Georgetown University MS Data Science and Analytics

What is the Georgetown University MS Data Science and Analytics?

The Georgetown University MS Data Science and Analytics is a 30-credit full-time master’s route based in Washington, DC. The programme runs for about 24 months and centres on data science, statistical modelling and computational analytics. This page covers the full-time route only, including its current cost basis, application evidence and curriculum.

For an Indian applicant, the decision has four independent tests: preparation in python and sql, a workable Fall 2027 application timeline, funding for the complete INR 1.47 crore planning case, and a clear use for team-based data science capstone. Clearing one test does not compensate for missing another.

How much does the Georgetown University MS Data Science and Analytics cost for an Indian student?

The full planning case is USD 152,605, about INR 1.47 crore. It combines the latest published or schedule-derived tuition and fees, Georgetown’s graduate living categories for 24 months, the application fee, SEVIS and the F-1 visa fee, before flights and a housing deposit.

The table treats the SEVIS and visa charges as separate payments. It does not add a second visa-maintenance total because the complete living plan already covers the study period. Converted at USD 1 = INR 96.33, derived from ECB reference rates dated 1 October 2026.

Georgetown requires international graduate students to submit a financial guarantee before the university can issue an I-20.Georgetown international graduate admission

The living line starts from Georgetown’s published annual graduate categories. Those figures include housing and meals, books and supplies, transport and personal costs. The 24-month multiplication is a planning assumption, so a shorter completion pattern or different housing choice changes the total.

The tuition line is a transparent calculation of 30 credits from the current school-specific 2026-27 tuition basis. It is not an invoice. Programme fees, term load and future board decisions can change what Georgetown bills.

Flights, exchange spreads, a refundable housing deposit and personal contingency remain outside the table. They vary too much to attach one official amount to every applicant, but they still need cash in the funding plan before departure.

A scholarship should reduce the plan only after it appears in a written award. Campus employment is limited, competitive and dependent on authorisation, so it is not a sound way to close a known tuition gap at the application stage.

The estimate isn’t an invoice, doesn’t cap individual spending and can’t replace the payment terms in an Georgetown offer. It won’t predict actual housing costs and shouldn’t be treated as a scholarship assumption. It is a common comparison case that keeps the main assumptions visible before an applicant commits.

The institution identity is independently recorded by the Research Organization Registry. That confirms the provider behind the bill, while the offer and student account remain the controlling sources for the amount and due dates.

Can an Indian applicant meet Georgetown University MS Data Science and Analytics entry rules?

The official admissions route requires a recognised bachelor’s-equivalent record and programme-specific evidence. For this degree, the useful file demonstrates calculus, statistics and programming preparation, with the summer connection where required. The degree title alone is not evidence of subject depth, and a published minimum is not an admission forecast.

The transcript match is strongest when earlier study names the methods behind Python and SQL and statistical modelling. Where a course title is broad, a syllabus or official description can show the topics and assessed depth.

Preparation for Probability and Statistics for Data Science should already be visible through Python and SQL. The master’s develops that base; it is not a substitute for every prerequisite the school expects at entry.

Evidence for statistical modelling can come from a substantial university project, laboratory, dissertation or professional assignment. The useful evidence states the problem, method, result and limitations.

The statement has a specific job: connect prior preparation to machine learning and explain why Georgetown’s data visualisation route is the next academic step. Repeating the programme description does not establish that progression.

Academic records, literal English translations where required, accepted language evidence and referee responses remain independent completion checks. A strong academic match can still be incomplete if one is missing.

The published No programme minimum published GPA is an eligibility floor, not an offer prediction. Subject depth, the statement, recommendations and the complete application can still distinguish applicants.

MS Data Science & Analytics uses Python And Sql as a 3-credit checkpoint. Readiness for Python and SQL is visible before Python And Sql, not repaired automatically by enrolment. A transcript item, syllabus topic and assessed result together make that preparation easier to recognise.

MS Data Science & Analytics uses Statistical Modelling as a 3-credit checkpoint. Readiness for statistical modelling is visible before Statistical Modelling, not repaired automatically by enrolment. A transcript item, syllabus topic and assessed result together make that preparation easier to recognise.

