Data Science & Analytics Programmes in the USA

Data is now part of almost every major business decision.

Companies use it to understand customers, forecast demand, detect fraud, improve products, optimise supply chains and make decisions about everything from pricing to hiring.

Artificial intelligence has only increased that demand.

But studying Data Science in the USA is not simply about learning Python and building a few machine learning models. The best programmes bring together statistics, mathematics, programming, data management, machine learning and business problem solving.

There is also more than one type of degree.

You could study an MS in Data Science, an MS in Analytics, an MS in Business Analytics, an MS in Statistics with a Data Science track, or a more technical programme such as Computational Data Science.

Those degrees can lead to overlapping careers, but they are not identical.

For an international student, the choice also has financial and immigration implications. Programme length, tuition, STEM classification, internship opportunities and OPT eligibility can all matter when comparing universities.

This guide explains what to look for and highlights some of the US programmes worth researching in 2026.

Why study Data Science or Analytics in the USA?

The US has one of the world's largest technology and data ecosystems, but the career opportunities are not limited to technology companies.

Data professionals work in:

• Technology
• Finance
• Healthcare
• Retail
• Consulting
• Manufacturing
• Insurance
• Marketing
• Logistics
• Government
• Media
• Energy
• Sports

The US Bureau of Labor Statistics projects employment of data scientists to grow by 34.6% between 2025 and 2035, from about 275,600 jobs to 371,000. Around 24,800 openings for data scientists are projected each year during that period. The median annual wage for data scientists was $120,230 in May 2025.

Data Science is not the only relevant occupation.

BLS projects 12% growth for operations research analysts from 2025 to 2035, with around 7,500 openings per year. Their work includes using data and analytical methods to improve business operations, supply chains, pricing and decision making.

The broader US economy also has a large computer and mathematical workforce. In 2025, these occupations accounted for around 5.5 million jobs, with the group projected to grow 7.3% through 2035. The median wage for the group was $109,280 in 2025.

These numbers are useful indicators of the size and direction of the market.

They are not promises of employment or starting salaries for international graduates.

That distinction is important.

Data Science vs Analytics: what is the difference?

The two terms are often used interchangeably.

They should not be.

Data Science

Data Science generally combines:

• Mathematics
• Statistics
• Programming
• Machine learning
• Data engineering
• Data analysis
• Model development
• Data visualisation

A technical Data Science programme can involve building predictive models, working with large datasets and developing systems that use machine learning.

Data Analytics

Analytics is often more focused on using data to answer business or operational questions.

For example:

Why did sales decline?

Which customers are most likely to leave?

Which marketing channel performs best?

How should inventory be allocated?

What factors are affecting profitability?

Analytics programmes can still contain machine learning and programming, but the balance between technical depth and business application varies.

Business Analytics

Business Analytics sits even closer to management and decision making.

A programme may combine:

• Statistics
• Data visualisation
• Business intelligence
• Predictive analytics
• Operations
• Finance
• Marketing
• Strategy
• Programming
• Machine learning

Georgia Tech's Master of Science in Analytics is a good example of this broader approach. Its three tracks are Computational Data Analytics, Business Analytics and Analytical Tools, while the programme itself combines data science and analytics with areas such as predictive modelling, optimisation, big data infrastructure and applied AI.

This is why a student should not select a programme simply because its name contains “Data Analytics”.

Read the curriculum.

What are the main types of Data Science and Analytics degrees?

Here is a practical way to understand the options.

There is no universally superior option.

The right degree depends on the work you want to do afterwards.

1. MS in Data Science

This is usually the most obvious option for students who want a career directly connected to data.

A strong programme should ideally cover:

• Probability
• Statistics
• Programming
• Machine learning
• Data management
• Algorithms
• Data visualisation
• Artificial intelligence
• Data ethics
• Research methods

Depending on the university, students may also study:

• Natural language processing
• Deep learning
• Causal inference
• Time series
• Recommender systems
• Cloud computing
• Big data

The level of mathematics can vary substantially.

