How to Become a Data Scientist

The path to become a data scientist can look a little different for everyone — some come from technical backgrounds but need to fill in the theoretical, business-minded gaps. Others already have an analytical, problem-solving approach and need to focus on mastering tools.

What almost every data scientist has in common is a foundation in analytical thinking, a working command of programming and statistics, and real experience solving problems with data.

There is no single required path into data science, and that is part of what makes the field accessible to people from a wide range of academic and professional backgrounds. A computer science graduate, a former financial analyst, and a biologist who picked up Python on the job can all become qualified data scientists.

What ties these paths together is a shared set of building blocks. Every data scientist needs:

The order and the source of that training can vary. A person might build technical skills through a computer science degree and then round it out with a master’s program focused on application. Another might start in an unrelated field, complete a rigorous graduate program like the Master of Applied Data Science, and use the program’s capstone to gain the project experience that employers expect to see.

The question worth asking is not which single path is correct. It is which combination of education, skill building, and experience fits your background and timeline.

With that foundation in mind, here is a practical roadmap for the path itself, broken into five steps you can adapt to your own background and timeline.

Data scientists typically need at least a bachelor’s degree in mathematics, statistics, computer science, or a closely related field, according to the Bureau of Labor Statistics. Some employers require an advanced degree, which can make the specific undergraduate degree subject less important.

Business, economics, and even liberal arts graduates can enter the data science field, particularly when they supplement their degree with coursework or independent study in programming and statistics.

What matters most at this stage is getting comfortable working with numbers and thinking through problems logically. You do not need to love math to succeed in data science, but a working knowledge of it goes a long way. A course or two in statistics and probability builds the foundation you will lean on throughout your career, and a course or two in computer science introduces the kind of logical thinking that every data science tool is built on.

Programming languages. Programming ability, usually in Python or R, lets you clean data, build models, and automate analysis. Python in particular is popular among data scientists. The 2025 Stack Overflow Developer Survey, which collected responses from more than 49,000 developers worldwide, found that Python’s adoption jumped seven percentage points from 2024 to 2025, driven largely by its role in AI and data science work.

Database knowledge. Before you can analyze anything, you need to know how to retrieve, structure, and manage it. That starts with relational databases and SQL, the standard language for querying structured data, and increasingly extends to NoSQL systems, which handle the unstructured and fast changing data common in industries like ecommerce and social media. Being comfortable moving between these systems means you can access whatever data a project calls for instead of waiting on someone else to pull it for you.

Data visualization. Even a strong analysis falls flat if you cannot explain it to the people who need to act on it. Tools like Tableau, Power BI, or Python libraries such as Matplotlib and Seaborn let you turn a dense set of findings into a chart or dashboard a nontechnical stakeholder can understand at a glance.

Choosing the right visualization technique for the story you are telling, whether that is a simple bar chart or a more layered dashboard, matters as much as the analysis itself, since a data scientist’s job is often only finished once someone else has understood and acted on the results.

Many aspiring data scientists build these skills through targeted courses, bootcamps, or self study before pursuing more advanced training. Graduate programs such as UNC’s Master of Applied Data Science also offer a skills refresher course for incoming students who want to shore up their programming or statistics background before diving into the core curriculum.

A master’s degree is not strictly required to work as a data scientist, but it can signify rigorous training and collaboration with experts. A well-designed graduate program builds a competitive edge in a few concrete ways:

UNC Chapel Hill’s online Master of Applied Data Science program is built around this idea of applied, practice based learning rather than research for its own sake. The curriculum spans ten courses and a capstone, covering the full data life cycle from data collection and database management through machine learning, deep learning, and data ethics.

Students can complete the degree in as few as four terms on an accelerated track, and the program does not require GRE or GMAT scores for admission. Courses are taught by faculty from across the university, including the Department of Computer Science, the Department of Statistics & Operations Research, and the Department of Biostatistics at the Gillings School of Global Public Health, giving students exposure to how data science is practiced across disciplines rather than in a single department.

That kind of interdisciplinary structure matters because data science rarely lives in one silo at work. A data scientist at a health system needs to understand clinical context. One at a retailer needs to understand consumer behavior. Programs that draw faculty from multiple fields tend to prepare students for that variety more effectively than a narrowly technical curriculum would.

