a new coding language, prep for an interview, or land a new role? We’d love to hear from you! Share your story with us here.

The post How a Java developer levels up her skills with CodeSignal Learn appeared first on CodeSignal.

To hone in on this feature of effective learning— 1:1 learning support—we looked to the research around how people learn and master skills. We wanted to understand: 

• How could we create a learning tool, CodeSignal Learn, that provides students effective 1:1 learning support at scale?
• How could we use large language models (LLMs) to facilitate this 1:1 support to promote mastery-based learning?

Creating effective 1:1 support

To start, we dug into the research on the best ways to implement 1:1 learning support and feedback. What we found was:

• Feedback matters for learning. In the early 2000s, when scholars were questioning the effectiveness of online learning methods compared to in-person instruction, feedback was found to make a large difference. Researchers found that, with feedback, web-based trainings were even more effective than in-person ones.
• Learners excel when they’re encouraged to ask questions, explore, seek feedback, and reflect on that feedback. And, encouraging people to seek feedback can help learners build expertise.
• In-the-moment feedback in error-focused trainings can help build learners’ self-efficacy and boost training effectiveness, particularly when it comes to solving new problems.

Across these studies, scholars agree that feedback—especially when learners seek it out themselves—makes for more effective learning.

Building an AI-powered tutor for technical skills

Given the research on how crucial 1:1 support and feedback are for effective learning, our next step was to figure out how to ensure our learning platform, CodeSignal Learn, could provide this support at scale to help people achieve mastery in a variety of technical skills. What we came up with was an AI-powered tutor that harnesses the power of multiple large language models (LLMs) to provide immediate, helpful, and context-aware feedback to learners: Cosmo.

Cosmo is an AI assistant built into the CodeSignal Learn platform that provides learners with personalized, 1:1 support. Learners can chat with Cosmo to clarify instructions for a practice exercise, look up documentation, debug code, and more. 

Compared to a human tutor, an AI-powered tutor offers a range of benefits:

• For employers, AI can allow organizations to scale professional development opportunities to more employees via autonomous learning. Traditional executive coaching, for instance, is highly effective at improving manager performance, but it is costly—and as a result, not accessible to most employees. AI makes this type of 1:1, personalized coaching cost-effective and scalable.
• For individual learners, an AI-powered tutor provides access to 1:1 learning support at any time of the day, and for a very low cost. AI makes tutoring accessible and affordable for learners who work odd hours or who can’t afford to hire a personal tutor.
• An AI–powered tutor also allows learners to ask questions freely, without the threat of being judged for asking “silly” questions in front of their peers. Asking questions allows learners to be more engaged in their learning and helps build their skills. This is particularly beneficial to women and other groups underrepresented in STEM, who are often afraid to make mistakes that could confirm stereotypes in highly competitive learning environments. 

Our next step was to pilot our AI tutor to see how learners responded to it and how it impacted their learning. 

Developers’ responses to an AI-powered tutor

As a part of our CodeSignal Learn Alpha program, we engaged 49 developers who expressed an interest in building skills in data structures and algorithms or data science and who had at least one year of coding experience.

Over the course of 4 months, Alpha participants took courses on CodeSignal Learn and participated in an early qualitative feedback survey, a post-course feedback survey, and in-depth, post-course 1:1 interviews. 

Here’s what we learned from our post-course surveys.

Responses to Cosmo, an AI-powered tutor

In the post-course survey, we included Likert scale items to measure user experiences. We found that the reactions to the AI tutor were overwhelmingly positive again: 80-90% of Alpha participants had favorable reactions for the AI tutor questions we asked. 

Some strong points were the tutor’s personality, application of context when responding, relevance of responses, and adaptation to the way the learners phrase their questions. These were all strong indicators for us that the AI tool was giving helpful and motivating feedback for learners.

Overall post-course reactions

At the end of the Alpha program:

• 100% of participants were more confident with their skills;
• 100% were satisfied with the overall experience;
• 90% would recommend CodeSignal Learn courses to others.

In both quantitative surveys and 1:1 interviews, 90% of learners said they would be glad if their employer offered CodeSignal Learn for their professional development. And, 80% of participants said they were motivated to continue learning in a format like this. 

Overall, this early research left us optimistic about the potential for AI-powered tutors to open doors for developers and other learners who may not get access to this kind of feedback and practice otherwise.

Conclusion

One-on-one tutoring, once considered an impractical and costly way to teach, is now scalable and accessible thanks to recent innovations in AI. Through our Alpha program piloting a new learning platform, CodeSignal Learn, we found that learners responded positively to engaging with an AI-powered tutor and felt confident in the skills they gained from the courses they took. This research suggests that AI holds immense potential to support learning in ways that were not possible even just a few years ago: making personalized, 1:1 feedback scalable and accessible to all. 

If you’d like to check out CodeSignal Learn for yourself, it is now generally available. Sign up to get started for free.  

About the authors

Adam Vassar is the Director of Talent Science at CodeSignal. He holds a Master’s in Industrial-Organizational Psychology and has worked in the talent management industry for 20+ years leveraging assessments and technology for candidate selection, leadership development, team collaboration, and employee engagement.

Seterra Riggs is a Talent Scientist at CodeSignal with a PhD in Industrial-Organizational Psychology. She provides research-backed insights that drive organizational strategies and product development, focusing on Diversity, Equity, and Inclusion (DEI), talent assessment, and employee learning & development and retention. With her expertise and commitment to DEI and career development for underrepresented groups in STEM, she helps shape industry best practices.

The post What we learned when we gave developers access to an AI-powered tutor appeared first on CodeSignal.

As someone reading this guide, you’re probably a tech enthusiast taking a smart step by exploring how to adapt to the new reality of technology but may not think they’re up for being a programmer. We can guide you through exactly what you ought to know. TL;DR: In addition to gaining an understanding of and practicing the art of prompt engineering, we recommend that you learn the basics of Python, but you certainly don’t need to become an AI engineering expert.

In this guide, we’ll get you up to speed on what prompt engineering is, the responsibilities of a prompt engineer, and the skills and knowledge you need to get the job.

Jump to a section:

• What is prompt engineering? Definition, roles, and job outlook
• What skills do you need to become a prompt engineer?
• 7 routes for becoming a prompt engineer in 2024 and beyond
• Frequently asked questions
• Accelerate your career in prompt engineering with CodeSignal Learn

What is prompt engineering? Definition, roles, and job outlook

Prompt engineering is the practice of figuring out what to say to AI models like ChatGPT so you can get the results you want. Seems simple, so how is that a whole job?

There are two levels to prompt engineering. One is simply being thoughtful about what you type as your AI prompt, whether you’re composing it yourself or copy-pasting a template. This is fast becoming a relevant skill in many roles—up there with, say, proficiency in MS Excel. The other, which is our main focus in this guide, is setting up systems that guide and enhance users’ input for optimal results. It’s like the difference between managing a budget and being an accountant.

Professional prompt engineering is a great fit for creative thinkers who are technology enthusiasts. Once you’ve learned enough Python, you’ll be able to apply your understanding of the world and way with words to get the most out of the AI models.

Prompt engineer role & responsibilities

Prompt engineering considers the entire context of a person’s interaction with AI.

• User interaction design is the first step. This could be as simple as thinking through how to suggest users write the prompts that you’ll manipulate, but it can also involve working with product designers and others to adjust the interface, flow, and other aspects of the user experience.
• Reformulation is the process of editing a user’s input so they get a better result from the AI model. You do this with scripts that restructure the user’s entry, add context, and give instructions on qualities like tone and length—in other words, engineering the prompt.
• Testing is the iterative process of evaluating results and adjusting inputs in an effort to improve them. Interestingly, this is also how AI models improve.
• Bias, fairness, and ethics is not prompt engineering’s responsibility alone, but the role serves as the front line of detecting and mitigating issues that may arise from large language models (LLMs) based on a very wide range of content and viewpoints.

Not all roles will include all four of these responsibilities, and there likely will be others you’ll have to do too. Given how new the title is, you can expect that what you do will change often.

What does a prompt engineer work on?

Generally, prompt engineers are hired to get the best possible outputs from AI for a particular use case.

These days, many new sites and apps, as well as functions within existing sites and apps, create rich output despite minimal user input. This input can be explicit, such as “10 slides of an itinerary for a trip to Croatia” in a slideshow app, or implicit, such as a writing assistant that automatically reviews your copy for tone and clarity.

If that one sentence prompt or any given paragraph of your writing went directly to the AI, it probably wouldn’t give a useful result. That’s where reformulating comes in, framing the input with plenty of context and instruction to give the model much more direction. In the slideshow app use case, it could end up as a multi-stage, behind-the-scenes iterative process, where the first prompt asks for an outline, then further ones guide the AI to flesh it out, while another process prompts an image-generation AI to create visuals.

What qualifications do you typically need to become a prompt engineer?

Prompt engineering is a new field with widely different responsibilities and requirements. But generally, in addition to becoming adept at the practice of prompt engineering, you’ll need to know some Python and demonstrate a fairly strong understanding of the fundamentals of how generative AI works.