That matters for Indian students because some programmes expect a very strong quantitative background.

2. MS in Analytics

Analytics programmes can be particularly useful for students who want to combine technical skills with business problem solving.

Georgia Tech's on campus MS in Analytics is one example.

The programme is structured around three tracks:

• Computational Data Analytics
• Business Analytics
• Analytical Tools

The university describes the programme as interdisciplinary and expects students to have basic knowledge of calculus, probability and statistics, linear algebra and programming.

It is also an official STEM programme, with CIP code 52.1399, and Georgia Tech states that eligible international students on the on campus Atlanta programme can qualify for OPT.

How long does it take?

The on campus programme can be completed as a one year course or extended to approximately 1.5 years.

For the 2026 to 2027 academic year, Georgia Tech estimates tuition and fees for international students at:

• $61,774 for the one year route
• $74,034 for the 1.5 year route

These figures are estimates and can change.

The programme therefore provides a useful example of why students should compare both tuition and course duration.

3. MS in Business Analytics

Business Analytics is worth considering if you want to work with data but do not necessarily want to become a deeply technical machine learning specialist.

A Business Analytics degree can prepare students for problems involving:

• Marketing
• Finance
• Operations
• Supply chain
• Customer analytics
• Business intelligence
• Forecasting
• Pricing
• Strategy
• Consulting

The quantitative level still varies.

Some programmes can be heavily mathematical.

Others place more emphasis on business application and communication.

This distinction is important for students deciding between Data Science and Business Analytics.

Ask yourself:

Do I want to build models, or do I want to use data to help organisations make decisions?

There is plenty of overlap, but the day to day work can be very different.

4. MS in Statistics with a Data Science track

Not every Data Science programme is housed inside a Computer Science department.

Stanford is a good example.

Its Department of Statistics offers an MS in Statistics with a Data Science track. The programme combines mathematical, statistical, computational and programming skills with Data Science coursework. Stanford describes the degree as a terminal programme designed to prepare students for careers in STEM.

The programme is academically demanding.

Students should expect substantial quantitative preparation.

Stanford's current admissions information for external applicants lists a 7 January 2027 deadline for the 2027 to 2028 MS and Data Science applications. The department also states that there is no financial aid for MS students through the Statistics department.

This is a good example of why a university name is not enough.

Stanford offers a Data Science pathway, but it sits inside Statistics and has a different academic character from an applied Business Analytics degree.

5. MS in Computational Data Science

If you want a more technical programme, Computational Data Science is another route.

Carnegie Mellon University's Master of Computational Data Science, or MCDS, focuses on engineering and deploying large scale information systems as well as analysing the data those systems generate.

Students choose from three concentrations:

• Systems
• Analytics
• Human Centered Data Science

The programme includes machine learning, cloud computing, data science, interactive data science and a substantial capstone.

The standard programme is designed to take 16 months, including a summer internship, while an extended option can take around 20 months.

This is a particularly good example of how technical Data Science can become.

You are not simply learning how to analyse spreadsheets.

You are learning how data systems are built, scaled and used.

6. Applied Data Science

Applied Data Science programmes can be useful for students who have a related background but do not necessarily have a traditional Computer Science degree.

USC's MS in Applied Data Science is explicitly designed for students from a range of backgrounds, including engineering, Computer Science, economics, science and mathematics. The university says applicants from other disciplines can also be considered if they demonstrate the required mathematical and scientific preparation.

The programme covers areas such as:

• Python
• Databases
• Big data infrastructure
• Machine learning
• Data mining
• Real world applications

USC requires 32 units for the programme.

For the 2026 to 2027 academic year, Viterbi lists graduate tuition at $2,742 per unit. USC estimates approximately $87,000 or more in total tuition for the 32 unit Data Science programme, before other costs.

USC also states that the GRE is not required for its 2027 applications for the relevant Data Science programmes.

7. Data Science at Columbia University

Columbia's Master of Science in Data Science is another programme worth researching, particularly for students looking for a technically strong programme in New York.

