Statistics
the field's direct ancestorProvides the inferential toolkit — hypothesis testing, regression, experimental design — that data science largely inherited from Fisher, Neyman and their successors.
Darker cells mean a higher score for this topic on that metric.
Resists AI38
Pay74
Barrier to entry55
Autonomy60
Demand78
Impact66
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Last reviewed Sources & creditsMedia creditsMethodology
Quick answers
What does a data scientist actually do day to day?
Contrary to the 'building AI models' image, most days split between writing SQL queries and Python code to pull and clean data, running statistical tests or training models, and translating results into a chart or memo a non-technical stakeholder can act on. Practitioner surveys consistently put data cleaning and preparation at roughly half or more of total working time.
Do you need a PhD to become a data scientist?
No. Unlike medicine or law, there is no license or single required degree; a bachelor's or master's in statistics, computer science, mathematics or a related quantitative field is the most common path, and a strong portfolio of real projects often matters more to employers than the exact credential. PhDs are more common in research-heavy or applied machine-learning roles.
Is data science just statistics with a new name?
Not quite. It draws heavily on statistics — Fisher's experimental design, Tukey's exploratory analysis — but adds programming, database engineering and machine learning that classical statistics departments rarely taught. William S. Cleveland proposed the term in 2001 specifically to describe this enlarged, computing-heavy version of the field, built on statistics rather than replacing it.
Is data science at risk from AI?
The routine end is already exposed: AutoML tools can fit and tune standard models, and AI assistants can write SQL queries, first-draft exploratory charts and boilerplate pipeline code faster than a person. What has not been automated is framing the right question, judging whether a pattern is meaningful or spurious, and taking responsibility for a decision built on the result.
How much do data scientists earn?
It varies widely by country and seniority. The US Bureau of Labor Statistics put the median annual wage for the Data Scientists occupation at roughly $108,000 in 2023, while junior analysts often start closer to $70,000-$95,000 and senior or principal data scientists at major technology companies can earn $200,000-$400,000 or more in total compensation with equity.
What's the difference between a data scientist, a data analyst and a machine learning engineer?
A data analyst typically answers defined business questions with existing data and dashboards; a data scientist builds new statistical models and predictive analyses, often from messier data; a machine learning engineer takes a model out of a notebook and makes it run reliably, at scale, in production. The lines blur constantly, and many people move between all three across a career.
There is no license to practice data science, and no professional board decides who may use the title — which is both the field's biggest opportunity and its biggest quality problem, since 'data scientist' has been applied to jobs ranging from advanced statistical research to routine spreadsheet reporting.
In practice a fairly consistent pipeline has formed anyway: a quantitative undergraduate degree, increasingly followed by a master's, a portfolio of real projects, and a technical interview built around SQL, statistics and a take-home case study that functions as the field's real, informal gatekeeping exam.
The route in
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1
Quantitative undergraduate foundations
3–4 yrsA degree in statistics, computer science, mathematics, economics or a related quantitative field, ideally with coursework or a project applying it to real data.
The filterUniversity admission and completing calculus- and probability-based coursework.
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2
Building a project portfolio
1–2 yrsInternships, a Kaggle competition, a research assistantship or a self-directed project with a public GitHub repository — the evidence employers actually look for beyond a transcript.
The filterLanding an internship or independently shipping a project worth showing.
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3
Master's degree (increasingly expected)
1–2 yrsA dedicated master's in data science, statistics or a related field, now common enough at large employers that it functions as a soft requirement even without a formal mandate.
The filterGraduate admission, typically requiring strong quantitative undergraduate grades.
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4
First data science or analyst role
entryEarly roles lean heavily toward data cleaning, dashboarding and well-scoped analysis rather than open-ended modeling, building the SQL and business-context fluency later work depends on.
The filterA technical interview built around SQL, statistics and a take-home case study.
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5
Specializing
3–5 yrsMost data scientists drift toward one lane — machine learning modeling, causal inference and experimentation, or analytics engineering — as the generalist version of the title becomes harder to sustain at scale.