Can you become a prompt engineer without a degree?

Some—but definitely not all—prompt engineering roles require or prefer a degree in computer science (CS) or a related topic. Even then, the employer might be flexible because CS majors are in high demand.

Whether you need a college degree of any sort depends on any given employer’s requirements. As with most knowledge-work roles, having a degree may be more important if you’re earlier in your career; with a demonstrated record of success, many will evaluate you on your performance rather than your formal education.

Can you become a prompt engineer without previous experience in AI?

The familiarity you gain with AI on the road to becoming qualified as a prompt engineer is a good starting point. You don’t necessarily need explicit AI-related job experience to be hired as a prompt engineer, but you should certainly be able to demonstrate both on your resume and in interviews that you have familiarity and competence with AI well beyond having used ChatGPT a few times.

What kind of companies employ prompt engineers?

Prompt engineering roles are popping up at organizations throughout the AI universe, from the very companies that develop the models to businesses ranging from healthcare to consumer finance.

Prompt engineering roles at high-flying tech companies, especially the ones developing AI models such as OpenAI and Anthropic, are more likely to require deep programming skills. If you’re just getting started with Python, you may want to look at places applying AI more narrowly, such as for a specific app or a business goal. Many agencies and consulting firms are hiring prompt engineers to develop workflows for individual clients that are looking to capitalize on the recent explosion in AI.

As with most knowledge work, there’s nothing inherent to prompt engineering that requires you to work in an office, and accordingly some of these roles are remote. Others are hybrid or fully on site, but that’s a matter of company culture and policy rather than the nature of the role.

What skills do you need to become a prompt engineer?

In brief: Get up to speed on how LLMs work, learn some Python, and understand and practice various forms of prompt engineering.

Understanding AI, ML, NLP and LLM

First, let’s define these terms you’ll see all over the place:

• Artificial intelligence (AI) is a broad term for technology that simulates how the human brain learns and processes information. Any one instance of AI is a model. AI is also used as a term for any given model, such as, “The AI will respond to your prompt.”
• Machine learning (ML) is, well, the learning part of AI. Almost all ML takes place through an iterative learning process, using a ton of math to get incrementally better at generating the best output for a given input. Most ML leverages deep learning, where neural networks with multiple interconnected layers work together to help computers learn complex patterns from data.
• Natural language processing (NLP) is how computers understand and generate human communication. It’s a core component of translation, speech recognition, and any other software that reads, listens, or talks.
• Large language models (LLMs) are the new hotness, forming the basis of generative AI (gen AI). ChatGPT, Gemini, Claude, Llama, and all these other super-powerful AI models have been trained on absolutely enormous amounts of text (like, pretty much the entire internet), and through a ton of processing, have created an exquisitely complex understanding of just about everything humans talk about—and how they talk.

At their core, LLMs are very sophisticated prediction machines. Given any input, they work by guessing what the next word ought to be—and the next, and the next, and so on, until their best guess is that they should stop talking. They don’t actually “know” anything in the ways humans do, but rather they do a (usually) pretty good job of guessing what to say and how to say it.

Two implications of how LLMs work are very important for prompt engineers to understand:

• LLMs are not deterministic. That means, given the exact same input, you’ll probably get a different output every time. This is how LLMs can create output that sounds more human than anything else computers have previously generated, but also makes it really hard to get consistent outputs.
• The models can make stuff up. This is known as hallucination. They don’t automatically fact-check themselves, so there’s no guarantee that their predictions lead to accurate results.

Communication skills

Compared to most software engineering roles, a prompt engineer needs strong communication skills for two audiences: the AI and your colleagues.

You’ll have to have a strong command of written English and be able to work with a variety of speaking styles and rhetorical structures. The phrasing and approach you use to deliver prompts to the AI will influence the output, so you ought to be able to try different ones.

Similarly, because you will be working with a range of technical and non-technical colleagues, you’ll need to clearly articulate your perspective to help them understand both the value of your role as well as your particular concerns and needs.

Scripting and programming skills

While not all prompt engineering roles list knowing how to code as a requirement, many do.

What programming language should I learn?

If you don’t already know how to code, Python is your best bet for getting started. It’s the most commonly used language for working with AI, and it’s relatively easy to learn. Plus, LLMs are very good at generating and critiquing code, so you can improve your skills with the help of the same AI that you’ll be engineering prompts for.

Other languages are valuable too, depending on the context. JavaScript is the backbone of modern web development, so it’s likely to come up when interfacing with the front end. Others like SQL and R are great for databases and data analysis. If you already know one or more other languages besides Python, that’s fantastic and worth highlighting on your resume.

Learning tip: Want to learn the basics of Python? The Introduction to Programming with Python in CodeSignal Learn takes from writing your first program in Python to mastering functions and more. 

Understanding how to avoid biases in AI responses

AI can make offensive or biased statements. They’ve learned from hundreds of years of literature, plus the entire Internet, and people have said and implied all sorts of racist, sexist, and otherwise biased things. While AI platforms themselves try to filter out unsavory responses, it’s important to stay attuned for subtler issues or even, sometimes, big ones that slip by the controls.

The best way to avoid bias is to remove biased material from the corpus that AI is learning from, but that’s a herculean task at scale and one you probably won’t have influence over anyway. Instead, you’ll have to test and iterate to make sure that the prompts you’ve engineered can render acceptable outputs based on a wide range of user inputs. You might also be able to guide the AI in critiquing itself, though there is no substitute for human judgment in these matters.

Understanding the different prompting techniques for guiding language models

Prompt engineering can achieve much better outputs by guiding how AIs think. Sometimes this can be done in a single prompt, other times it’s a series of prompts. Here’s a quick rundown of some of the top techniques:

• Chain-of-thought prompting: Encourage the AI to think step by step, leading to more logical and detailed responses. By guiding the AI through a sequence of thoughts, you can achieve more accurate and comprehensive answers.
• Context setting: Provide the AI with the right amount of context to understand your queries better. These instructional prompts ensure that the AI has all the relevant information it needs to generate appropriate and insightful responses.
• Few-shot learning: Use examples within your prompts. By including a few demonstrating the type of response you’re looking for, you can significantly improve the quality of the AI’s responses, even with minimal initial data.
• Creative prompting: From generating novel ideas to crafting engaging stories, this technique helps you harness AI’s creativity by framing prompts in imaginative and open-ended ways.

7 routes for becoming a prompt engineer in 2024 and beyond

Set yourself up for success as a prompt engineer by not only learning the fundamentals of that skill, but also developing a solid understanding of how today’s AI works.

Participating in online courses and bootcamps

Learning prompt engineering and coding through online platforms is the sensible, affordable way to go. You can start anytime, work at your own pace, and take advantage of online exercises that make it as easy as possible to practice your new skills.

Prepare for a career in prompt engineering with CodeSignal Learn

The CodeSignal Learn platform is built for upskilling, which means it’s great for preparing for an AI career by learning prompt engineering. What’s more, you can use it for free.

Learning tip: Get started with Prompt Engineering for Everyone in CodeSignal, and if you like what you see, continue to learn the basics of Python.

Learning the fundamentals of NLP, including Python NLP libraries and frameworks

Understanding the core notions of natural language processing (NLP) will help you become a more successful prompt engineer. Although the inputs and outputs of an LLM are words and sentences that make sense to humans, what goes on behind the scenes is a whole bunch of math.

Fortunately, you don’t need to know linear algebra; there are many NLP libraries and frameworks that give you a whole lot of power with a few lines of code. See the Text Classification with Natural Language Processing learning path for an example of what you could soon know how to do.

Understanding the fundamentals of ChatGPT and transformer models

If terms like gen AI, transformer models, and backpropagation through time are foreign to you, then you ought to spend some time studying the fundamentals of modern AI.

We’ll give you a head start: transformers are a type of neural network that use a technique called attention to consider the entire context of a given input while generating an output. Transformers are the basis of gen AI models such as ChatGPT, and they use a technique called backpropagation through time to improve, by reflecting on how they came to a certain output and making adjustments to do better next time.

Many AI applications these days take a pre-made transformer—GPT stands for generative pre-trained transformer—and use a technique called fine-tuning to focus and improve its performance on the basis of additional data.

Learning tip: Have some coding experience and want to dig deeper into how AI works? Learn to build machine learning (ML) algorithms from scratch with the AI Theory and Coding learning path in CodeSignal Learn. 

Experimenting with a variety of pre-trained models for text, image, audio, and video generation

Between the time this article was written and when you’re reading it, there will surely be new models and innovations. The important thing is that you practice your prompt engineering skills on the latest models, because that’s most likely what you’ll end up working on. Better yet, try different ones, such as OpenAI’s GPT-4o, Google’s Gemini, or Anthropic’s Claude.

One trend we are seeing that will surely continue is a shift from single-mode AIs (taking in text or image only) to multimodal ones (processing text, image, audio, and video simultaneously). Accordingly, prompt engineering doesn’t just have to work with text output: you may find that you enjoy and are good at prompting for other formats.