The programme is jointly connected to Columbia's Statistics, Computer Science and Industrial Engineering and Operations Research departments.

Its curriculum includes:

• Algorithms
• Computer systems
• Machine learning
• Probability and statistics
• Exploratory data analysis
• Data visualisation
• Capstone work

Students can also explore electives in areas such as natural language processing, quantitative finance, large language models and specialised applications.

The degree requires 30 credits, including 21 credits of core coursework and at least 9 elective credits. The capstone involves applying Data Science methods to real world problems with faculty or industry partners.

The programme also expects previous quantitative coursework and introductory programming.

Columbia lists calculus, linear algebra and programming experience among its expected preparation.

For international applicants, Columbia currently lists TOEFL, IELTS or PTE Academic where English language testing is required.

This is another example of why students should check prerequisites before applying.

A strong university does not necessarily mean an easy admission process.

8. NYU MS in Data Science

New York University offers an MS in Data Science through its Center for Data Science and the Graduate School of Arts and Science.

The programme requires 36 credits and offers an industry concentration as well as other ways to structure the degree.

The university describes the programme as highly selective and expects students to have substantial mathematical preparation, along with programming and basic Computer Science knowledge.

Relevant academic backgrounds include:

• Mathematics
• Statistics
• Engineering
• Physics
• Computer Science
• Theoretical economics

Applicants with previous experience in machine learning, computational statistics, data mining, large scale scientific computing or operations research may also be well positioned.

The programme includes an industry concentration with practical training and a capstone.

That can make NYU particularly interesting for students who want to combine rigorous study with an industry oriented pathway.

9. University of Michigan Master of Applied Data Science

The University of Michigan's School of Information offers a Master of Applied Data Science.

The programme requires 38 credits, with advanced standing potentially reducing the number of credits required. For 2026 to 2027, the published tuition rate is:

• $1,218 per credit for Michigan residents
• $1,624 per credit for out of state students

The estimated total for 38 credits at the out of state rate is approximately $61,712, before other living and programme costs.

The programme is useful to consider because it provides another example of an applied Data Science pathway housed within an information school rather than a traditional Computer Science department.

That can influence the curriculum and the types of students who are attracted to the programme.

What do you actually study in a Data Science Master's?

The exact curriculum varies, but a serious Data Science programme will usually include several of these areas.

Mathematics and statistics

You may study:

• Probability
• Statistical inference
• Linear algebra
• Regression
• Optimisation
• Experimental design

This is the part many students underestimate.

Machine learning is built on mathematical concepts.

Understanding those concepts makes it much easier to know why a model works, when it fails and how to improve it.

Programming

Python is widely used.

You may also encounter:

• SQL
• R
• Java
• Scala
• C++

The specific language matters less than understanding how to work with data and build reliable programs.

Machine learning

Topics can include:

• Supervised learning
• Unsupervised learning
• Deep learning
• Natural language processing
• Computer vision
• Recommendation systems
• Time series
• Model evaluation
• Data engineering

As datasets grow, knowing how to build the model is only part of the job.

You also need to understand how data is:

• Collected
• Stored
• Cleaned
• Processed
• Accessed
• Scaled
• Governed

That is why stronger programmes often include distributed systems, databases, cloud computing or big data infrastructure.

Communication

A model is not useful if nobody understands what it means.

Data professionals need to communicate findings to engineers, managers, executives, customers and other stakeholders.

That includes visualisation, storytelling and explaining uncertainty.

What should you learn before starting a Data Science Master's?

You do not need to know everything before applying.

But having a solid foundation can make your Master's significantly easier.

Mathematics

Learn:

• Calculus
• Probability
• Statistics
• Linear algebra
• Basic optimisation
• Programming

Be comfortable with:

• Python
• SQL
• Data structures
• Functions
• Object oriented programming
• Git
• Data tools

Learn how to work with:

• Pandas
• NumPy
• Jupyter
• SQL databases
• Visualisation tools
• Basic cloud environments
• Machine learning

Understand:

• Regression
• Classification
• Clustering
• Feature engineering
• Model evaluation
• Overfitting
• Cross validation

You do not need to become an expert before you arrive.

