The world is becoming increasingly data-driven. Data is one of the most valuable resources a company can have, but without a data scientist, it’s just numbers.

Businesses everywhere are looking for professionals who can turn raw data into clear insights. According to the U.S. Bureau of Labor Statistics, jobs for data scientists are expected to grow by 34% between 2024 and 2034, much faster than most careers.

Becoming a data scientist takes more than coding or statistics. It’s a mix of math, computer science, business knowledge, and communication skills. This combination makes the role both challenging and in demand.

I know it’s possible to get there. I started with a history degree and later became a machine learning engineer, data science consultant, and founder of Dataquest. With the right plan, you can do it too.

What is a Data Scientist?

A data scientist is someone who uses data to answer questions and solve problems. They collect large amounts of information, clean it, analyze it, and turn it into something actionable.

They use tools like Python, R, and SQL to manage and explore data. They apply statistics, machine learning, and data visualization to find patterns, understand trends, and make predictions.

Some data scientists build tools and systems for users, while others focus on helping businesses make better decisions by predicting future outcomes.

What Do Data Scientists Do?

Data scientists wear many hats. Their work depends on the company and the type of data they handle, but the goal is always the same: to turn data into useful insights that help people make data-driven decisions.

Data science powers everything from the algorithm showing you the next TikTok video to how ChatGPT answers questions to how Netflix recommends shows.

Some data scientist responsibilities include:

• Collect and clean data from databases, APIs, and spreadsheets to prepare it for analysis.
• Analyze and explore data to find trends, patterns, and relationships that explain what’s happening.
• Build machine learning models and make predictions to forecast sales, detect fraud, or recommend products.
• Visualize and communicate insights through charts and dashboards using tools like Tableau, Matplotlib, or Power BI.
• Automate and improve systems by creating smarter processes, optimizing marketing campaigns, or building better recommendation engines.

In short, they help businesses make smarter decisions and work faster.

The Wrong and Right Way

When I started learning data science, I followed every online guide I could find, but I ended up bored and without real skills to show for it. It felt like a teacher handing me a pile of books and telling me to read them all.

Eventually, I realized I learn best when I’m solving problems that interest me. So instead of memorizing a checklist of skills, I began building real projects with real data. That approach kept me motivated and mirrored the work I’d actually do as a data scientist.

With that experience, I created Dataquest to help others learn the same way: by doing. But courses alone aren’t enough. To succeed, you need to learn how to think, plan, and execute effectively. This guide will show you how.

How to Become a Data Scientist:

• Step 1: Earn a Degree (Recommended, Not Required)
• Step 2: Learn the Core Skills
• Step 3: Question Everything and Find Your Niche
• Step 4: Build Projects
• Step 5: Share Your Work
• Step 6: Learn From Others
• Step 7: Push Your Boundaries
• Step 8: Start Looking for a Job

Now, let’s go over each of these one by one.

Step 1: Earn a Degree (Recommended, Not Required)

Most data scientists start with a degree in a technical field. According to Zippia, 51% of data scientists hold a bachelor’s degree, 34% a master’s, and 13% a doctorate.

A degree helps you build a solid foundation in math, statistics, and programming. It also shows employers that you can handle complex concepts and long-term projects.

Relevant degrees include computer science, statistics, mathematics, data science, or engineering.

If university isn’t an option, you can still learn online. Platforms like Dataquest, Coursera, edX, and Google Career Certificates have trusted online courses and programs that teach the same essential skills through practical, hands-on projects.

Step 2: Learn the Core Skills

Even if you can’t study at a university or enroll in a course, the internet and books offer everything you need to get started. So, let’s look at what you should learn.

If you come from a computer science background, many concepts like algorithms, logic, and data structures will feel familiar. If not, Python is a great starting point because it teaches those fundamentals in a practical way.

1. Programming languages

Start with Python. It’s beginner-friendly and powerful for data analysis, machine learning, and automation.

Learn how to:

• Write basic code (variables, loops, functions)
• Use data science libraries like pandas, NumPy, and Matplotlib
• Work with raw data files (e.g., CSVs and JSON) and collect data via APIs

Once you’re comfortable with Python, consider learning R for statistics and SQL for managing and querying databases.

Helpful guides:

1. How to learn Python
2. How to learn R
3. How to learn SQL

2. Math and Statistics

A strong understanding of math and statistics is essential in data science. It helps you make sense of data and build accurate models.

Focus on:

3. Data Handling and Visualization

Being able to clean, organize, and visualize data is a key part of any data scientist’s toolkit. These skills help you turn raw data into clear insights that others can easily understand.

You’ll use tools like Excel, Tableau, or Power BI to build dashboards and reports, and Python libraries like pandas and Matplotlib for deeper analysis and visualization.

Here are some learning paths to guide you:

4. Core Concepts

Once you’ve built a solid technical foundation, it’s time to understand how these skills fit into the bigger picture.