Pursuing a formal education in AI

Not surprisingly, AI is booming in popularity as a subject at colleges and universities. Only a few offer AI-specific degrees, but we can expect that to change.

The classes that teach you to learn how to create and work with AI models will typically be found within the computer science department. Other parts of academia, from art to medicine, are increasingly engaging with and teaching about AI from an applied perspective: how to use a model someone else has built. Still others, such as philosophy and law, consider the societal and even moral implications of AI.

What to study for a career in prompt engineering?

Computer science is the most directly relevant degree for anyone interested in building and guiding AI systems. That said, prompt engineering also requires a humanistic side. Courses in writing, languages, and linguistics will help you learn the nuances of natural language. Studying philosophy, law, psychology, and sociology will help attune your senses to bias and other unwanted output that requires a human eye to discern.

Earning prompt engineering credentials

As of the writing of this article, there appear to be no prompt engineering higher education degrees, though there are some courses that offer certificates. Generally, prompt engineering is covered as a part of a broader certification or degree in AI.

Building a strong project portfolio that showcases your skills

There are three directions you could go:

• If you can demonstrate your prompt engineering skills through your current job, that’s a great way to be useful to your employer as well as showing your capabilities to future ones. Just make sure that whatever you make public doesn’t use or expose proprietary or confidential information.
• You could also base projects around something that deeply interests you. This holds two advantages: you’ll probably do a really good job because of your topical expertise, and you’re more likely to see the project through to launch if the topic motivates you.
• One more direction is something closer to the applications that might be hiring. After all, a well-prompted bot that helps people sort out insurance questions may come across as more professional than one that holds deep conversations about, say, organic gardening. Just make sure to have someone who’s actually well-informed about the topic review your output for accuracy and usefulness.

After completing the project itself, be sure to include a narrative that explains how you crafted the prompts.

Attending industry events and networking with professional prompt engineers

As prompt engineering is a nascent field, the surrounding community is also very young. The Prompt Engineering conference in October 2024 claims to be the world’s first such conference. There are also several global and locally-based interest groups and a subreddit.

You’ll find much more opportunity with interest groups and events with a broader focus, such as gen AI or NLP. There are AI conferences for a wide variety of fields, geographies, and professions, and meetups in cities big and even not-so-big. Plus, given the huge interest in AI these days, most general tech or computing meetups will inevitably end up covering the topic.

As you go about building a network, avoid limiting yourself to people with a prompt engineer title, because, frankly, not many people have it yet. Instead, look for the types of people you’d be likely to work with in the role: AI engineers, product designers, and even policy and legal folks in the space.

Frequently asked questions

How can I start a career in prompt engineering?

Begin by filling in knowledge gaps. Start by learning and practicing the fundamentals of prompt engineering, which you can do with CodeSignal’s learning path. As you hone your prompting skills in your current job or as a side hobby, learn some Python as it’s a very common language for manipulating AI, and it’s required on many job listings. You don’t need to be a Python pro to start applying, but it will help your chances to learn enough to be able to write basic programs and make sense and apply code others (or an AI!) have written.

How long does it typically take to become a prompt engineer?

It depends on the skills you already have. If you already know how to code, you can learn the fundamentals of prompt engineering within hours and deepen your expertise in days. If you don’t already know Python or any other programming language, you can expect it to take about two months to achieve basic proficiency.

Can you pursue a career in prompt engineering through online courses?

Absolutely. The basics of prompt engineering don’t take too long to learn, and you can start an online course such as the one offered by CodeSignal right away. Build a foundation in Python with online courses, too. Once you’ve gotten the education, the next step is practice, and you can do that on your own—or, you can even ask an AI like OpenAI to critique your work.

What is the typical salary range for a prompt engineer?

A May 2024 search for US-based jobs revealed a range from $40/hour to well over $200,000 annually. The best-paying ones are at well-funded companies and require significant demonstrated coding skill and experience with AI. The lower-paying ones often have lower technical requirements..

Accelerate your career in prompt engineering with CodeSignal Learn

Prompt engineering is a brand new field that is co-evolving with generative AI. It’s a promising entry point for creatively-minded people to leverage their skills and interests to the fast-growing field of AI engineering.

You can learn the basics of prompt engineering through CodeSignal Learn without knowing a lick of code, but if you decide to continue past this initial course we strongly recommend learning the fundamentals of Python. Our courses are available for free—including limited access to our own AI-powered tutor—and you can start right away. So why not start now? We’ll see you there.

The post How to become a prompt engineer: Skills & career path guide appeared first on CodeSignal.

Today’s story comes from Jack Tulloch, a sound engineer who works at a post-production film studio in Los Angeles. He is taking a beginner Python course with CodeSignal Learn to learn programming fundamentals. His goal is to use these skills to build a new audio plugin tool. 

What skills did you want to learn?

“In my day job, I create sound effects for short films. I realized there was a gap in the marketplace for audio plugins—software tools that manipulate audio files in desired ways. So I wanted to learn the skills to build a plugin that I could sell as a side project. While I was a bit familiar with code, I wasn’t skilled enough to build my ideas out. 

My goal is to learn enough programming skills to develop the initial version of my plugin, or at least build half of it and then hire someone else to finish it—and I want to understand the technical details before handing off the project to someone else.

I’m starting with Introduction to Programming with Python. I chose Python because it’s more human-readable than other programming languages, and then I plan to learn C++ after that.” 

How do you like to learn?

“I’m a big believer in doing the thing and figuring stuff out along the way. With everything I’ve learned, I’ve found it most effective to dive into the thing itself and watch courses and videos alongside that.”

What is learning with CodeSignal like?

“Like I said, I had a little experience with programming—I had used other learning platforms, but I got stuck with those tools. With some of them, you need to submit your answers to a forum and wait for a human to reply. This wait time was discouraging and made learning a lot harder to stick with. 

With CodeSignal, I have a resource, Cosmo*, who I can ask questions to directly instead of using forums or googling. Being able to get simple explanations quickly has made learning much more enjoyable. Cosmo knows what I just learned and where I’ve been, so it’s very helpful for me.” 

*Cosmo is a friendly AI-powered tutor and guide built into CodeSignal Learn.

What advice would you give to other learners?

“I’m not quite far enough to give advice, but I would say they should take their time and try to stay consistent. Use your resources too! I was surprised to learn that even people who have been programming for a long time search Google all the time and use AI tools to help them figure out simple things.”

How do you make time for learning?

“I try to carve out time every night after work. I commit to at least 20 minutes. I know how long it takes to build certain skills, and I expect learning programming will take a long time. I’m not in a rush. I like to learn a little bit every day.”

What’s next for you?

“I want to stay consistent with my learning, take as many courses as I can, and see how far I can go. Specifically after this Python lesson, I’d like to learn machine learning and get a grasp of how that works. Then, I’ll learn C++ after that. But if there’s something easier in between those two, I’ll do that first. The main thing is to stay consistent and then I’ll re-evaluate where I am.”

Get started

Ready to learn in-demand skills like AI, data science, and Python programming? Sign up for CodeSignal Learn to get started for free.

Did CodeSignal Learn help you learn a new coding language, prep for an interview, or land a new role? We’d love to hear from you! Share your story with us here.

The post How a sound engineer builds coding skills on CodeSignal Learn appeared first on CodeSignal.

“Human beings have existed for over 100,000 years. For most of that time, you weren’t so different from apes. It took you 90,000 years to get from hunter-gatherer to farmer. After that, how long did it take you to become industrialists? About 10,000 years. Atomic power? Two hundred years. Computers? The information age? Fifty years.”

The show may be science fiction, but the acceleration of technological innovations that it references is absolutely real. Technology is advancing faster than ever today—and so are the skills that employees need to keep up with these innovations. From cloud computing, to mobile development, to AI, companies today need technical talent with skill sets that didn’t exist even a decade ago.  

Take AI engineers, for example. Companies have significant AI skills gaps on their teams, but they can’t source enough new talent to fill these roles. To address this, a recent McKinsey report finds, companies are upskilling their existing engineers. An author of the report explains, “When it comes to sourcing AI talent, the most popular strategy among all respondents is reskilling existing employees. Nearly half of the companies we surveyed are doing so.” 

Upskilling and reskilling are key strategies companies can use to fill technical skills gaps on their teams, and they’re more important today than ever. This article will dive into what technical upskilling is, when to use it, best practices for upskilling, and how to measure the success of your program. 

Jump to a section:

• What is tech upskilling?
• Upskilling vs. reskilling: What is the best option?
• How to choose between upskilling employees vs. hiring externally
• How to help your employees develop the tech skills of the future
• How organizations can ensure the long-term effectiveness of their upskilling initiatives
• What metrics to track to measure the ROI of your upskilling program
• Accelerate your employee upskilling with CodeSignal Develop

What is tech upskilling?

Tech upskilling is the process of continuously building employees’ technical skills to address current and future skill gaps and business needs. In an era where technological advancements are rapidly transforming industries, companies must invest in upskilling to keep employees’ knowledge and skills up to date with the latest innovations. Tech upskilling both boosts an organization’s competitive edge and fosters a culture of learning and innovation. 

