A bachelor’s degree in computer science, statistics, mathematics, or a related field is the standard entry point for data science careers. The Bureau of Labor Statistics lists a bachelor’s degree as the typical entry-level education, and about 57% of data scientist job postings ask for at least a bachelor’s. That said, the field is broad enough that several degree paths can get you there, and your ideal choice depends on the type of data science work you want to do.
Best Undergraduate Majors
The most common undergraduate fields among working data scientists are computer science, statistics, mathematics, engineering, and business. Each one gives you a different foundation. Computer science builds strong programming and software engineering skills, which matter when you’re writing production-level code or working with large datasets. Statistics and mathematics programs emphasize the theoretical underpinnings of modeling, probability, and inference, which is the core intellectual work of data science. Engineering degrees develop problem-solving discipline and often include enough math and coding to bridge into data roles. Business degrees with a quantitative focus (think business analytics or econometrics) prepare you to frame data problems in terms that organizations actually care about.
A growing number of universities now offer dedicated data science bachelor’s programs. These combine elements of computer science, statistics, and domain knowledge into a single curriculum. If your school offers one, it’s a natural fit. If not, pairing a quantitative major with electives in programming, machine learning, or database management gets you to roughly the same place.
Physics, economics, and other quantitative social sciences also work well. Employers care less about the exact name on your diploma and more about whether you can write code, build statistical models, and communicate results clearly. A physics major who can program in Python and has built predictive models during research has a competitive profile.
When a Master’s Degree Matters
About 28% of data scientist job postings request a master’s degree, and 11% ask for a Ph.D. That means most openings are technically accessible with a bachelor’s, but a graduate degree opens doors, especially at certain companies and for certain roles.
A master’s in data science, statistics, computer science, or applied mathematics typically takes one to two years. It deepens your technical skills in areas like machine learning, natural language processing, and advanced statistical modeling. More importantly, it signals to hiring managers that you can handle complex, ambiguous analytical problems. If you’re switching into data science from an unrelated undergraduate field, a master’s program is one of the fastest ways to build credibility and fill gaps in your technical training.
Ph.D.s become relevant when you want to work on research-heavy teams, develop novel algorithms, or join organizations where deep expertise in a narrow area (computer vision, causal inference, reinforcement learning) is the job description. For most applied data science roles, a Ph.D. isn’t necessary.
Specialized Roles Need Deeper Training
The umbrella of “data science” covers a wide range of jobs, and the education expectations shift as roles get more specialized. Machine learning engineers, for example, skew slightly more toward advanced degrees. About 21% of machine learning engineering postings list a master’s degree, and 7% ask for a Ph.D. or professional degree. These roles require not only statistical knowledge but also software engineering skills to deploy models into production systems.
Some employers also want industry-specific experience layered on top of your degree. A data scientist at a pharmaceutical company may need coursework in biostatistics or clinical trial design. One at a financial firm might need demonstrated understanding of investments, banking, or risk modeling. Your degree gets you in the door, but domain knowledge often determines which doors you can walk through.
Core Skills Your Degree Should Cover
Regardless of your major, hiring managers look for a consistent set of technical skills. Make sure your education covers these areas, whether through your degree program, electives, or self-study:
- Programming: Python is the dominant language in data science, with R as a strong complement for statistical analysis. SQL is essential for querying databases.
- Statistics and probability: Hypothesis testing, regression analysis, Bayesian methods, and experimental design form the analytical backbone of the field.
- Machine learning: Supervised and unsupervised learning, decision trees, neural networks, and model evaluation techniques are expected knowledge for most roles.
- Data wrangling: The ability to clean, transform, and merge messy real-world datasets is where data scientists spend a large share of their time.
- Communication: Translating technical findings into business recommendations through visualization and storytelling separates strong candidates from purely technical ones.
If your degree program doesn’t cover all of these, you can fill gaps with online courses, personal projects, or certifications. What matters is that you can demonstrate competence, ideally through a portfolio of projects that show real analytical work.
Certifications and Online Degrees
Professional certifications can strengthen your profile, particularly if your degree isn’t in a core quantitative field. Credentials like the SAS Certified Data Scientist, AWS machine learning certifications, or certificates from recognized university programs validate specific technical skills. Over 80% of learners who earn SAS certifications report career advancement, which reflects real employer trust in that credential.
Online degrees have also gained significant acceptance. Over 60% of employers now view online degrees from accredited institutions as equally credible to traditional ones, and more than 70% of organizations have hired candidates with online degrees. That said, full parity hasn’t been reached. A small percentage of employers still view online credentials with some skepticism, so choosing an accredited, well-known program matters. Online degrees that include hands-on projects and measurable portfolio work tend to carry the most weight.
Certifications work best as supplements, not replacements, for a degree. They demonstrate that you’ve kept your skills current and can pass rigorous technical assessments, but most employers still use a degree as the baseline screening criterion.
Choosing the Right Path
If you’re an undergraduate deciding on a major, computer science or statistics gives you the broadest foundation with the least need for supplemental training. Mathematics and engineering are close behind. A dedicated data science major is ideal if your school offers a strong one.
If you already have a bachelor’s in a non-quantitative field, a master’s in data science or applied statistics is the most direct route. It typically takes under two years and gives you both the skills and the credential employers look for. Bootcamps and certificate programs can work for career changers who already have strong analytical instincts, but they require you to build a compelling project portfolio to compensate for the lack of a traditional degree.
If you’re aiming for senior or research-oriented positions, plan on a master’s at minimum. For roles developing cutting-edge algorithms at tech companies or research labs, a Ph.D. gives you a meaningful advantage. For applied roles at most companies, a bachelor’s paired with strong skills and a solid portfolio is enough to start building your career.