• How machine learning models work
• How to ask business questions and measure results
• How to translate data insights into real business impact

Step 3: Question Everything and Find Your Niche

The data science and data analytics field is appealing because you get to answer interesting questions using actual data and code. These questions can range from “Can I predict whether a flight will be on time?” to “How much does the U.S. spend per student on education?”

To answer these questions, you need to develop an analytical mindset.

The best way to develop this mindset is to start by analyzing news articles. First, find a news article that discusses data. Here are two great examples: Can Running Make You Smarter? or Is Sugar Really Bad for You?

Then, think about the following:

• How they reach their conclusions given the data they discuss
• How you might design a study to investigate further
• What questions you might want to ask if you had access to the underlying data

Some articles, like this one on gun deaths in the U.S. and this one on online communities supporting Donald Trump, actually have the underlying data available for download. This allows you to explore even deeper.

You could do the following:

• Download the data, and open it in Excel or an equivalent tool
• See what patterns you can find in the data by eyeballing it
• Does the data support the conclusions of the article? Why or why not?
• What additional questions can you use the data to answer?

Here are some good places to find data-driven articles:

Think About What You’re Interested In

After a few weeks of reading articles, reflect on whether you enjoyed coming up with questions and answering them. Becoming a data scientist is a long road, and you need to be very passionate about the field to make it all the way. What is the industry that attracts you the most?

Perhaps you don’t enjoy the process of coming up with questions in the abstract, but maybe you enjoy analyzing healthcare or finance data. Find what you’re passionate about, and then start viewing it through an analytical lens.

Personally, I was very interested in stock market data, which motivated me to build a model to predict the market.

If you want to put in the months of hard work necessary to learn data science, working on something you’re passionate about will help you stay motivated when you face setbacks.

Step 4: Build Projects

As you’re learning the basics of coding, start applying your knowledge to get practical experience. Coursework isn’t enough. Projects help you practice real-world techniques and develop the practical skills employers look for in the job market. It’s a great way to test your knowledge.

Your projects don’t have to be complex. For example, you could analyze Super Bowl winners to find patterns, study weather data to predict rainfall, or explore movie ratings to see what drives popularity. The goal is to take an interesting dataset, ask good questions, and use code to answer them.

As you build projects, keep these points in mind:

• Most real-world data science work involves data cleaning and preparation.
• Simple machine learning algorithms like linear regression or decision trees are powerful starting points.
• Focus on improving how you handle messy data, visualize insights, and communicate results. These are the techniques that make you stand out.
• Everyone starts somewhere. Even small projects can show your creativity, logic, and problem-solving skills.

Building projects early helps you get practical experience that will make your portfolio much stronger when entering the job market.

As you’re learning the basics of data science, you should start building projects that answer interesting questions that will showcase your data science skills.

If you need help finding free datasets for your projects, we’ve got you covered!

Where to Find Project Ideas

Not only does building projects help you practice your skills and understand real data science work, it also helps you build a portfolio to show potential employers.

Here are some more detailed guides on building projects on your own:

Additionally, most of Dataquest’s courses contain interactive projects that you can complete while you’re learning.

Here are just a few examples:

• Profitable App Profiles for the App Store and Google Play Markets — Explore the app market to see what makes an app successful on both iOS and Android. You’ll analyze real data and find out why some book-based apps perform better than others.
• Exploring Hacker News Posts — Analyze a dataset of posts from Hacker News, a popular tech community, and find out which kinds of discussions get the most attention.
• Exploring eBay Car Sales Data — Use Python to work with a scraped dataset of used cars from eBay Kleinanzeigen, a classifieds section of the German eBay website.
• Star Wars Survey — Analyze survey data from Star Wars fans and find fun patterns, like which movie is the most loved (or the most hated).
• Analyzing NYC High School Data — Explore how different factors like income and race relate to SAT scores using scatter plots and maps.
• Classifying Heart Disease — Go through the complete machine learning workflow of data exploration, data splitting, model creation, and model evaluation to develop a logistic regression classifier for detecting heart disease.

Our students have fun while practicing with these projects. Online courses don’t have to be boring.

Take It Up a Notch

After a few small projects, it’s time to level up! Start adding more complexity to your work so you can learn advanced topics. The key is to choose projects in an area that interests you.

For example, since I was interested in the stock market, I focused on predictive modeling. As your skills grow, you can make your projects more detailed, like using minute-by-minute data or improving prediction accuracy.

Check out our article on Python project ideas for more inspiration.

Step 5: Share Your Work

Once you’ve built a few data science projects, share them with others on GitHub! This might just be the way to find internships!

Here’s why:

• It makes you think about how to best present your projects, which is what you’d do in a data science role.
• They allow your peers to view your projects and provide feedback.
• They allow employers to view your projects.

Helpful resources about project portfolios:

Start a Simple Blog

Besides uploading projects to GitHub, start a blog. Writing about what you learn helps you understand topics better and spot what you’ve missed. Teaching others is one of the fastest ways to master a concept.