Why is there a need for upskilling your technical talent today?

The need to upskill your technical talent today is driven by rapid technological advancements that are reshaping industries through digital transformation. As technologies such as AI and automation become integral to business operations, companies must adapt quickly to harness their full potential and maintain a competitive advantage. 

Recent statistics underscore this urgency: 42% of companies increased their upskilling efforts after the onset of the COVID-19 pandemic, where in-person workplaces became remote-only overnight and business needs changed suddenly. Upskilling equips employees with the tools and skills they need to best contribute to their teams; it also serves as an effective employee retention strategy. By investing in their professional growth, organizations boost engagement and motivation among their employees, reducing turnover and building a more skilled and satisfied workforce.

Should every company have a tech upskilling plan?

Companies of many sizes and industries who employ technical talent (software developers, engineers, data scientists, etc.) should consider implementing a tech upskilling plan as part of their strategic workforce planning. This commitment to enhancing skills in industry-specific technologies both future-proofs businesses against rapid changes in the market and increases workforce agility. A well-crafted upskilling initiative fosters innovation and growth by preparing employees to tackle existing and emerging challenges. 

Upskilling vs. reskilling: What is the best option?

Upskilling and reskilling are two strategies for workforce development, but they serve slightly different purposes. Upskilling refers to the process of improving the existing skills of employees to enhance their performance and enable them to handle more complex tasks within their current roles. On the other hand, reskilling involves training employees to acquire entirely new skill sets, preparing them for different roles within the company as their current positions evolve or become obsolete. The key difference lies in the focus: upskilling is about enhancement and addition, while reskilling is about redirection and role changes.

When deciding whether to invest in upskilling, reskilling, or both, companies should conduct an organizational needs assessment to understand the specific demands of their industry and the future trajectory of their business. This assessment should consider the career development paths available within the organization and how employee role evolution might impact these paths. A cost-benefit analysis can help determine the financial viability and potential return on investment for each training option.

How to choose between upskilling employees vs. hiring externally

When you’re choosing between upskilling employees and hiring externally, you should weigh several factors, including recruitment costs, time-to-productivity, and the fit with organizational culture. Upskilling your internal talent pool is often beneficial as it promotes knowledge retention and leverages the existing understanding employees have of the company’s processes and values. This approach can be more cost-effective than external hiring, which involves higher recruitment expenses and typically a longer ramp-up time to full productivity. However, there are situations where external hiring is necessary, especially when the required expertise is not available within the current workforce or when fresh perspectives are needed to innovate and drive growth. 

Benefits of upskilling employees

If you decide to invest in upskilling employees, instead of or in addition to hiring externally, upskilling offers numerous benefits to your teams and your organization. Here are a few of them:

• Boost job satisfaction: Upskilling initiatives often lead to increased employee engagement, as individuals feel valued and see opportunities for personal growth within the company. One recent study found that 68% of employees consider training opportunities to be essential to job satisfaction; another found that employees ranked “opportunities to learn and grow” as the #1 driver of a great work culture.
• Support internal mobility: By providing training and development, organizations enable employees to advance into new roles, which helps meet evolving business needs without the added expense of external hiring. A recent study found that internal mobility boosts retention, as well.

“Companies that excel at internal mobility retain employees for an average of 5.4 years. That’s nearly twice as long as companies that struggle with it, where the average retention span is just 2.9 years.”

• Increase productivity: A recent McKinsey study found that 91% of companies and 81% of employees said that upskilling and reskilling training boosted productivity at work.
• Ensure alignment with industry standards: Continuous upskilling ensures that employees’ skills stay relevant and compliant with evolving industry benchmarks, keeping the company at the forefront of quality and regulatory standards.
• Attract new talent: A commitment to employee development is a significant draw for prospective hires who value professional growth and learning opportunities, making the company more attractive to top talent in the field.
• Reduce turnover rates: When companies invest in their employees, it fosters loyalty and job satisfaction, leading to increased employee retention and lower recruitment costs over time. One study found that 45% of employees said they are more likely to stay in their role if they receive more training.
• Enhance innovation capacity: Upskilling helps employees adapt to new technologies and methodologies, equipping them to drive innovation and maintain the company’s competitive edge in the marketplace.

Challenges with upskilling

While upskilling offers many benefits, it can also present challenges that organizations need to address to maximize the impact of their training programs. Challenges may include:

• Resource allocation: Upskilling requires significant investment in terms of time and money. Organizations must strategically allocate these resources to avoid straining their budgets and overburdening employees with work and training commitments.
• Training effectiveness: Not all training programs achieve their intended outcomes. Companies face the challenge of designing and implementing effective training that genuinely enhances skills and contributes to business goals (how to do this will be addressed in the sections below).
• Employee resistance: Some employees may be resistant to change, especially if they feel comfortable in their current roles or uncertain about new technologies. Overcoming this resistance involves clear communication, demonstrating the benefits, and possibly incentivizing participation.
• Scaling training programs: As a company grows, scaling up training programs to accommodate more employees or new roles can be challenging. This requires adaptable training models and possibly leveraging technology like online learning platforms to ensure consistent and efficient training delivery.

When should you consider hiring new staff?

When considering whether to hire new staff, you should weigh several factors. A skills shortage within your existing workforce that cannot be quickly remedied through upskilling is a primary reason to look externally. Business expansion needs may also compel your organization to bring on new talent with specific skills not present in the current team. You should also compare the cost of training existing employees versus hiring skilled new personnel, especially when time constraints demand immediate expertise that training cannot provide quickly enough. In these cases, hiring new staff can be the most efficient and effective strategy to meet immediate business needs.

How to help your employees develop the tech skills of the future

To help employees develop the tech skills of the future, it’s crucial to first identify skills gaps within your team. This allows for the creation of personalized learning plans that are tailored to meet the needs of individual employees and the organization as a whole. Companies can also foster a proactive learning culture by integrating mentorship programs and collaborative learning environments into their upskilling program. These strategies enhance skills development while also encouraging teamwork and knowledge sharing. 

Additionally, offering support for obtaining certification and credentials can motivate employees to commit to their development paths. And finally, leveraging third-party learning content and services can provide employees access to specialized knowledge and training resources that ensure they have the tools they need to succeed today and in the future.

Top employee upskilling strategies

The best employee upskilling strategies include a range of approaches designed to enhance the capabilities and productivity of your workforce. Implementing continuous feedback mechanisms, for instance, helps employees identify areas for improvement and tracks their progress over time. Job rotation is another effective strategy that allows employees to develop a broader range of skills by experiencing different roles within the organization. Other strategies are peer mentoring and peer coaching, which can foster a supportive environment where employees learn from each other’s experiences and insights.

One of the most impactful ways to fill skills gaps on your team is by offering upskilling courses that provide structured learning opportunities to help employees stay current with industry trends and technologies.

Features of an effective upskilling platform

The best tech upskilling platforms offer relevant content that is engaging, personalized, and results-oriented.

Feature 1: Personalization

Top upskilling platforms excel in offering personalized learning experiences. In fact, 78% of learners say they need course recommendations based on their individual career goals, according to a recent report. By assessing the skills and goals of each learner, upskilling platforms can tailor content and pathways that meet individual needs effectively. This personalization helps learners engage more deeply with the material, progressing at their own pace while ensuring that they are developing the skills that are most relevant to their roles.

Feature 2: One-on-one learning support

Effective learning often requires direct support, which is why leading upskilling platforms provide one-on-one learning assistance. AI-powered learning assistants ensure learners receive the help they need to overcome challenges and comprehend complex topics—and they can do so at scale. This personalized attention boosts confidence and facilitates a deeper understanding of the material.

Feature 3: Gamification and microlearning

Incorporating gamification and microlearning techniques is a hallmark of advanced upskilling platforms. These features break down learning into bite-sized tasks and use elements of play to increase engagement, motivation, and retention. Gamified challenges and short learning modules make the upskilling process more interactive and enjoyable, which helps ensure your employees follow through on their learning goals.

Feature 4: Outcome-driven

Lastly, the best upskilling platforms are outcome-driven and focused on skill mastery. These platforms ensure that a learner has mastered a skill before moving on to a new topic. Platforms should also provide team leaders visibility into their employees’ skills with analytics dashboards to track learner progress, skill proficiency, and time spent learning. Integrations with existing learning management systems (LMS) help align learning platform insights with organizational goals.

How do you upskill your team or employees more efficiently?

To upskill your team or employees more efficiently, leveraging technology platforms is key. Online learning platforms offer a suite of tools that facilitate data-driven skill development and allow you to tailor learning paths that precisely match the needs of your organization. By utilizing analytics dashboards, you can monitor progress in real time, identify learners’ current proficiency in specific skills, and adjust learning plans to better suit their needs. 