MS Data Science & Analytics uses Machine Learning as a 3-credit checkpoint. Readiness for machine learning is visible before Machine Learning, not repaired automatically by enrolment. A transcript item, syllabus topic and assessed result together make that preparation easier to recognise.

MS Data Science & Analytics uses Data Visualisation as a 3-credit checkpoint. Readiness for data visualisation is visible before Data Visualisation, not repaired automatically by enrolment. A transcript item, syllabus topic and assessed result together make that preparation easier to recognise.

MS Data Science & Analytics uses Python And Sql as a 3-credit checkpoint. Readiness for Python and SQL is visible before Python And Sql, not repaired automatically by enrolment. A transcript item, syllabus topic and assessed result together make that preparation easier to recognise.

Admission preparation for Probability and Statistics for Data Science begins with an earlier assessed use of python and sql. The applicant should identify the question, method and result in the transcript or supporting syllabus; the 3-credit component then deepens that evidence for data scientist work.

Admission preparation for Data Structures and Algorithms begins with an earlier assessed use of statistical modelling. The applicant should identify the question, method and result in the transcript or supporting syllabus; the 3-credit component then deepens that evidence for machine-learning analyst work.

Admission preparation for Machine Learning begins with an earlier assessed use of machine learning. The applicant should identify the question, method and result in the transcript or supporting syllabus; the 3-credit component then deepens that evidence for analytics engineer work.

Admission preparation for Data Science Capstone begins with an earlier assessed use of data visualisation. The applicant should identify the question, method and result in the transcript or supporting syllabus; the 3-credit component then deepens that evidence for data consultant work.

For Georgetown University MS Data Science and Analytics, the admission evidence has to support Python and SQL and statistical modelling before Probability and Statistics for Data Science begins. The College of Arts & Sciences wording makes this a course-match question, not a branding question about the applicant’s previous university.

A coherent preparation map for Georgetown University MS Data Science and Analytics moves from prior Python and SQL into Probability and Statistics for Data Science, then uses Data Structures and Algorithms to extend statistical modelling. Readiness for Machine Learning is shown through machine learning, while Data Science Capstone makes the data visualisation expectation visible. This sequence is specific to data science, statistical modelling and computational analytics; a generic claim of quantitative or professional ability would not establish all four links.

Probability and Statistics for Data Science makes python and sql the first transcript audit for MS Data Science & Analytics. An applicant should point to assessed work that used that capability, name the method and explain the result. A broad claim of interest in data science, statistical modelling and computational analytics does not establish the same preparation.

The second audit links Data Structures and Algorithms to statistical modelling. Evidence may come from a university course, research project or substantial professional assignment, but the file should state what was personally completed. That distinction is especially important for a future machine-learning analyst.

Machine Learning introduces the machine learning test. Where the transcript uses a different course name, a syllabus or project description can make the match visible. Without that detail, Georgetown reviewers may see a related degree without seeing the preparation this Master of Science in Data Science and Analytics actually uses.

Probability and Statistics for Data Science isn’t interchangeable with a broad survey, and MS Data Science & Analytics doesn’t waive python and sql preparation. Data Structures and Algorithms can’t repair a missing foundation after admission; machine-learning analyst experience won’t automatically count as academic evidence. Applicants shouldn’t assume Machine Learning is remedial, couldn’t rely on team-based data science capstone to teach every prerequisite, and wouldn’t strengthen the file by hiding a gap. A Master of Science in Data Science and Analytics application mustn’t leave that preparation unexplained.

Three checks that can block a MS Data Science & Analytics application

1. A GPA floor is not an admission promise

   The No programme minimum published figure is a minimum under Georgetown’s wording. Competitive review can still distinguish applicants through subject depth, statement quality, recommendations or quantitative readiness.

2. The degree title cannot prove prerequisites

   A broad Indian degree title may hide whether Python and SQL and statistical modelling were studied at the expected depth. Syllabi or official course descriptions can make that preparation visible when transcript titles do not.

3. English rules can be Georgetown course-specific

   The working reference is TOEFL or IELTS when Georgetown requires proof. Georgetown says individual programmes may set higher rules or restrict test formats, so the exact degree page controls.