When I was learning data science, writing blog posts helped me do the following:

• Capture interest from recruiters
• Learn concepts more thoroughly (the process of teaching really helps you learn)
• Connect with peers

You can write about:

• Explaining data science concepts in simple terms
• Walking through your projects and findings
• Sharing your learning journey

Here’s an example of a visualization I made on my blog many years ago that tries to answer the question: do the Simpsons characters like each other?

Step 6: Learn From Others

After you’ve started to build an online presence, it’s a good idea to start engaging with other data scientists. You can do this in-person or in online communities.

Here are some good online communities:

Here at Dataquest, we have an online community where learners can receive feedback on projects, discuss tough data-related problems, and build relationships with data professionals.

Personally, I was very active on Quora and Kaggle when I was learning, which helped me immensely.

Engaging in online communities is a good way to do the following:

• Find other people to learn with
• Enhance your profile and find opportunities
• Strengthen your knowledge by learning from others

You can also engage with people in person through Meetups. In-person engagement can help you meet and learn from more experienced data scientists in your area. Take all the opportunities to learn.

Step 7: Push Your Boundaries

What kind of data scientists do organizations want to hire? The ones that find critical insights that save them money or make their customers happier. You have to apply the same process to learning, keep searching for new questions to answer, and keep answering harder and more complex questions.

If you look back on your projects from a month or two ago, and you don’t see room for improvement, you probably aren’t pushing your boundaries enough. You should be making strong progress every month, and your work should reflect that. Interesting projects will make you stand out among applicants.

Here are some ways to push your boundaries and learn data science faster:

• Try working with a larger dataset
• Start a data science project that requires knowledge you don’t have
• Try making your project run faster
• Teach what you did in a project to someone else

Step 8: Start Looking for a Job

Once you’ve built a few projects and learned the core skills, it’s time to start applying, not “someday,” but now. Don’t wait until you feel completely ready. The truth is, no one ever does.

Start with internships, entry-level roles, or freelance gigs. These give you real-world experience and help you understand how data science works in a business setting. Even if the job description looks intimidating, apply anyway. Many employers list “ideal” requirements, not must-haves.

Don’t get stuck studying forever. The best learning happens on the job. Every interview, every project, and every rejection teaches you something new.

You never know, the opportunity that looks like a long shot might be the one that launches your data science career. The more practical experience you gain, the deeper your knowledge becomes.

Becoming a Data Scientist FAQs

I know what you might be thinking: Is it still worth pursuing a career in data science? Will AI replace data scientists, or will the role evolve with it? What skills do I actually need to keep up?

I get these questions a lot, and since I was once in your shoes, let me share what I’ve learned and help you find the right path.

Is data science still a good career choice?

Yes, a data science career is still a fantastic choice. Demand for data scientists is high, and the world is generating a massive (and increasing) amount of data every day.

We don’t claim to have a crystal ball or know what the future holds, but data science is a fast-growing field with high demand and lucrative salaries.

Will AI replace data scientists?

AI won’t replace data scientists, but it will definitely change what they do. As AI tools become more advanced, data scientists will use them to make decisions faster and with greater accuracy. Instead of doing only technical work, they’ll focus more on strategy and big-picture analysis.

Data scientists will also work closely with AI engineers and machine learning specialists to develop and improve AI models. This includes tasks like choosing the right algorithms, engineering features, and making sure systems are fair and reliable.

To stay relevant, data scientists will need to expand their skills into areas such as machine learning, deep learning, and natural language processing. They’ll also play an important role in ethical AI, helping prevent bias, protect data privacy, and promote responsible use of technology.

Continuous learning will be essential as the field evolves, but AI isn’t replacing data scientists. It’s helping them become even more powerful problem solvers.

What are the AI skills a data scientist needs?

Every data scientist should have a knowledge of the basics, but as artificial intelligence becomes part of nearly every industry, learning AI-related skills is essential.

Start with a strong understanding of machine learning and the ability to use deep learning frameworks like TensorFlow and PyTorch. Learn natural language processing (NLP) for analyzing text data, and make sure you understand AI ethics, especially how to recognize and reduce bias in models.

It also helps to be comfortable with AI development tools and libraries, build some data engineering skills, and learn to work effectively in cross-disciplinary teams.

Continuous learning is key. AI evolves quickly, and the best data scientists keep experimenting, exploring new methods, and adapting their skills to stay ahead.

You’ve Got This!

Studying to become a data scientist or data engineer isn’t easy, but the key is to stay motivated and enjoy what you’re doing. If you’re consistently building projects and sharing them, you’ll build your expertise and get the data scientist job that you want.

After years of being frustrated with how conventional sites taught data science, I created Dataquest, a better way to learn data science online. Dataquest solves the problems of MOOCs, where you never know what course to take next, and you’re never motivated by what you’re learning.

Dataquest is just the lessons I’ve learned from helping thousands of people learn data science, and it focuses on making the learning experience engaging. Here, you’ll build dozens of projects, and you’ll learn all the skills you need to be a successful data scientist. Dataquest students have been hired at companies like Accenture and SpaceX .

I wish you all the best on your path to becoming a data scientist!