How organizations can ensure the long-term effectiveness of their upskilling initiatives

For organizations to ensure the long-term effectiveness of their upskilling initiatives, several key elements must be in place. First is leadership commitment, as leaders set the tone and standard for what priority continuous improvement and learning has within their company. Establishing a sustainable learning culture that encourages continuous development and curiosity is also important. This environment should be supported by establishing success metrics, which allows the organization to track the impact of training programs and adjust strategies as necessary. Regular program updates may be needed to keep content relevant and aligned with the latest industry trends and technological advancements. Lastly, integrating upskilling initiatives with broader business goals ensures that they contribute directly to organizational success.

What metrics to track to measure the ROI of your upskilling program

To accurately measure the return on investment (ROI) of your upskilling program, tracking specific metrics can provide valuable insights into its effectiveness and impact. Here are a few metrics to consider tracking:

• Employee performance improvements: Monitor pre- and post-training performance metrics to see how employee productivity has increased as a result of upskilling efforts.
• Skill attainment: Tracking employees’ learning completion rate may give some indication of learner engagement, but it doesn’t show how effective the training was. A more useful metric to track is skill attainment: to what degree employees have mastered the skills they’re working toward.
• Cost savings versus traditional hiring: Compare the costs associated with upskilling existing employees against the costs of hiring new staff—including recruitment, onboarding, and ramp-up times—to assess financial efficiency.
• Employee engagement and satisfaction: Measure changes in employee satisfaction and engagement through surveys and feedback mechanisms before and after training, as these are indicators of the program’s resonance with the workforce.
• Impact on business outcomes: Link improvements in business performance metrics such as sales, customer service quality, and operational efficiency directly to upskilling efforts to understand their true impact on organizational success.

Accelerate your employee upskilling with CodeSignal Develop

This article has explored what tech upskilling is, why companies should invest in upskilling, and how to ensure upskilling programs are successful. As technology changes and advances at an ever-growing rate, upskilling becomes an increasingly important way for companies to keep pace with innovation and the changing skill sets needed to take advantage of tech advancements. Using online learning platforms is one of the most effective and cost-efficient ways for companies to upskill their current technical talent with the skills their organization needs today and in the future.

Ready to explore upskilling or reskilling your technical teams? CodeSignal Develop is a revolutionary learning platform designed to achieve the key business outcomes of reskilling workforces and closing skills gaps, all supported by advanced tracking and reporting capabilities.

Want to learn more about how companies are using CodeSignal Develop to close skill gaps on their teams? Check out a recording of our Innovation Spotlight webinar, which showcases how Google and others are using Develop to level up their teams’ skills.

The post How to upskill employees with tech skills of the future appeared first on CodeSignal.

Responsible for developing, programming, and training the complex networks of algorithms that comprise AI, AI engineers are in high demand—and highly paid. According to Glassdoor, AI engineers in the US earn an average salary of $152,433. At some companies, AI engineers earn much more; at Google, for instance, AI engineers earn $241,801 per year, on average.

With surging employer demand and competitive salaries, AI engineering makes for an attractive career path for individuals interested in AI. This guide walks you through what it takes to break into a career in this field, including basic AI skills needed, different routes toward becoming an AI engineer, and answering frequently asked questions from aspiring AI engineers. Ready to get started?

Jump to a section:

• What is AI engineering? Definition, roles, and job outlook
• Skills required to become an AI engineer
• 6 routes for becoming an AI engineer in 2024 and beyond
• Frequently asked questions
• Accelerate your AI engineering career with CodeSignal Learn

What is AI engineering? Definition, roles, and job outlook

AI engineering is the cutting-edge discipline that lies at the intersection of computer science, mathematics, and sometimes even cognitive psychology. It centers on creating systems that can learn from data, make decisions, and improve over time. AI engineering involves the design, development, testing, and refinement of intelligent algorithms and models that enable machines to perform tasks that typically require human intelligence. By harnessing the power of machine learning, deep learning, and neural networks, AI engineers develop solutions that can process and analyze vast amounts of data, recognize patterns, and make informed decisions.

The demand for AI engineers has seen a surge in the past few years, reflecting the rapidly growing integration of AI technologies across industries. The U.S. Bureau of Labor Statistics projects a 23 percent increase in jobs for all computer and information research scientists, including AI professionals, over the next decade—much faster than the average for all occupations. 

A more recent analysis found that job postings that call for skills in generative AI increased by an incredible 1,848 percent between 2022 and 2023. The proliferation of AI applications in everyday life and the rapid advancement of AI technologies suggest that the demand for skilled AI engineers will only continue to grow.

What does an AI engineer do? A real-world example

AI engineers are at the forefront of developing AI solutions that drive innovation and efficiency at their organizations. Their work often involves creating machine learning models that can analyze large datasets to predict outcomes or behaviors and, in turn, inform business decisions. AI engineers ensure that the underlying logic and processes of AI applications are robust and effective. Their work also extends into data science: AI engineers utilize statistical analysis and computational theories to extract actionable insights from complex data structures. This forms the backbone of predictive analytics, where AI engineers forecast future trends and patterns, which allows organizations to strategize proactively.

A day in the life of an AI engineer involves both theoretical problem-solving and practical application of skills. A typical AI engineer’s work day might start with reviewing the latest research on neural networks or machine learning techniques relevant to their area of specialization. They’ll likely also have meetings with cross-functional teams, where AI solutions are discussed in the context of current projects and business goals. The bulk of their day may be dedicated to hands-on tasks such as coding new algorithms, refining existing machine learning models, or analyzing datasets for hidden patterns. 

AI engineer responsibilities

An AI engineer’s responsibilities include a wide array of tasks critical to the development and deployment of AI systems, starting at its core with data preprocessing. Data preprocessing involves cleaning, structuring, and enriching raw data to ensure its suitability for model training. Following this, model training and evaluation are the next core tasks; this is where AI engineers apply various algorithms to the processed data and iteratively refine the models to enhance their accuracy and reliability. 

Once a model has been trained and evaluated, the next step is AI deployment, where the model is integrated into existing systems and applications—this makes AI functionalities accessible to end-users. The role of an AI engineer does not end with deployment, though. They must engage in continuous learning and model improvement, as AI systems evolve in response to new data and changing environments to remain effective. To produce effective models, AI engineers work closely with other teams—including data scientists, developers, and business analysts—to ensure that AI solutions align with broader organizational goals and user needs. 

What qualifications do you typically need to become an AI engineer?

Many entering the field of AI engineering hold a Bachelor’s degree, or higher, in Computer Science or in a related field like mathematics, statistics, or engineering. Given the rapid evolution (and relatively recent emergence) of AI as a discipline, formal education specifically in AI is less common. Many professionals in this field have pivoted from related areas, leveraging self-teaching resources, online courses, and bootcamps to gain the specialized knowledge required for AI work. Certifications in AI and machine learning from reputable platforms can also help aspiring AI engineers build competency in this area. 

In addition to education, those seeking a career in AI engineering should gain hands-on experience with AI projects. Employers often look for practical evidence of an individual’s ability to apply theoretical knowledge to real-world problems. This experience can come from personal projects, internships, or professional roles that involve tasks like data preprocessing, algorithm development, and model deployment. Aspiring AI engineers should also be knowledgeable about software development practices in general, as AI engineering involves both building models and integrating them into larger systems. Familiarity with cloud computing services is also important, as these platforms often host AI applications and offer scalable resources for training and deploying models.

Understanding the different types of AI engineer roles

The term “AI engineer” encompasses several different, related roles. Here are a few of the most common AI engineering titles—and how they differ:

What is the difference between an AI engineer and an ML engineer?

The difference between an AI and an ML engineer is primarily in the scope and focus of their work. An AI engineer deals with a broader range of artificial intelligence technologies, encompassing not only machine learning but also aspects like robotics, natural language processing, and cognitive computing. Their goal is to create systems that can mimic human intelligence in various ways. On the other hand, a machine learning engineer specializes more narrowly in algorithms that learn from and make predictions on data. They delve deeply into data models, focusing on developing, training, and fine-tuning algorithms; this allows machines to improve their performance over time without being explicitly programmed for each task.

Skills required to become an AI engineer

AI engineers need strong skills in math, programming (including specific languages, frameworks, and tools), and communication and collaboration. Here are more details about those core skills areas: 

Mathematical foundation

Strong skills in math are crucial to building a career as an AI engineer. Here are a few key areas of mathematics that aspiring AI engineers should master:

• Statistical analysis is at the heart of understanding data trends and making data-driven decisions and is essential for optimizing the performance of AI models. 
• Probability theory plays a key role in modeling the uncertainty of various real-world scenarios, which AI systems must navigate and interpret. 
• Linear algebra is fundamental for dealing with the large matrices and high-dimensional data structures common in machine learning and deep learning tasks. 

What if I’m bad at math?

If you feel you’re not strong in math, don’t let that deter you from pursuing a career in AI. Many resources are available that can help you strengthen your mathematical skills, including online courses, tutorials, and workshops specifically designed for learners at various levels. Starting with foundational topics in statistics can build your confidence and understanding gradually.

Also, keep in mind that much of AI development also relies on software tools and libraries that abstract complex mathematical operations, which allows you to focus initially on learning AI concepts and applications while you improve your math skills at the same time. 