How should an Indian applicant apply for Georgetown University MS Data Science and Analytics?

Use the programme application route and treat Fall 2027 closing date must be confirmed on the official programme page as the current timing control. Academic records, recommendations, written statements, English evidence and any programme-specific test must all reach the correct Georgetown office. An offer comes before funding evidence, I-20 processing, SEVIS payment and the F-1 interview.

Select the exact in-person degree, upload the academic and programme documents, pay the international application fee, monitor My Georgetown, clear every offer condition, submit the financial guarantee and then complete the I-20 and F-1 steps.

The allowances are preparation estimates, not deadlines or processing times published by Georgetown University. Start dates remain unconfirmed.

Build the file around Python And Sql and Statistical Modelling, not around a generic Georgetown statement. Use earlier coursework or employment to show readiness, then identify the gap that the published curriculum is meant to close.

The official status is Fall 2027 closing date must be confirmed on the official programme page. A priority date and a final closure serve different purposes, and a published academic deadline is separate from the time needed for the I-20 and visa process.

After admission, read the offer and My Georgetown task list line by line. Financial guarantee, final transcripts, immunisation records and I-20 processing can each continue after the academic decision and can each delay enrolment if ignored.

For F-1 planning, check the SEVIS I-901 step and the US visa fee page directly. Fee payment does not guarantee a visa, and a programme offer does not replace consular review.

What jobs can follow Georgetown University MS Data Science and Analytics?

The curriculum supports directions such as data scientist, machine-learning analyst, analytics engineer and data consultant. Georgetown marks the degree STEM-OPT eligible, but the OPT framework is work authorisation rather than a placement or sponsorship guarantee. Each direction still needs role-specific, inspectable work samples.

These are curriculum-linked directions for MS Data Science & Analytics, not a measured probability of employment, salary, sponsorship or promotion. No guaranteed programme outcome is claimed.

For a data scientist application, work from Probability and Statistics for Data Science is most useful when it preserves the brief, inputs, method, decisions and limitations. That record is stronger than a transcript line alone.

The route towards machine-learning analyst work draws more directly on Data Structures and Algorithms and statistical modelling. A credible sample shows the trade-off made and the effect of uncertainty, rather than listing software or techniques without context.

A candidate targeting analytics engineer can use Machine Learning to connect machine learning with one inspectable result. The role is a curriculum-linked direction, not a measured placement probability.

The data consultant direction depends on how Data Science Capstone and the culminating work are chosen. Elective access, prior experience and the quality of the retained work sample all affect whether that direction is plausible.

The degree carries no published placement rate, salary promise or employer-sponsorship guarantee. Any OPT period is work authorisation subject to current rules; it does not require an employer to hire the graduate.

This research pass did not locate three independent, exact-programme student accounts. Workload, elective access and team-project experience therefore remain questions rather than claims borrowed from unrelated Georgetown degrees.

Inside MS Data Science & Analytics, Statistical Modelling carries 3 published credits. Its value for a data scientist direction depends on making statistical modelling inspectable. A retained question, method, result and limitation can show what Statistical Modelling added without implying a promised hiring result.

Inside MS Data Science & Analytics, Machine Learning carries 3 published credits. Its value for a machine-learning analyst direction depends on making machine learning inspectable. A retained question, method, result and limitation can show what Machine Learning added without implying a promised hiring result.

Inside MS Data Science & Analytics, Data Visualisation carries 3 published credits. Its value for a analytics engineer direction depends on making data visualisation inspectable. A retained question, method, result and limitation can show what Data Visualisation added without implying a promised hiring result.

Inside MS Data Science & Analytics, Python And Sql carries 3 published credits. Its value for a data consultant direction depends on making Python and SQL inspectable. A retained question, method, result and limitation can show what Python And Sql added without implying a promised hiring result.

Inside MS Data Science & Analytics, Statistical Modelling carries 3 published credits. Its value for a data scientist direction depends on making statistical modelling inspectable. A retained question, method, result and limitation can show what Statistical Modelling added without implying a promised hiring result.

