How to become a data scientist 

Data scientists are in high demand—and for good reason. Companies rely on them to turn large, messy datasets into insights that drive smarter decisions.  

It’s a role that offers strong job growth, competitive pay, and the flexibility to work across industries (and even remotely). In fact, data scientists command a median salary in the six figures ($112,590), according to US Bureau of Labor Statistics data. And the agency projects job growth in the field to soar 36% by 2033, far exceeding the average for all occupations.  

If you’re looking to break into this high-impact, high-growth field, this step-by-step guide on how to become a data scientist breaks down what to expect along the way. 

Key points

• Data scientists use coding, statistics, and machine learning to give meaning to raw data. It’s a role in high demand across many industries. 
• You don’t necessarily need a degree to get started in data science. Bootcamps, online courses, and self-learning paths can all lead to a successful career. 
• Core technical skills include Python, SQL, data visualization, and a solid foundation in math and statistics. These tools help you explore data, build models, and present findings clearly. 
• Gaining real-world experience through projects, internships, or freelance work is key. A well-rounded portfolio often carries more weight than a resume alone. 
• Breaking into data science takes time. But with consistent learning and a curiosity-driven mindset, becoming a data scientist is within reach. 

What does a data scientist do?

Data scientists use data to solve real problems. They explore massive datasets, build predictive models, and turn raw numbers into practical recommendations. One day, a data scientist might be training a model to flag suspicious transactions. The next, they might be digging into user behavior to uncover what’s working (and what’s not). 

Whatever the day’s task, data science work often includes writing code, building models, and communicating results through clear visualizations or reports. 

The role mixes technical skill with curiosity and clear thinking. It’s about finding patterns in massive datasets, asking better questions, and figuring out what the data is actually saying. A data scientist’s findings might be used to predict customer behavior, improve product features, or proactively identify risks.  

Where data scientists fit into the field

While data analysts focus more on interpreting existing data, data scientists create new models and tools to dig deeper. Machine learning (ML) engineers, on the other hand, take those models and scale them for use in real-time systems. Data scientists often sit in the middle, shaping the questions, building the models, and translating the answers into action. 

Step-by-step guide to becoming a data scientist

There’s no single path to becoming a data scientist, but most successful journeys have the same building blocks. It takes a mix of education, technical skills, real-world experience, and, perhaps most important, the ability to communicate your analysis in a way non-data teams can digest.  

No matter where you’re starting from, following these steps can elevate your skills and move you closer to a role in the field. 

Build a strong educational foundation

Most data scientists begin by learning how computers process and analyze data. That often means earning a degree in computer science, data science, statistics, or a related field. These programs develop critical thinking, math, and programming skills—core abilities you’ll use every day on the job. 

But a traditional degree isn’t your only path. Many people break into the field through online courses or bootcamps. These programs are faster and more focused, making them a good fit for career-switchers or those looking to level up quickly. 

So, yes, you can become a data scientist without a degree. But it takes initiative. Self-taught paths, for example, involve building your own curriculum and consistent practice. And you’ll need to showcase your newly developed skills through real-world projects (more on that later).  

Experience in related fields like software engineering, economics, or analytics can also transfer well to data science. What matters most is that you know how to work with data and use it to solve real problems. 

Learn core technical skills

Data scientists use code every day. Python and R are two common programming languages in the field. They are great for analyzing data, building models, and automating tasks. SQL is essential, too. It’s the language of databases, and you’ll rely on it to pull the data you need before any analysis can begin. 

Once you’ve got your data, you’ll need to make sense of it and present your findings. That’s where data visualization tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn come in. They’re your allies in turning raw numbers into visual stories others can act on. And the ability to storytell is one of the most valuable skills in data science roles. 

But your skills won’t shine without a strong foundation in math. A working knowledge of statistics, probability, and linear algebra helps you ask better questions, build better models, and spot red flags in your results.  

Understand machine learning and AI fundamentals

Machine learning is what allows computers to learn from data and make predictions without being explicitly programmed. ML algorithms are a core part of what sets data science apart from more traditional analytics roles. It’s how data scientists move from analyzing what happened to predicting what’s likely to happen next. 