But you should understand the basics.

Do you need a Computer Science degree for Data Science?

No.

But you do need to meet the programme's academic prerequisites.

USC's Applied Data Science programme accepts students from engineering, Computer Science, economics, science and mathematics backgrounds and can consider applicants from other disciplines with demonstrated preparation.

Columbia expects quantitative coursework and introductory programming.

NYU expects substantial mathematical preparation along with programming and basic Computer Science.

Georgia Tech expects calculus, probability or statistics, linear algebra and programming.

So the better question is not:

“Can a non Computer Science student study Data Science?”

It is:

“Does my academic background give me the foundations this particular programme expects?”

Is Data Science a STEM degree in the USA?

Many Data Science and Analytics programmes are STEM designated.

But students should never assume that every programme is STEM simply because it contains the words Data Science or Analytics.

STEM classification matters because qualifying international students can potentially receive an additional 24 month STEM OPT extension after the standard period of post completion OPT.

USCIS states that the STEM extension is available to eligible F 1 students whose qualifying degree is on the DHS STEM Designated Degree Program List and who meet the other requirements. The STEM extension itself can add 24 months to the initial OPT period.

Georgia Tech explicitly identifies its on campus MS in Analytics as an official STEM programme.

USC also identifies its MS in Computer Science with Data Science as eligible for the OPT STEM extension.

The important word is specific.

Before accepting an offer, check the programme's exact classification.

Do not rely on the degree title.

How long can you work in the USA after graduation?

Eligible F 1 students can generally receive up to 12 months of post completion OPT.

Students whose qualifying degree meets the STEM requirements can potentially receive an additional 24 months of STEM OPT, for a possible total of 36 months of post completion work authorisation, subject to the applicable rules.

This can be an important consideration when choosing between programmes.

However, OPT is work authorisation.

It is not a job guarantee.

You still need to compete for employment, find eligible work and maintain the requirements associated with your status.

What careers can you pursue after Data Science or Analytics?

The career options depend heavily on what you study.

Data Science

Data Scientist

Machine Learning Engineer

Applied Scientist

Data Analyst

Analytics Scientist

Analytics

Data Analyst

Business Analyst

Operations Research Analyst

Analytics Consultant

Decision Scientist

Business Analytics

Business Analyst

Business Intelligence Analyst

Marketing Analyst

Product Analyst

Strategy Analyst

Consulting roles

Computational Data Science

Data Scientist

Machine Learning Engineer

Data Engineer

Software Engineer

Systems related roles

The distinction is important because the job title Data Scientist is not the only outcome of a Data Science degree.

Operations research analysts are also increasingly connected to analytics and decision science, with BLS projecting 12% employment growth through 2035.

What salary can you expect after a Data Science degree?

This is where students need to be careful.

You will find plenty of websites claiming that an MS in Data Science automatically leads to a $100,000 or $150,000 salary.

That is not how the labour market works.

The Bureau of Labor Statistics reports a $120,230 median annual wage for data scientists in May 2025. But that figure includes workers with different levels of experience and does not represent a guaranteed starting salary for a new international graduate.

Similarly, operations research analysts had a median wage of $88,940 in May 2025 and a projected growth rate of 12% from 2025 to 2035.

Your actual salary can depend on:

• University
• Location
• Previous experience
• Technical skills
• Internships
• Industry
• Job title
• Employer
• Visa situation
• Negotiation

The degree can help you access opportunities.

It does not determine the outcome by itself.

Internships are extremely important

For students who plan to work in the US after graduation, internships can be one of the most valuable parts of the Master's experience.

Look for programmes that provide access to:

• Career fairs
• Employer events
• Industry projects
• Capstone projects
• Research projects
• Internship opportunities
• Alumni networks
• Technical workshops

CMU's MCDS programme, for example, incorporates a summer internship into its standard 16 month structure.

NYU's Data Science programme includes an industry concentration with practical training and a capstone.

