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In the United States, the typical entry route into data science is a bachelor’s degree in mathematics, statistics, computer science, or a related field, supported by substantial quantitative and computing preparation. Some employers require or prefer a master’s or doctoral degree, and some specialized roles expect industry experience or coursework. The guidance below comes from U.S. Department of Labor sources, principally the Bureau of Labor Statistics (BLS) Occupational Outlook Handbook and its current projections. It does not describe requirements in other countries.
What the typical entry route looks like
The BLS career guidance for data scientists states: “Data scientists typically need at least a bachelor’s degree in mathematics, statistics, computer science, or a related field to enter the occupation.” The Handbook names business and engineering among other common degree fields. That means several academic routes can provide relevant preparation. The source does not say that any single major is mandatory for every employer.
Common degree fields
- Mathematics and statistics, which build the modeling and inference foundation most data science work relies on.
- Computer science, which supplies the programming, data handling, and algorithm skills.
- Business or engineering, which the Handbook lists as common fields; these often pair well with quantitative coursework.
- Other related fields, provided the program includes substantial math, statistics, and computing.
Mathematics and statistics preparation
BLS points to specific areas of school preparation. Students should build a working foundation in linear algebra, calculus, and probability and statistics. At the college level, the Handbook emphasizes computer science alongside mathematics and statistics.
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- Calculus: the rate-of-change and optimization reasoning used when fitting and tuning models.
- Probability and statistics: the basis for collecting samples, testing hypotheses, and quantifying uncertainty in results.
- College-level computer science: the programming and data structure knowledge that lets analysis run on real, messy datasets.
Programming and software skills
BLS states that learners must work with data-oriented programming languages and with statistical, database, and presentation software. The source does not prescribe a particular language or vendor tool, so it does not establish that any one language is universally required. Job postings and employers differ in the tools they name, which is why the Handbook frames these as categories of capability rather than a fixed toolkit.
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The skill set employers look for
BLS’s occupational profile lists six groups of qualities. The table below summarizes what each one covers in the Handbook’s own terms.
| Skill group | What it involves, per BLS |
|---|---|
| Analytical | Researching, examining, and interpreting findings. |
| Computer | Writing code, analyzing data, developing or improving algorithms, and using data visualization tools. |
| Communication | Conveying analysis to technical and nontechnical audiences and making business recommendations. |
| Logical thinking | Understanding and developing statistical models and analyzing data. |
| Math | Using statistical methods to collect and organize data. |
| Problem solving | Addressing data collection and cleaning challenges, and developing statistical models and algorithms. |
How BLS ranks the top skills
In its 2025–35 skills table, BLS identifies mathematics, computers and information technology, and writing and reading as the three most important skills for data scientists. These are BLS skill categories, not an exhaustive employer checklist. Writing and reading appear in the ranking even though the role is often described in technical terms, which is a useful reminder that written explanation of results is part of the job.
A methods note on the skill scores
BLS says its occupational skills data are based on O*NET information, and that it creates scores for 17 skills for occupations with published projections. Readers comparing skill rankings across occupations should use the same source and scoring system for every comparison.
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Experience and employer-specific requirements
Experience requirements vary by employer and specialty. The typical entry education for the occupation is a bachelor’s degree, and the BLS assignment for 2025 lists no related work experience and no typical on-the-job training. That describes the occupation in general. It does not mean that no individual job asks for prior experience.
Industry-specific expectations
BLS notes that some employers require industry-related experience or education. Its example is data scientists seeking roles at asset management companies, who may need finance experience or coursework showing knowledge of investments, banking, or related subjects. The same logic applies to other sectors: domain knowledge can be a deciding factor even when the general degree requirement is met.
When employers ask for graduate education
Some employers require or prefer a master’s or doctoral degree. The BLS source does not establish that every data scientist needs graduate school, and it does not quantify how many jobs require one. A graduate degree is therefore best treated as a route that certain employers or specialties favor, not a universal prerequisite. Before committing to graduate study, check the job postings for the roles and sectors you want.
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U.S. outlook figures
The following figures come from the BLS Occupational Outlook Handbook profile and the 2026 projection release. They describe the occupation as a whole.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match| Measure | Figure | Period or date | Source |
|---|---|---|---|
| Projected employment growth | 35 percent (rounded in the Handbook; 34.6 percent in the BLS skills and projections table) | 2025 to 2035 | BLS 2026 projection release |
| Average annual openings | About 24,800 | 2025 to 2035 average | BLS 2026 projection release |
| Employment | 275,600 data scientist jobs | 2025 | BLS 2026 projection release |
| Median annual wage | $120,230 | May 2025 | BLS Occupational Outlook Handbook profile |
If you cite the headline growth figure, use the rounded 35 percent and keep BLS’s 2025 to 2035 period. The more precise 34.6 percent comes from a different BLS table, so the two should not be mixed in one sentence. Projections describe demand for the occupation; they are not a guarantee of employment for any individual.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Comparing education routes
When weighing a degree program, BLS’s guidance supports four comparison axes. BLS does not rank degree programs or specific providers, so use these axes to evaluate a program’s curriculum rather than its reputation alone.
Quick Recap
- Depth in mathematics and statistics: does the program cover linear algebra, calculus, and probability and statistics in substance?
- Computer science and programming: does it teach data-oriented programming and database work, not just introductory coding?
- Access to domain coursework or experience: are there courses or internships in the sector you want, such as finance for asset management roles?
- Graduate expectations: do the employers or specialties you target require or prefer a master’s or doctoral degree?
Practical steps for getting started
- Map your current coursework against the four areas BLS names: mathematics, statistics, computer science, and communication.
- Choose a programming language and a database tool that your target job postings mention most often, rather than assuming a single standard.
- Practice writing about your analysis for a nontechnical reader, since BLS ranks writing and reading among the top three skills.
- If your target employer is in a specialized sector, add coursework or project work in that domain early.
- Review current job postings for the specific roles you want to confirm whether a graduate degree or experience is expected.
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