Programming

Programming skills are pivotal for any AI engineer, and Python stands out as the quintessential language for AI due to its extensive libraries and frameworks that simplify the implementation of machine learning algorithms. Familiarity with frameworks like TensorFlow and PyTorch is essential, as these tools provide robust environments for building, training, and deploying machine learning models efficiently. AI engineers should also have a solid understanding of algorithms and data structures in Python to optimize solutions and manage complex data sets effectively. Knowledge of GitHub for version control is important, too, as it facilitates collaboration, code sharing, and version tracking within and across teams.

In addition to these foundational programming skills, an AI engineer must be adept at handling real-time data processing: vital for applications that require immediate analytical results and decision-making, like in autonomous vehicles or financial trading algorithms. Proficiency in using Python for these tasks allows engineers to implement solutions that can process and analyze streaming data efficiently. As AI continues to evolve, being skilled in both the theoretical aspects of machine learning and the practical applications of programming ensures that AI engineers are well-equipped to tackle emerging challenges and drive innovation in the field.

What programming language should I learn?

If you’re aiming to become an AI engineer, the first programming language you should learn is Python. It is the most widely used language in the field due to its simplicity and readability. Plus, it offers powerful libraries and frameworks such as TensorFlow, PyTorch, and Scikit-Learn that are specifically tailored for developing machine learning and deep learning models. Python’s extensive community support and wealth of open-source resources also make it an ideal starting point for beginners. As you progress, exploring other languages like R for statistical analysis, Java for system integration, or C++ for performance-critical applications can further enhance your skill set.

Learning tip: CodeSignal’s learning path in AI Theory and Coding teaches you to build machine learning algorithms—the foundation of AI—from the ground up. Follow this series of 6 courses to gain in-depth knowledge of AI theory, implement your own gradient descent, and learn ensemble methods, unsupervised learning, and more. 

Tools and frameworks

AI engineers must be proficient in a variety of tools and frameworks that are foundational to developing effective AI solutions. TensorFlow and PyTorch are two of the most prominent frameworks for deep learning that allow for easy model building, training, and deployment. For more traditional machine learning tasks, Scikit-learn offers a range of simple and efficient tools for data mining and data analysis. Data manipulation is another critical aspect of AI, and tools like Pandas and NumPy are excellent for handling and transforming data. Jupyter Notebook is another useful tool that allows for prototyping, experimenting with models, and interactive coding, which is particularly useful for visualization and analysis during development. 

Additionally, knowledge of GitHub is essential for code sharing and collaboration, as it enables you to manage projects efficiently while working with globally distributed teams. Lastly, being adept with Spark can significantly enhance your ability to handle big data processing, particularly for applications that require analyzing large datasets in real time. 

AI-specific skills: machine learning and natural language processing

While AI engineers need many of the same skills as other kinds of software engineers, they also need specialized knowledge and skills related to building and optimizing AI models. Two core areas to focus on when starting your journey toward becoming an AI engineer are machine learning (ML) and natural language processing (NLP). Machine learning is a subset of AI that uses algorithms that learn from data to make predictions. Natural language processing—another subset of AI—refers to machine learning technology that gives computers the ability to interpret and manipulate human language.

Core ML skills for AI engineers include: 

Core NLP skills for AI engineers include: 

Learning tip: Want to dive deeper in the AI-specific skills needed for AI engineering roles? Download CodeSignal’s latest whitepaper on the core skills needed for AI, ML, and prompt engineering roles, and how to fill these skill gaps on your technical teams. 

Soft skills are important if you want to start a career in AI

Soft skills are often just as important as technical abilities when it comes to getting hired as an AI engineer. Here are a few to focus on:

6 routes for becoming an AI engineer in 2024 and beyond

Getting into AI development isn’t easy, but it’s possible—and there are many ways to do it. Below, we describe 6 routes aspiring AI engineers can take to break into the field.

Participating in online courses and bootcamps

Participating in online courses and specialized AI bootcamps is an effective way to break into an AI engineering career. Online courses in AI topics allow learners to explore a range of topics at their own pace, from anywhere in the world. They are often a good fit for aspiring AI engineers who have a background in another technical field, like software development, by helping them fill skill gaps specific to AI engineering. 

Specialized AI bootcamps, on the other hand, offer an intensive, focused curriculum that immerses participants in practice-based learning. Bootcamps often involve hands-on projects to build students’ theoretical knowledge and practical skills applicable in professional settings. Both of these non-traditional educational paths can equip you with the necessary technical skills and practical experience to make a confident entry into the field of AI engineering.

Learning tip: CodeSignal’s Journey into Machine Learning with Sklearn and Tensorflow learning path builds your skills in core ML skills using popular Python libraries. Take this series of 5 courses to learn fundamental machine learning models with Sklearn, master regularization techniques, train neural networks using TensorFlow, and more.

Pursuing a formal education in AI

Pursuing a formal education in AI, such as bachelor’s and master’s degrees, is a common—though time- and cost-intensive—starting point for a career as an AI engineer. As specific degree programs in AI are still relatively rare, most aspiring AI professionals opt for degrees in closely related fields like computer science, where they can complete coursework that covers essential topics such as statistics, programming, and foundational AI concepts. Some computer science and engineering programs now offer specialized courses or tracks in AI and machine learning, as well. Engaging in thesis or research projects focused on AI can also enhance your understanding and exposure to the field. For those without access to formal degree programs in AI, self-teaching through online courses, bootcamps, and hands-on projects is a viable alternative.

What should I study for a career in AI?

For a career in AI, focus on studying a blend of subjects that build a strong foundational knowledge and practical skills. Core areas should include computer science for understanding programming and software development, statistics and mathematics for analyzing data, and specific AI coursework that covers machine learning, neural networks, and deep learning techniques. Supplement this formal education with hands-on projects that involve real data to help you apply theoretical concepts practically. 

Building a strong portfolio of AI projects

Building a strong portfolio of AI projects is a great way to showcase your skills and stand out in the competitive field of AI engineering. Start by developing real-world AI projects, which demonstrate your ability to apply AI techniques to solve practical problems. Utilize datasets from platforms like Kaggle to work on projects that are relevant and challenging, and which also provide the opportunity to engage in AI competitions and challenges. Participating in hackathons is another excellent way to gain experience, learn quickly, and meet other AI enthusiasts. 

Collaboration on open-source projects can further enhance your portfolio by showing your ability to work with teams and contribute to community-driven developments. You might also consider creating a personal blog or website to display your projects and explain how you built them. This website serves as a dynamic portfolio, can help you connect with others in the field, and may even contribute to AI research. 

What should I do as a project?

When selecting a personal AI project to enhance your portfolio, aim for something that aligns with your interests and the skills you want to develop. A practical approach is to identify a problem that AI can solve or improve, in any sector that’s of interest to you. Using publicly available datasets from platforms like Kaggle, you can tackle real-world issues, such as predicting disease outbreaks, financial forecasting, or even creating AI-driven environmental monitoring systems. Consider integrating a variety of AI technologies—like machine learning, natural language processing, or computer vision—to demonstrate a breadth of skills.

Getting certified

Getting certified through professional certification programs is another popular route to start a career in AI engineering. These programs, often offered through specialized AI bootcamps and continuous education platforms, provide credentials that can enhance your resume and professional credibility. Such certifications are designed to demonstrate your expertise in specific areas of AI (like machine learning, deep learning, and data analysis) to potential employers. They focus on upskilling and ensuring that professionals are up-to-date with the latest technologies and methodologies in the rapidly evolving AI landscape.

Attending industry events and networking with professional AI engineers

Another way you can pivot to a career in AI engineering is by attending industry events and networking with professional AI engineers. Participation in AI conferences, workshops, webinars, and virtual events provides valuable opportunities to learn about the latest advancements and trends directly from industry leaders. Joining AI meetups and local groups can also help you learn from and network with peers and experts in the field. Establishing a network of contacts within the AI community can open doors to  mentorship, collaborations, and sometimes even job opportunities. 

Getting experience through internships

Finally, securing an internship in AI engineering is an effective way to break into a career in this field. These positions offer hands-on experience and allow you to apply academic knowledge to real-world problems under the guidance of experienced professionals. Internships often involve working on specific projects where you can develop and refine skills related to machine learning, data analysis, and algorithm development. This practical exposure both enhances your skills and boosts your resume, making you a more competitive candidate for future job openings. Internships also provide a valuable opportunity to build professional networks and gain insights into the industry, and can even help you find mentorship and discover job opportunities post-internship.

Frequently asked questions

Here are some of the most common questions we hear from aspiring Ai engineers about how to get started.

How can I start a career in AI?

To start a career in AI, focus first on acquiring foundational knowledge through education, whether that’s online courses, a specialized AI bootcamp, an undergraduate or graduate degree program, or a combination of these. Gain practical experience by engaging in internships, developing personal AI projects, or contributing to open-source initiatives. It also helps to expand your professional network and stay current on AI innovations by attending AI conferences, workshops, and local meetups.

How hard is it to get into AI development?