Georgia Tech's Analytics programme also highlights practicum projects, workshops and employer engagement through its career services.

These practical components can be more useful for employability than choosing a programme simply because its ranking is higher.

How much does a Data Science Master's cost in the USA?

There is no single national tuition fee.

The difference between universities can be substantial.

Current 2026 examples demonstrate the range:

There is an important caveat with these numbers.

They are not directly comparable.

Some figures include mandatory fees.

Some are tuition estimates.

Some programmes have different numbers of credits.

Some are online.

The UC Berkeley MIDS example is an online programme and should not be treated as the cost of an on campus F 1 study option. Berkeley currently estimates $82,226 in total tuition and fees for students starting Fall 2026.

Your own budget should include:

• Tuition
• University fees
• Housing
• Food
• Health insurance
• Transportation
• Books and equipment
• Visa related costs
• Travel
• Personal expenses

You also need to consider the cost of living in the city.

New York, San Francisco and Boston are very different financial propositions from many smaller US university towns.

Can Data Science students get scholarships?

Yes, but do not assume that every Master's programme is heavily funded.

Funding can come from:

• Merit scholarships
• Department scholarships
• Research assistantships
• Teaching assistantships
• Fellowships
• External scholarships
• University employment

The availability varies considerably.

Stanford's Statistics department currently states that there is no departmental financial aid for its MS students, which is an important example of why applicants should research funding before applying.

Georgia Tech's Analytics programme also publishes its tuition separately and prospective students should investigate the specific scholarship options available for their programme.

Never build a study abroad budget on the assumption that you will receive a scholarship.

Treat funding as something you apply for rather than something you are guaranteed.

What tests do you need?

There is no single test combination for every Data Science programme.

Depending on the university, you may need:

• English language test
• GRE
• GMAT in some analytics programmes
• Academic transcripts
• Letters of recommendation
• Statement of purpose
• Résumé
• Programming or mathematics prerequisites

The GRE situation varies.

Columbia currently lists GRE General scores as optional for its 2026 applications.

USC states that the GRE is not required for its 2027 Data Science applications.

Georgia Tech's on campus MS in Analytics currently requires GRE or GMAT scores and does not grant GRE or GMAT waivers.

This is exactly why an applicant should not follow a blanket rule such as:

“GRE is not required for Data Science in the USA.”

It depends on the programme.

Do you need IELTS, TOEFL or PTE?

Many US universities accept more than one English language test.

Columbia's current MS Data Science requirements list TOEFL, IELTS or PTE Academic, where applicable.

Other universities may accept different combinations, and some applicants may qualify for exemptions depending on their previous education.

The important point is to check:

• The exact test accepted
• Minimum overall score
• Minimum section scores
• Validity period
• Exemption rules

Do this before booking your exam.

How to choose between Data Science and Business Analytics

This is probably the most useful decision students can make before they start applying.

Data Science may be a better fit if you enjoy:

• Programming
• Mathematics
• Statistics
• Machine learning
• Algorithms
• Data systems
• AI
• Building technical solutions

Business Analytics may be a better fit if you enjoy:

• Business problems
• Decision making
• Consulting
• Finance
• Marketing
• Operations
• Communication
• Data visualisation
• Strategy

There is an overlap.

A Business Analytics graduate can still learn machine learning.

A Data Science graduate can still work in business.

The difference is usually the centre of gravity of the programme.

How to choose the right university

Do not start with the ranking.

Start with the curriculum.

Then compare:

Course content

Does it actually contain the subjects you want?

Mathematics

How quantitative is it?

Programming

How much real programming will you do?

Machine learning

Is it fundamental or superficial?

Data engineering

Does the programme cover databases, cloud or distributed systems?

Industry exposure

Are internships, projects or employer interactions available?

Location

What industries are nearby?

Cost

What will the entire degree cost?

STEM classification

Does the exact programme qualify?

Career support

What support does the school offer?

Admission requirements

Do you actually meet the prerequisites?

A sample shortlist for different students

There is no one perfect list.