The data scientist case inside Georgetown University MS Data Science and Analytics turns on what Probability and Statistics for Data Science adds to Python and SQL. A retained output from Probability and Statistics for Data Science can show how evidence was selected and how uncertainty changed the decision. That is the programme-specific value of the 3-credit Probability and Statistics for Data Science component.

The machine-learning analyst case inside Georgetown University MS Data Science and Analytics turns on what Data Structures and Algorithms adds to statistical modelling. A retained output from Data Structures and Algorithms can show how evidence was selected and how uncertainty changed the decision. That is the programme-specific value of the 3-credit Data Structures and Algorithms component.

The analytics engineer case inside Georgetown University MS Data Science and Analytics turns on what Machine Learning adds to machine learning. A retained output from Machine Learning can show how evidence was selected and how uncertainty changed the decision. That is the programme-specific value of the 3-credit Machine Learning component.

The data consultant case inside Georgetown University MS Data Science and Analytics turns on what Data Science Capstone adds to data visualisation. A retained output from Data Science Capstone can show how evidence was selected and how uncertainty changed the decision. That is the programme-specific value of the 3-credit Data Science Capstone component.

The published culminating work is where Georgetown University MS Data Science and Analytics can join machine learning with analytics engineer evidence. Its value depends on the retained method, result and explanation; a capstone label does not create a analytics engineer employment outcome on its own.

The role map for Georgetown University MS Data Science and Analytics also has limits. Probability and Statistics for Data Science may support data scientist evidence, but it does not replace the different proof expected for machine-learning analyst. Machine Learning may help a analytics engineer portfolio, while Data Science Capstone can be more relevant to data consultant. Those distinctions keep data science, statistical modelling and computational analytics outcomes tied to assessed work instead of presenting every graduate as suitable for every adjacent role.

Data Scientist is the clearest direction when Probability and Statistics for Data Science produces evidence in python and sql. The useful artefact could be an analysis, model, policy brief or implemented decision, depending on the assessment. The programme does not turn that artefact into a hiring promise.

For machine-learning analyst work, Data Structures and Algorithms should deepen statistical modelling beyond the applicant’s previous level. A retained output needs enough context for an employer to understand the question, the student’s contribution and the limits of the result.

The analytics engineer direction depends more heavily on Machine Learning. Its connection to machine learning is academically credible, but it remains a capability signal rather than evidence of a Georgetown placement rate, salary level or sponsorship decision.

Team-based data science capstone is the strongest connection toward data consultant work when it applies data visualisation to a defined problem. The student should preserve the brief, method, result and caveats where confidentiality allows; otherwise the final requirement may be difficult to demonstrate after graduation.

Who is Georgetown University MS Data Science and Analytics for, and who should avoid it?

A strong fit already has preparation in Python and SQL and statistical modelling, wants assessed evidence in machine learning and can fund INR 1.47 crore without promised employment. A weak fit needs foundational repair, wants a different academic centre or depends on uncertain US earnings to make the course affordable.

The positive academic case begins with Probability and Statistics for Data Science. A suitable entrant recognises its foundation from previous work but still needs graduate depth in data science, statistical modelling and computational analytics.

The next fit question is whether Data Structures and Algorithms adds a method or system the applicant cannot already demonstrate. Heavy overlap weakens the value case unless the elective structure can redirect those credits.

For someone pursuing data scientist work, the degree is more useful when machine learning produces an artefact that can be inspected and explained. A purely aspirational role title is not evidence of fit.

Someone aiming for machine-learning analyst work must also value data visualisation. If that part of the degree has little use in the intended role, a differently structured master’s may be a better match.

The named modules Machine Learning and Data Science Capstone create a real choice about depth. A coherent selection builds one capability story; a scattered selection can satisfy credits without producing a clear portfolio.

Affordability is separate from academic fit. The INR 1.47 crore plan excludes flights and a housing deposit, and the rupee amount moves with the exchange rate even when Georgetown leaves a dollar charge unchanged.

This page assumes full-time campus study at Washington, DC. Online or part-time enrolment would change the attendance, living-cost, visa and work-authorisation assumptions and is outside this comparison.

The weakest fit is an applicant who needs extensive prerequisite repair or guaranteed US employment to make the degree viable. Graduate tuition is an expensive way to repeat foundations, and no placement guarantee is published.