The filterA track record of shipped analyses or models with measurable business impact.
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6
Senior, staff or lead data scientist
5+ yrsSets the analytical agenda for a team or product area, mentors juniors, and is judged on the decisions a body of work influenced rather than any single model.
The filterInternal promotion committees, weighing demonstrated influence over technical output alone.
Master's in Data Science (US)
$30k–$80k
US master's programs in data science or statistics typically run $30,000 to $80,000 in total tuition depending on the university, though some public and online programs, such as Georgia Tech's online analytics master's, cost considerably less. A bootcamp alternative typically runs $10,000–$20,000 over three to six months. Outside the US, many European master's programs charge little or no tuition for domestic or EU students, though living costs and, for non-EU students, higher international fees still apply.
What to study
Computer Science
for the engineering half of the jobDatabases, algorithms and enough software engineering to write production-quality code, not just a one-off analysis script, increasingly separate strong candidates from weak ones.
Mathematics or Applied Mathematics
for the theory under the modelsLinear algebra, probability and optimization are the language machine-learning methods are actually written in, useful well beyond fitting models with default settings.
Economics
an unusually common feederEconometrics training in causal inference and observational-data analysis translates directly, and many working data scientists, especially in finance and tech, hold economics degrees.
Physics
for quantitative problem-solving habitsPhysicists bring strong numerical intuition and comfort with messy, real-world measurement error, and move into data science more easily than the degree title might suggest.
Where it is taught best
Stanford University
United StatesA statistics department with deep ties to Silicon Valley hiring, and home to some of the most widely used introductory statistical-learning textbooks and online courses.
University of California, Berkeley
United StatesIts School of Information's Master of Information and Data Science program and the influential 'Foundations of Data Science' undergraduate course helped define a dedicated data-science curriculum.
ETH Zurich
SwitzerlandA strong statistics, machine-learning and data-engineering program feeding Switzerland's dense concentration of pharmaceutical, finance and technology employers.
University of Cambridge
United KingdomDeep statistics and machine-learning research, with graduates regularly recruited into London's finance- and technology-driven data teams.
National University of Singapore
SingaporeA leading regional data-science and analytics program feeding Southeast Asia's fast-growing banking and technology sectors.
Indian Institute of Technology Bombay
IndiaOne of India's most selective engineering institutions, whose graduates feed both domestic technology firms and global companies' large India-based data teams.
Tsinghua University
ChinaA top computer-science and statistics program supplying China's largest technology companies with data and machine-learning talent.
Technical University of Munich
GermanyA leading German program combining data engineering, statistics and machine learning, closely tied to the country's automotive and industrial data science hiring.
Licences and exams
Master's or PhD in Data Science, Statistics or a related field
GlobalNot legally required, but the closest thing the field has to a default credential — increasingly listed as a preference or requirement in job postings at larger employers.
Certified Analytics Professional (CAP)
Global (issued by INFORMS, US-based)A vendor-neutral analytics certification requiring an exam plus verified work experience, issued by the Institute for Operations Research and the Management Sciences since 2013.
Microsoft Certified: Azure Data Scientist Associate
GlobalA cloud-vendor certification (exam DP-100) demonstrating the ability to build and deploy machine-learning models on Microsoft's Azure platform, commonly listed in job postings that use Azure.
Google Data Analytics Professional Certificate
GlobalA widely taken entry-level credential delivered through Coursera, often used by career-changers to demonstrate foundational SQL, spreadsheet and visualization skills without a full degree.
The other way in
Coding and data bootcamps
Intensive multi-month programs teach Python, SQL and applied statistics to career-changers, substituting a fast, project-heavy curriculum and a portfolio for a full degree — a route more accepted at smaller companies and startups than at large research-heavy employers.
Kaggle and open-source portfolio route
A smaller number of self-taught practitioners build a public track record through Kaggle competition rankings or widely used open-source data tools, occasionally reaching interviews at companies that weight demonstrated ability over formal credentials.
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