Your shortlist should reflect your background.

Computer Science graduate

You could explore:

• MS Data Science
• MS Computer Science with Data Science
• MS Computational Data Science
• MS Machine Learning
• MS Artificial Intelligence
• Engineering graduate

You could explore:

• MS Data Science
• MS Applied Data Science
• MS Analytics
• MS Business Analytics
• MS Operations Research
• Mathematics or Statistics graduate

You could explore:

• MS Statistics with Data Science
• MS Data Science
• MS Machine Learning
• MS Analytics
• Business or Economics graduate

You could explore:

• MS Business Analytics
• MS Analytics
• MS Applied Data Science
• MS Information Systems
• MS Management and Analytics

But you must still check mathematics and programming prerequisites.

For example, Columbia, NYU and Georgia Tech all expect meaningful quantitative preparation for their programmes.

Data Science and AI are becoming increasingly connected

It would be difficult to discuss Data Science in 2026 without discussing AI.

Generative AI, large language models and machine learning are changing the tools used by data professionals.

But the fundamentals have not disappeared.

Students still need:

• Statistics
• Probability
• Data quality
• Programming
• Experimentation
• Model evaluation
• Data engineering
• Critical thinking
• Ethics

In fact, stronger technical foundations may become more valuable as AI tools make it easier to produce code and models.

A student who understands the mathematics and assumptions behind a model can evaluate whether an AI generated solution actually makes sense.

A student who only knows how to use a tool may struggle when the problem changes.

Common mistakes students make

Choosing the degree because Data Science is “in demand”

Demand alone is not enough.

You need to be good at the work.

Ignoring mathematics

Data Science is not simply Python.

Probability, statistics and linear algebra can be central to serious programmes.

Assuming Business Analytics and Data Science are identical

They overlap, but the emphasis can be very different.

Choosing the university only because of ranking

Look at the programme.

A lower ranked university with the right curriculum and a stronger internship pathway may be the better choice.

Assuming every Data Science degree is STEM

Check the exact programme classification.

Assuming OPT guarantees a job

It does not.

Ignoring cost of living

Tuition is only part of the budget.

Starting applications without checking prerequisites

A university may reject an otherwise strong applicant because the required mathematics or programming background is missing.

A practical application strategy for Indian students

A sensible timeline is to begin your university research well before application deadlines.

Start by deciding:

What type of work do I want?

Then ask:

Which degree teaches the skills required for that work?

Next:

Which universities offer that degree?

Then compare:

• Course
• Prerequisites
• GRE
• English test
• Tuition
• Scholarships
• STEM classification
• Internship opportunities
• Location
• Application deadline
• Career support

You should ideally divide your shortlist into:

• Ambitious universities
• Strong match universities
• Safer options

The exact number depends on your profile, but the idea is to avoid building an application strategy entirely around universities where admission is extremely competitive.

Data Science vs Analytics vs Business Analytics

These are general tendencies, not formal definitions.

The actual curriculum of the university should always take priority.

Is a Master's in Data Science worth it in the USA?

For the right student, it can be.

The US data science and analytics ecosystem is large, and government projections show strong growth for data scientists and other analytical occupations.

Universities also offer programmes with very different academic approaches.

You can study statistics at Stanford.

Computational Data Science at Carnegie Mellon.

Data Science at Columbia or NYU.

Analytics at Georgia Tech.

Applied Data Science at USC or Michigan.

That variety is one of the biggest advantages of studying in the USA.

But it comes with a responsibility.

Do not choose a programme simply because the words AI, Data Science or Analytics appear in its title.

Study the curriculum.

Look at the prerequisites.

Understand the total cost.

Check STEM status.

Investigate internships.

And think about the kind of job you actually want.

Final thoughts

Data Science is one of the strongest areas to consider if you genuinely enjoy working with mathematics, programming and data.

Analytics can be a better fit for someone who wants to use data to solve business and operational problems.

Business Analytics can be especially relevant to students interested in consulting, finance, marketing, operations and strategy.