An elective-fit reading of Georgetown University MS Data Science and Analytics asks whether Python and SQL closes a prior gap for data scientist work. This 3-credit component remains academic until the candidate can explain its assessment, result and limitations. Credit weight alone cannot predict hiring.

An elective-fit reading of Georgetown University MS Data Science and Analytics asks whether statistical modelling closes a prior gap for machine-learning analyst work. This 3-credit component remains academic until the candidate can explain its assessment, result and limitations. Credit weight alone cannot predict hiring.

An elective-fit reading of Georgetown University MS Data Science and Analytics asks whether machine learning closes a prior gap for analytics engineer work. This 3-credit component remains academic until the candidate can explain its assessment, result and limitations. Credit weight alone cannot predict hiring.

An elective-fit reading of Georgetown University MS Data Science and Analytics asks whether data visualisation closes a prior gap for data consultant work. This 3-credit component remains academic until the candidate can explain its assessment, result and limitations. Credit weight alone cannot predict hiring.

A candidate choosing Georgetown University MS Data Science and Analytics for data consultant work should be able to explain why data visualisation matters more than another specialisation. That explanation links data science, statistical modelling and computational analytics to a real gap in the candidate’s existing record instead of relying on Georgetown’s general reputation.

Four checkpoints distinguish Georgetown University MS Data Science and Analytics: Probability and Statistics for Data Science tests Python and SQL for data scientist; Data Structures and Algorithms tests statistical modelling for machine-learning analyst; Machine Learning tests machine learning for analytics engineer; and Data Science Capstone tests data visualisation for data consultant. A candidate need not pursue all four roles, but the chosen data science, statistical modelling and computational analytics route should make one of those links materially stronger than it was before the 30-credit degree.

For data scientist work, python and sql matters only when the student can retain and explain a method, result and limitation from Probability and Statistics for Data Science. The course title and Georgetown name cannot substitute for that evidence.

Data Structures and Algorithms creates a different checkpoint for the machine-learning analyst direction. It adds value when statistical modelling is missing from the applicant’s current record; it adds less when the new study merely repeats prior assessed work.

The decisive comparison is between Machine Learning and team-based data science capstone. The first builds machine learning, while the second should produce inspectable evidence for analytics engineer work. That sequence is more useful than a broad prestige argument.

An applicant targeting data consultant should ask whether data visualisation is explicitly assessed. If the answer is no, an attractive elective list cannot repair the mismatch. Another Georgetown master’s may then offer a better use of time and funds.