Start with the basics—regression, classification, clustering, and decision trees. They’re foundational to data analysis and prediction and break down like this: 

• Regression: Predicting a number, like housing prices 
• Classification: Sorting data into categories, like spam vs. not spam 
• Clustering: Grouping similar data points, like customer segments 
• Decision trees: Flowchart-like models for making decisions based on data 

These techniques show up in everything from predicting customer churn to detecting fraud. As you build confidence, you can explore more advanced topics like neural networks (loosely inspired by the human brain) and deep learning (neural networks with many layers that power things like image recognition and voice assistants). These techniques are behind everything from recommendation engines to image recognition. 

You don’t need to master everything at once, but you do need to understand how machine learning models work (and how to evaluate them). Online courses from platforms like Coursera, edX, or Fast.ai are great places to start, especially if you’re learning on your own. 

Network and learn from industry experts

The “who you know” of data science is just as important as what you know. Connecting with people already working in the field can give you insight into what companies are looking for, which tools are trending, and how to stay sharp as the field evolves. 

Look for virtual and in-person events like data science meetups, conferences, or hackathons. Follow data professionals on LinkedIn and other social media platforms and reach out with thoughtful questions or comments. Many are open to sharing advice or pointing you toward helpful resources. 

If you can, find a mentor. That might be someone at your current job or a connection you cultivated online. A mentor can be invaluable in helping you navigate the early stages of your career and avoid common pitfalls. 

Get hands-on experience through real projects

The best way to learn data science is to apply it. Start with public datasets from sources like Kaggle, Google Dataset Search, or government open-source data portals. Pick a goal that interests you—perhaps predicting housing prices or analyzing social media trends—and work through the full process:  

• Cleaning the data 
• Building a model 
• Explaining your results 

If you’re looking for experience beyond personal projects, try freelancing, volunteering for nonprofits, or contributing to open-source projects. Internships, even if they’re short-term or unpaid, are another great way to get exposure to real-world data and tools. 

Finally, build a portfolio to showcase your work. Highlight a few well-documented projects that show how you think through problems and communicate results. Employers want to see how you approach data, not just that you can code and manipulate it. 

Apply for entry level-roles

Once you’ve built your skills and completed a few projects, you’re ready to start applying. Most data scientists don’t land the exact title they want right away. And that’s totally normal. 

Entry points often include roles like data analyst, business analyst, or junior data scientist. These positions give you the chance to work with real data, collaborate with teams, and develop your technical skills. 

When applying, tailor your resume to highlight technical tools and your project experience and problem-solving ability. If you’ve built a strong portfolio, link to it directly. Employers want to see how you communicate data insights rather than read a list of tools you’ve used. 

Also, keep in mind that data science is a broad field. You’ll find opportunities in marketing, health care, finance, government, and more. Target companies or industries you care about, and be ready to explain how your work connects to their goals. 

How long does it take to become a data scientist?

The timeline depends on where you’re starting and how you’re learning. If you already have a degree in a related field—like computer science, statistics, or engineering—you could be job-ready in about one to two years with focused upskilling and hands-on practice. 

If you’re starting from scratch or switching careers, it might take closer to two to five years. That includes time to learn programming, math, and machine learning basics, build a portfolio, and gain practical experience. 

Data scientists often work closely with software engineers on development teams. When you compare these timelines to software engineering, they are fairly similar. Both paths require a mix of technical knowledge, problem-solving skills, and project work.  

The biggest difference? Data science leans more heavily on statistics and model building, while software engineering focuses more on system design and development. 

Tips to stand out in a competitive field

Once you’ve got the basics down, it’s all about differentiation. One of the best ways to stand out is by helping on open-source projects or sharing your own data work online. This includes blog posts, GitHub repositories, or tutorials. This shows initiative and helps others see how you think. 

Mastering tools beyond the standard stack can also set you apart. Cloud platforms like Amazon Web Services (AWS), Google Cloud, or Microsoft Azure are widely used in production environments. If you’re comfortable working with large-scale data and deploying models, you’ll have an edge. 

Lastly, get involved in the data community. Speaking at meetups, joining competitions, or mentoring others shows leadership. And that matters just as much as technical skill. 

Is a career in data science right for you?

Learning how to become a data scientist starts with a curious, analytical mind and a passion for solving problems that have a real impact. If this sounds like you, there’s a place for you in data science. You don’t need a traditional tech background. You just need the willingness to learn, experiment, and keep going. 

The field moves fast. Staying sharp means constant learning through mentors, internships, continued education—whatever keeps your skills evolving. Anything you can add to your tool belt helps you stand out in a world that runs on data.  

Ready to take the next step? Explore Intuit’s data science roles today and see how you can help shape the future and power prosperity around the world.