The USA offers all three, along with specialised programmes in Statistics, Machine Learning, Computational Data Science and Applied Data Science.

That is good news for students.

It also makes choosing more complicated.

There is no single “best Data Science university in the USA” for everyone.

The right programme is the one that matches your:

• Academic background
• Quantitative ability
• Technical skills
• Career direction
• Budget
• Preferred location
• Post graduation plans

The US Bureau of Labor Statistics currently projects Data Scientist employment to grow by about 35% between 2025 and 2035, with a median annual wage of $120,230 in May 2025.

Those numbers make Data Science an attractive field.

But the degree itself is only one part of the equation.

The students who are most likely to benefit from a US Data Science or Analytics Master's are the ones who use the programme to build something employers can see:

strong technical foundations + real projects + internship experience + communication skills + a clear career direction.

That is a much better goal than simply graduating with “Data Scientist” written on your résumé.

Frequently Asked Questions

What is the best Data Science course in the USA?

There is no universal best programme. Stanford's Statistics and Data Science track, Columbia's MS in Data Science, NYU's MS in Data Science, Carnegie Mellon's MCDS, Georgia Tech's MS in Analytics, USC's Applied Data Science programme and Michigan's Master of Applied Data Science all have different structures and strengths. Your academic background and career goal should determine the shortlist.

Is Data Science in demand in the USA?

Yes. The US Bureau of Labor Statistics projects approximately 35% employment growth for data scientists between 2025 and 2035, significantly faster than the average for all occupations.

What is the difference between Data Science and Business Analytics?

Data Science generally has more emphasis on programming, statistics, machine learning and technical data work. Business Analytics is more focused on applying data to business decisions, although the two fields overlap significantly.

Can I study Data Science without a Computer Science degree?

Yes, depending on the university. Engineering, mathematics, statistics, economics and other quantitative backgrounds can be suitable for some programmes. Individual universities have different prerequisite requirements.

Do I need GRE for MS Data Science in the USA?

Not everywhere. Columbia currently lists GRE scores as optional for its MS Data Science application, while USC does not require the GRE for its 2027 Data Science applications. Georgia Tech's MS in Analytics currently requires the GRE or GMAT.

Is Data Science a STEM degree?

Many programmes are STEM designated, but students need to verify the specific programme. STEM classification is important for international students considering the 24 month STEM OPT extension.

How long does a Data Science Master's take in the USA?

It varies. Columbia's programme can be completed in two full time semesters, Carnegie Mellon's standard MCDS takes 16 months, and Georgia Tech's MS in Analytics can be completed in one year or extended to around 1.5 years.

How much does an MS in Data Science cost in the USA?

Costs vary substantially. Current examples range from roughly $61,000 in published tuition estimates at programmes such as Georgia Tech and Michigan to around $87,000 or more in estimated tuition at USC's Applied Data Science programme. These figures do not represent a national average and should be checked against the university's latest fee schedule.

What jobs can I get after Data Science?

Potential roles include Data Scientist, Data Analyst, Machine Learning Engineer, Analytics Consultant, Business Intelligence Analyst, Operations Research Analyst, Data Engineer and related positions. Your exact opportunities depend on your skills, experience and specialisation.

Is an MS in Data Science worth it for Indian students?

It can be, particularly for students with a strong quantitative or technical background who are willing to build practical skills and pursue internships. The decision should be based on the total cost, university, curriculum, STEM status, job market and your own career plan.

Can I work in the USA after a Data Science Master's?

Eligible F 1 students can generally receive up to 12 months of post completion OPT. Graduates whose specific degree and circumstances qualify for STEM OPT can potentially receive an additional 24 months, subject to the applicable requirements.

Planning to study Data Science or Analytics in the USA? TIRO Education can help you assess your academic background, compare programmes, check prerequisites and STEM eligibility, and build a university shortlist around your career goals and budget.

Information in this article reflects university, US government and labour market information available in October 2026. Tuition fees, admission requirements, programme structures and immigration policies can change. Students should verify the final requirements directly with the university and relevant US government agencies before applying.