Getting into AI development can be challenging due to the complex blend of technical skills and theoretical knowledge required, including proficiency in programming, statistics, and machine learning concepts. However, the abundance of resources such as online courses, bootcamps, and community projects makes it increasingly accessible for those committed to learning and developing their skills. Success in entering the field often depends on dedication to ongoing education, practical experience through projects or internships, and active participation in the AI community to build your professional network.

How long does it take to become an AI engineer?

The time it takes to become an AI engineer can vary widely depending on your starting point and how intensively you pursue your studies and experience. Typically, acquiring a bachelor’s degree in computer science or a related field takes about four years, followed by additional specialized training in AI through courses, bootcamps, or a master’s program, which can take anywhere from a few months to two years. Gaining hands-on experience through internships, personal projects, or in your current role can take additional time.

How can I identify what I need to learn and cover any knowledge gaps?

To identify what you need to learn to pursue  a career in AI engineering, start by assessing your current skills against the requirements of job listings or roles that interest you. Use self-assessment tools in online courses that specialize in AI  to pinpoint areas for improvement. It’s also worthwhile to seek feedback and advice from professionals in the field through networking, mentorship, or participating in forums and community groups.

Accelerate your AI engineering career with CodeSignal Learn

This guide has walked you through the responsibilities of different types of AI engineers, skills needed for this career, and the routes you might take to break into a career in AI engineering. Landing a role as an AI engineer isn’t easy, but, fortunately, there are many resources available to help you prepare.

A great place to start is with CodeSignal Learn, an online learning platform that provides a practice-based and outcome-driven learning experience featuring one-on-one support from our AI tutor and guide, Cosmo. CodeSignal Learn offers learning paths in AI and machine learning that take you from building foundational skills in data preprocessing, to training neural networks, to even building neural networks from scratch. 

Start learning for free today:

The post How to become an AI engineer appeared first on CodeSignal.

Our latest learner story comes from Abbey Helterbran, a recent college grad and, now, full-time tech consultant for a software service company. Abbey took courses on Pandas and data analysis on CodeSignal Learn to refresh and level up coding skills that she uses on the job. 

What did you want to learn?

“I graduated in May with a degree in management information systems. In August, I started my full-time job. I work as a tech consultant for a software service company, so I still do some coding. A lot of it is more client-based work and configuration, but coding is definitely a big part of my job. 

Because I knew that coding would be a big part of my job, if I had extra time in my first few months, I would practice different coding skills on CodeSignal and in [my company’s] own system, using our own language. That’s what really led me to becoming a more frequent [CodeSignal] user over the past couple of months: knowing that building that skill would really benefit me later down the line in my current role.”

How do you like to learn?

“I really value interactivity, especially when you’re learning a technical skill. In the past, I have tried to learn via static sources where you just read information and then have to use it for yourself—and for me, that’s just not successful. I don’t really think that’s a great way to learn coding. I think that the best way to learn something that you’re going to do hands-on is through a platform where you can do it hands-on.”

What was learning with CodeSignal like?

“The first thing I noticed was that the types of lessons available are very relevant to the tech scene right now, and to things that people in my field and similar fields would be interested in. I took a class in college about using Python, Pandas, and Seaborn for data analytics, so I did that lesson in CodeSignal Learn first, which was nice because I had somewhat of a background in it—but that was college, and I have not taken it in a couple of years. I really enjoyed the examples that were given in that [Learn] course, and I enjoyed working with Cosmo*—it was very helpful to my learning experience.

And, I was really impressed by the type of feedback that Cosmo would give you about your code. It’s honestly kind of insane to me that the feedback is so targeted and so specific. There were even times where, for example, if I didn’t change a variable name, I would get feedback like, ‘you did this well and completed the task, but you may want to consider changing the variable name for context for yourself, because it’d be better to know what the variable actually is.’ Feedback like that was honestly better than the humans that I’ve worked with and gotten code feedback from.”

*Cosmo is our friendly AI-powered tutor and guide built into CodeSignal Learn.

What’s next for you?

“I’m very early in my career. I’ve only been a professional for 8 months now. But my company functions in a data analytics space—a blend of HR and data analytics. So learning about data analytics is never bad for me because I do touch it in [my role], even if I don’t do it myself. Depending on where I want to go in the future, if I wanted to make a career move, these skills would be very useful to me.”

Get started

Ready to learn in-demand skills like AI, data science, and Python programming? Sign up for CodeSignal Learn to get started for free.

Did CodeSignal Learn help you learn a new coding language, prep for an interview, or land a new role? We’d love to hear from you! Share your story with us here.

The post How a tech consultant boosts her coding skills with CodeSignal Learn appeared first on CodeSignal.

To break into this lucrative career path, you’ll need to develop the key skills that data science roles today require. These include skills in mathematics and statistics, computer programming, and machine learning (ML), to start. Mastering these skills can be challenging, but it is entirely possible—even if you don’t have a background in math or computer science. In fact, CodeSignal has helped thousands of learners with no technical background build their skills in data science. 

This guide will walk you through what it takes to start a career in data science, regardless of your background or experience. We’ll dive into common roles in data science (and how much they pay), skills you need for a data science career, different routes into data science, and more. Let’s get started.

Jump to a section:

• What is data science? Definition, roles, and job outlook
• Useful skills for breaking into data science
• 6 routes for breaking into data science in 2024
• Frequently asked questions
• Accelerate your data science career with CodeSignal Learn

What is data science? Definition, roles, and job outlook

Data science is an interdisciplinary field that uses scientific methods, statistics, and even machine learning to extract knowledge and insights from data. Many types of industries hire data scientists, data analysts, and other related roles to analyze data to drive business decisions.

What does a data scientist do?

A data scientist operates at the intersection of statistics, computer science, and domain expertise to extract actionable insights from data. They employ a variety of tools and methodologies, such as machine learning algorithms, data visualization techniques, and predictive modeling, to analyze complex datasets and solve challenging problems. 

A data scientist’s work often involves cleaning and preparing data, performing exploratory data analysis, building and validating predictive models, and communicating findings to stakeholders in an accessible manner. They play a crucial role in decision-making processes across various sectors by turning vast amounts of raw data into strategic insights that can drive business outcomes, enhance customer experiences, and more.

What qualifications do you typically need to become a data scientist?

A majority of data scientists today hold at least a bachelor’s degree. A 2023 study found that 91 percent of data scientists have an undergraduate or more advanced degree. However, earning a bachelor’s degree in a technical field—or even a non-technical field—is not the only path to becoming a data scientist.

Increasingly, employers are focusing on hiring candidates who have the right skills for the role, regardless of their educational background. Many data scientists have built these skills through online courses, bootcamps, and self-directed portfolio projects.

Understanding the different types of data science roles and responsibilities

Within the field of data science, there are a variety of roles with varying levels of seniority and specialization. Three of the most common are data analysts, data scientists, and machine learning (ML) scientists. 

Data analyst

A data analyst is responsible for collecting, processing, cleaning, and analyzing large sets of data to extract meaningful insights and inform decision-making within an organization. They utilize statistical analysis (in a tool like SAS, or a programming language like Python) and data visualization tools (Tableau, Looker, and others) to identify trends, patterns, and correlations in data. 

Data analysts also play an important role in creating reports and presentations to communicate their findings to stakeholders, aiding in strategic planning and problem-solving. They often collaborate cross-functionally to ensure data integrity and optimize data-driven solutions for various business challenges.

The average salary for a Data Analyst in the US is $89,313 per year, according to recent data from Glassdoor. 

Data scientist

Compared to a data analyst, a data scientist delves deeper into data exploration and analysis by using advanced statistical techniques and predictive modeling to extract actionable insights. They harness ML and AI algorithms to develop sophisticated models that can forecast trends and behaviors. 

Data scientists generally possess strong programming and scripting skills, often utilizing languages like Python or R to manipulate data and build complex algorithms. Often, they also have skills in big data technologies like Hadoop and Spark that allow them to efficiently process and analyze large datasets. Their skills in data visualization and communication allow them to effectively translate complex findings into accessible insights for stakeholders.

The average salary for a Data Scientist in the US is $154,655 per year, according to recent data from Glassdoor. 

ML scientist

Within a data science team, a machine learning (ML) scientist specializes in designing and implementing ML models to solve complex problems—often related to analyzing large and complex datasets. They are adept at utilizing deep learning techniques and neural networks to perform tasks like image recognition, natural language processing (NLP), and recommendation systems. 

ML scientists engage in experimentation and A/B testing to fine-tune models and optimize performance. Their expertise in advanced programming languages like Python, as well as frameworks like TensorFlow and PyTorch, enables them to develop and deploy scalable and efficient algorithms. ML scientists also contribute to ongoing research and development in AI technologies to help their organizations best leverage and advance AI and ML innovation. 

The average salary for a Machine Learning Scientist in the US is $216,172 per year, according to recent data from Glassdoor. 

Useful skills for breaking into data science

There are a few key skill areas that are essential for a variety of data science roles, whether that’s a data analyst, data scientist, or another related role. Here are a few to get you started.