Probability and Statistics for Data Science in MS Data Science & Analytics tests python and sql; data scientist evidence from Probability and Statistics for Data Science must name an assessed result. Probability and Statistics for Data Science in Master of Science in Data Science and Analytics then changes the data scientist comparison by adding python and sql. For MS Data Science & Analytics, that Probability and Statistics for Data Science to Probability and Statistics for Data Science link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Data Structures and Algorithms in MS Data Science & Analytics tests python and sql; machine-learning analyst evidence from Data Structures and Algorithms must name an assessed result. Probability and Statistics for Data Science in Master of Science in Data Science and Analytics then changes the machine-learning analyst comparison by adding python and sql. For MS Data Science & Analytics, that Data Structures and Algorithms to Probability and Statistics for Data Science link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Machine Learning in MS Data Science & Analytics tests python and sql; analytics engineer evidence from Machine Learning must name an assessed result. Probability and Statistics for Data Science in Master of Science in Data Science and Analytics then changes the analytics engineer comparison by adding python and sql. For MS Data Science & Analytics, that Machine Learning to Probability and Statistics for Data Science link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Data Science Capstone in MS Data Science & Analytics tests python and sql; data consultant evidence from Data Science Capstone must name an assessed result. Probability and Statistics for Data Science in Master of Science in Data Science and Analytics then changes the data consultant comparison by adding python and sql. For MS Data Science & Analytics, that Data Science Capstone to Probability and Statistics for Data Science link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Probability and Statistics for Data Science in MS Data Science & Analytics tests python and sql; data scientist evidence from Probability and Statistics for Data Science must name an assessed result. Data Structures and Algorithms in Master of Science in Data Science and Analytics then changes the data scientist comparison by adding python and sql. For MS Data Science & Analytics, that Probability and Statistics for Data Science to Data Structures and Algorithms link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Data Structures and Algorithms in MS Data Science & Analytics tests python and sql; machine-learning analyst evidence from Data Structures and Algorithms must name an assessed result. Data Structures and Algorithms in Master of Science in Data Science and Analytics then changes the machine-learning analyst comparison by adding python and sql. For MS Data Science & Analytics, that Data Structures and Algorithms to Data Structures and Algorithms link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Machine Learning in MS Data Science & Analytics tests python and sql; analytics engineer evidence from Machine Learning must name an assessed result. Data Structures and Algorithms in Master of Science in Data Science and Analytics then changes the analytics engineer comparison by adding python and sql. For MS Data Science & Analytics, that Machine Learning to Data Structures and Algorithms link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Data Science Capstone in MS Data Science & Analytics tests python and sql; data consultant evidence from Data Science Capstone must name an assessed result. Data Structures and Algorithms in Master of Science in Data Science and Analytics then changes the data consultant comparison by adding python and sql. For MS Data Science & Analytics, that Data Science Capstone to Data Structures and Algorithms link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Probability and Statistics for Data Science in MS Data Science & Analytics tests python and sql; machine-learning analyst evidence from Probability and Statistics for Data Science must name an assessed result. Machine Learning in Master of Science in Data Science and Analytics then changes the machine-learning analyst comparison by adding python and sql. For MS Data Science & Analytics, that Probability and Statistics for Data Science to Machine Learning link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Data Structures and Algorithms in MS Data Science & Analytics tests python and sql; analytics engineer evidence from Data Structures and Algorithms must name an assessed result. Machine Learning in Master of Science in Data Science and Analytics then changes the analytics engineer comparison by adding python and sql. For MS Data Science & Analytics, that Data Structures and Algorithms to Machine Learning link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Machine Learning in MS Data Science & Analytics tests python and sql; data consultant evidence from Machine Learning must name an assessed result. Machine Learning in Master of Science in Data Science and Analytics then changes the data consultant comparison by adding python and sql. For MS Data Science & Analytics, that Machine Learning to Machine Learning link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Data Science Capstone in MS Data Science & Analytics tests python and sql; data scientist evidence from Data Science Capstone must name an assessed result. Machine Learning in Master of Science in Data Science and Analytics then changes the data scientist comparison by adding python and sql. For MS Data Science & Analytics, that Data Science Capstone to Machine Learning link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Probability and Statistics for Data Science in MS Data Science & Analytics tests python and sql; machine-learning analyst evidence from Probability and Statistics for Data Science must name an assessed result. Data Science Capstone in Master of Science in Data Science and Analytics then changes the machine-learning analyst comparison by adding python and sql. For MS Data Science & Analytics, that Probability and Statistics for Data Science to Data Science Capstone link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Data Structures and Algorithms in MS Data Science & Analytics tests python and sql; analytics engineer evidence from Data Structures and Algorithms must name an assessed result. Data Science Capstone in Master of Science in Data Science and Analytics then changes the analytics engineer comparison by adding python and sql. For MS Data Science & Analytics, that Data Structures and Algorithms to Data Science Capstone link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Machine Learning in MS Data Science & Analytics tests python and sql; data consultant evidence from Machine Learning must name an assessed result. Data Science Capstone in Master of Science in Data Science and Analytics then changes the data consultant comparison by adding python and sql. For MS Data Science & Analytics, that Machine Learning to Data Science Capstone link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Data Science Capstone in MS Data Science & Analytics tests python and sql; data scientist evidence from Data Science Capstone must name an assessed result. Data Science Capstone in Master of Science in Data Science and Analytics then changes the data scientist comparison by adding python and sql. For MS Data Science & Analytics, that Data Science Capstone to Data Science Capstone link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing python and sql record.

Probability and Statistics for Data Science in MS Data Science & Analytics tests statistical modelling; analytics engineer evidence from Probability and Statistics for Data Science must name an assessed result. Probability and Statistics for Data Science in Master of Science in Data Science and Analytics then changes the analytics engineer comparison by adding statistical modelling. For MS Data Science & Analytics, that Probability and Statistics for Data Science to Probability and Statistics for Data Science link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing statistical modelling record.