Coursework teaches you the mechanics of data science. Project experience teaches you how those mechanics hold up against real, imperfect data and real stakeholders who need an answer. This is often the piece of the puzzle that separates a strong candidate from one who has only studied the theory.

Recruiters treat this experience as close to essential. Dataquest’s guide to building a data science portfolio includes an account from Michael Hupp, manager of data science and analytics at G2, who describes asking every candidate about their projects, then testing whether they can explain the results in plain terms. For candidates without professional experience, a portfolio can help stand in for work history.

In UNC’s MADS program, this experience comes primarily through the capstone, a required, team-based course where small groups work directly with industry, government, or community partners to solve an actual data problem and deliver a finished product such as a dashboard or codebase. This kind of client-facing project mirrors the way data science actually gets done in most workplaces, where a single analyst rarely works in isolation.

Outside of a formal program, you can build similar experience through internships, freelance analytics projects, contributions to open source data projects, or a self-directed portfolio built around publicly available datasets.

Once you have the technical foundation and a portfolio of applied work to point to, you are ready to start applying for entry level positions. Hiring managers for data scientist and data analyst roles typically look for demonstrated proficiency in programming and statistics, familiarity with the tools used in their industry, and evidence, through a capstone, internship, or personal project, that you can apply those skills independently.

An analysis of job postings by 365 Data Science found that 57% of listings seek versatile professionals with a broad range of skills rather than narrow specialists, and that machine learning skills now appear in 77% of postings.

You can read more about the range of data science jobs available across industries including technology, health care, government, and finance.

If you are trying to decide whether a graduate program makes sense for your own path into data science, it can help to look closely at how a program’s curriculum, capstone, and faculty are structured, and to compare that against your current skills and career goals. For a closer look at what the field actually involves day to day, see our breakdown of what data science is, or explore how data science compares to related fields in our guide to data science versus data analytics.

• How Long Does It Take to Become a Data Scientist?

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  The timeline to become a data scientist depends heavily on your starting point. Someone with a quantitative undergraduate degree who pursues a full-time, accelerated master’s program can be ready in as little as a year to sixteen months. Someone starting from a less technical background, or studying part time while working, may take several years to build the same foundation. UNC’s MADS program, for example, can be completed in as few as four terms on an accelerated schedule, with a part-time track available for working professionals who need more flexibility.

• Do I Need a Master’s Degree to Become a Data Scientist?

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  Not strictly. Data scientists typically need at least a bachelor’s degree in mathematics, statistics, computer science, or a closely related field, according to the BLS. However, many employers require or prefer a master’s or doctoral degree, particularly for more advanced or specialized roles.

• Can I Become a Data Scientist Without a Computer Science Degree?

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  Yes. Common undergraduate fields of study for data scientists include mathematics, statistics, computer science, business, and engineering, but plenty of successful data scientists come from other quantitative or research heavy backgrounds and build their technical skills afterward. What matters more than your specific undergraduate major is whether you can demonstrate programming ability, statistical reasoning, and experience applying both to real data.

• Is a Database Scientist the Same as a Data Scientist?

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  Not exactly. A data scientist works across the full data life cycle, from collecting and cleaning data through building models and communicating insights to decision makers. Someone working specifically with database systems, sometimes searched as a database scientist, is typically closer to a database administrator or data engineer, focused more narrowly on storing, structuring, and managing data infrastructure rather than building predictive models or statistical analyses. Many data scientists develop working database skills as part of their broader toolkit, but the roles are distinct.

• What Does a Data Scientist Earn?

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  According to the BLS, the median annual wage for data scientists was $120,230 in May 2025. Pay varies by industry. The highest median wages in BLS data is among data scientists working for publishing, broadcasting, and content providers: $142,240.

• What Kind of Jobs Can I Get With a Data Science Master’s Degree?

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  A master’s in applied data science can prepare you for roles including data scientist, machine learning engineer, data analyst, data engineer, and business intelligence or applications architect, among others. You can explore the range of data science jobs across industries like technology, health care, government, finance, and retail to see where your interests might fit.

Created by the online master’s in applied data science program from the School of Data Science and Society at the University of North Carolina at Chapel Hill.