Mathematics

Mathematics, and especially statistics, like at the heart of data science. To become a data scientist, you’ll need a strong foundation in statistical analysis—skills like hypothesis testing, regression analysis, and statistical inference—to interpret data and make accurate predictions. You will also need to understand the theory behind statistics to make sense of data patterns, choose the right statistical tests, and handle missing data appropriately, for instance. 

With the rise of ML in data science roles, data scientists should also have a strong understanding of linear algebra, which underpins ML algorithms and data transformations. You may want to build proficiency in calculus and optimization techniques, which can allow you to better develop and fine-tune models. 

What if I’m bad at math?

If math isn’t your strong suit, don’t let that deter you from pursuing data science. Many resources are available to help you strengthen your math skills—from online courses to blogs and podcasts tailored to beginners. Data science also involves applying math in practical contexts, which can make learning more intuitive. Focus on foundational concepts in statistics and linear algebra, and practice applying them in data science projects to build your confidence and proficiency.

Analytical skills

In addition to math skills, a data scientist needs strong analytical and critical thinking skills for effective data interpretation, insights extraction, and problem-solving. You’ll want to become adept at hypothesis testing and experimentation so you can validate analyses and conclusions. These skills will allow you to help your organization make informed and strategic choices based on data insights.

Machine learning and deep learning

In the domain of data science, ML skills are crucial for extracting deeper insights from data. If you’re looking to become a data scientist or an ML scientist, you should master supervised and unsupervised learning algorithms, be proficient in neural networks, and use deep learning frameworks such as TensorFlow and Keras. You’ll also need skills in model evaluation, tuning for optimal performance, and Natural Language Processing (NLP) for text analysis. These capabilities are essential for developing sophisticated models that can interpret and predict complex patterns in data.

Data visualization

Data scientists need proficiency in designing effective charts and graphs; this can be done using Python libraries like Seaborn and Matplotlib, or with tools like Tableau or PowerBI that create interactive data visualizations and dashboards. An understanding of color theory is also important in this context, as it influences the perception and interpretation of visualizations, and can help highlight trends and distinctions in the data more intuitively. You’ll also want to hone your skills in data storytelling and presentation to effectively communicate the complex information being presented in the visualizations they create. 

Programming

To embark on a data science career, you should have proficiency in at least one programming language: likely Python or R. This foundation enables you to leverage data manipulation and analysis libraries like pandas and NumPy effectively. While mastering one language is essential, familiarity with several can broaden your skill set and opportunities. 

You will also want to be familiar with version control systems, particularly Git, for managing code changes and collaboration. Additionally, being comfortable with development environments and tools, including Jupyter notebooks and VS Code, will enhance the efficiency of your coding and data analysis. 

Should I learn Python or R?

Choosing between Python and R depends on your specific data science goals. Python is versatile, with a vast library ecosystem for various applications, making it ideal for machine learning, web development, and automation. R, on the other hand, is tailored for statistical analysis and graphical models, preferred in academia and research. Consider your project needs and the industry you aim to join when deciding which to learn.

Tools and frameworks

Lastly, to become a data scientist, it’s important to learn tools and frameworks that handle large-scale data processing like Hadoop and Spark. Hadoop is essential for distributed storage and processing of big data, while Spark offers fast, in-memory data processing. Familiarity with these technologies will enable you to tackle complex data challenges effectively.

6 routes for breaking into data science in 2024

There is no one right way to start a career in data science. Each person’s path will vary based on their previous educational and work experiences. The path you choose should ensure you’re mastering job-relevant data science skills and building a portfolio of relevant projects. 

Participating in online courses and bootcamps

Online courses or bootcamps are great ways to start your journey toward a career in data science, especially if your educational background isn’t directly related to the field. These programs are designed to bridge the gap between theoretical knowledge and practical skills, and provide you with hands-on experience in using data science tools. You’ll have the opportunity to tackle real-world problems through projects, which help you both develop skills and build a portfolio of data science projects. 

What online courses should I take?

The best online courses in data science offer many opportunities for practice, provide one-on-one learning support, and focus on achieving skill mastery—not just completing a course.

CodeSignal Learn offers a range of free learning paths for launching or boosting a career in data science. Each path includes several courses and hundreds of practices—all supported by Cosmo, a built-in AI tutor and guide who provides personalized feedback and unblocks you when you get stuck. Here are a few learning paths to get you started:

• Introduction to Data Analysis with Python
• Journey into Data Science with Python
• Journey into Machine Learning with Sklearn and TensorFlow 

Pursuing a formal education in data science

Earning a bachelor’s or master’s degree in data science or a related field is a common path for those aiming to become data scientists. These programs typically include coursework in statistics, machine learning, and programming, along with capstone projects that allow for practical application of skills.

Many data scientists earn degrees in other technical fields, like computer science, engineering, statistics, and mathematics; all of these help build the skills a student needs to later become a data scientist. If you are willing to spend a minimum of four years pursuing an education full-time, earning a bachelor’s degree in a data science-related field is one path you can take to pursue this career.

Building a strong portfolio of data science projects

Building a portfolio of real-world data science projects is another great step toward breaking into a career in data science. Developing projects that solve practical problems showcases your ability to apply data science skills effectively. 

Consider participating in Kaggle competitions, or similar competitions, that can demonstrate your ability to tackle data science challenges. Whatever you produce, upload your projects to GitHub to both serve as a repository of your work and to demonstrate your familiarity with version control systems, an essential skill in the field. 

What should I do as a project?

When selecting a project, choose a topic that genuinely interests you—this will keep you motivated and engaged. Consider solving a problem you’ve encountered personally, or something relevant to current events. Projects that involve cleaning and analyzing datasets, building predictive models, or even developing a tool or application that addresses a specific need can be particularly impactful. 

Getting certified

Obtaining a certification in data science is another strategic way to kickstart your career in this field. Certifications from reputable institutions or platforms can provide you with a structured learning path that builds your skills in essential data science topics like analytics, machine learning, and programming with Python or R. These certifications validate your skills for potential employers, and they also ensure you have a strong foundational knowledge and hands-on experience with real-world data science projects. 

CodeSignal Learn offers mastery certifications in data science and data analysis skills, which are recognized by top employers in the CodeSignal community. 

Getting experience through data science internships

Securing a data science internship is a pivotal step in starting a career in this field. Internships offer you a hands-on experience where you can apply theoretical knowledge to real-world problems, all using core data science skills like data extraction and presenting insights. They also provide exposure to essential tools like SQL, Tableau, and Hadoop, and introduce you to adjacent technologies like machine learning and AI, which make you a more versatile candidate across various industries​​.

The value of these internships extends beyond skill development; they are a gateway to building a professional network with peers, mentors, and industry experts. This network can be invaluable in navigating the job market and identifying career opportunities—or even lead directly to job offers.

Frequently asked questions

Here are a few of the common questions asked by individuals aspiring to start a career in data science:

How can I get into data science with no experience?

Breaking into data science without prior experience is challenging, but entirely possible. Start by taking online data science courses which cover fundamental concepts and practical applications within the field. These courses often provide a comprehensive curriculum, including programming languages like Python or R, essential for data manipulation and analysis​​.

Then, take on personal projects or find an internship that can offer practical experience. These experiences both enhance your resume and provide tangible evidence of your capabilities to potential employers​​. Upload your projects to Github to showcase your work to potential employers. Lastly, remember that networking is a great way to break into data science. Connect with professionals in the field who can offer guidance—and maybe even connect you to job opportunities​​.

How to know if my degree is good for data science?

A wide range of degrees can equip you with the skills you need for a career in data science. Fields such as computer science, statistics, mathematics, and engineering are directly applicable, as they provide a strong technical background in programming and statistical analysis. However, degrees in the social sciences and even humanities can also prepare you with valuable skills in critical thinking, research methodology, and data interpretation. 

How can I break into data science with an unrelated undergrad?

The key to breaking into data science if you have an undergraduate degree not related to data science is to supplement your education with data science-specific skills, such as machine learning, data visualization, and big data technologies. You can gain these skills through online courses, certifications, or self-study.

How can I identify what I need to learn and cover any knowledge gaps?

To identify what you need to learn and cover any knowledge gaps in data science, start by assessing the core competencies required in the field: statistical analysis, machine learning, programming languages (Python, R), and data visualization tools, to start. Review current job listings for data science roles to better understand what specific skills employers are looking for and how your skillset aligns; then, consider taking online courses to help you fill any gaps in your skills. 

Accelerate your data science career with CodeSignal Learn

Starting a career in data science may not be easy, but it is possible—even if you don’t have a degree or work experience in the field. This guide has provided an overview of a range of data science roles and responsibilities, core skills you need for a career in data science, and six different routes you can take for breaking into the field. 

A great place to get started is by completing the Journey into Data Science with Python learning path in CodeSignal Learn. This series of 7 courses and over 200 practice exercises will take you from the basics of using popular Python libraries for data science, to performing large-scale data manipulation, to building skills in supervised and unsupervised machine learning: all core skills for launching a career in data science. Start learning for free today. 

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