Data Structures and Algorithms in MS Data Science & Analytics tests statistical modelling; data consultant evidence from Data Structures and Algorithms must name an assessed result. Probability and Statistics for Data Science in Master of Science in Data Science and Analytics then changes the data consultant comparison by adding statistical modelling. For MS Data Science & Analytics, that Data Structures and Algorithms to Probability and Statistics for Data Science link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing statistical modelling record.

Machine Learning in MS Data Science & Analytics tests statistical modelling; data scientist evidence from Machine Learning must name an assessed result. Probability and Statistics for Data Science in Master of Science in Data Science and Analytics then changes the data scientist comparison by adding statistical modelling. For MS Data Science & Analytics, that Machine Learning to Probability and Statistics for Data Science link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing statistical modelling record.

Data Science Capstone in MS Data Science & Analytics tests statistical modelling; machine-learning analyst evidence from Data Science Capstone must name an assessed result. Probability and Statistics for Data Science in Master of Science in Data Science and Analytics then changes the machine-learning analyst comparison by adding statistical modelling. For MS Data Science & Analytics, that Data Science Capstone to Probability and Statistics for Data Science link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing statistical modelling record.

Probability and Statistics for Data Science in MS Data Science & Analytics tests statistical modelling; analytics engineer evidence from Probability and Statistics for Data Science must name an assessed result. Data Structures and Algorithms in Master of Science in Data Science and Analytics then changes the analytics engineer comparison by adding statistical modelling. For MS Data Science & Analytics, that Probability and Statistics for Data Science to Data Structures and Algorithms link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing statistical modelling record.

Data Structures and Algorithms in MS Data Science & Analytics tests statistical modelling; data consultant evidence from Data Structures and Algorithms must name an assessed result. Data Structures and Algorithms in Master of Science in Data Science and Analytics then changes the data consultant comparison by adding statistical modelling. For MS Data Science & Analytics, that Data Structures and Algorithms to Data Structures and Algorithms link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing statistical modelling record.

Machine Learning in MS Data Science & Analytics tests statistical modelling; data scientist evidence from Machine Learning must name an assessed result. Data Structures and Algorithms in Master of Science in Data Science and Analytics then changes the data scientist comparison by adding statistical modelling. For MS Data Science & Analytics, that Machine Learning to Data Structures and Algorithms link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing statistical modelling record.

Data Science Capstone in MS Data Science & Analytics tests statistical modelling; machine-learning analyst evidence from Data Science Capstone must name an assessed result. Data Structures and Algorithms in Master of Science in Data Science and Analytics then changes the machine-learning analyst comparison by adding statistical modelling. For MS Data Science & Analytics, that Data Science Capstone to Data Structures and Algorithms link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing statistical modelling record.

Probability and Statistics for Data Science in MS Data Science & Analytics tests statistical modelling; data consultant evidence from Probability and Statistics for Data Science must name an assessed result. Machine Learning in Master of Science in Data Science and Analytics then changes the data consultant comparison by adding statistical modelling. For MS Data Science & Analytics, that Probability and Statistics for Data Science to Machine Learning link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing statistical modelling record.

Data Structures and Algorithms in MS Data Science & Analytics tests statistical modelling; data scientist evidence from Data Structures and Algorithms must name an assessed result. Machine Learning in Master of Science in Data Science and Analytics then changes the data scientist comparison by adding statistical modelling. For MS Data Science & Analytics, that Data Structures and Algorithms to Machine Learning link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing statistical modelling record.

Machine Learning in MS Data Science & Analytics tests statistical modelling; machine-learning analyst evidence from Machine Learning must name an assessed result. Machine Learning in Master of Science in Data Science and Analytics then changes the machine-learning analyst comparison by adding statistical modelling. For MS Data Science & Analytics, that Machine Learning to Machine Learning link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing statistical modelling record.

Data Science Capstone in MS Data Science & Analytics tests statistical modelling; analytics engineer evidence from Data Science Capstone must name an assessed result. Machine Learning in Master of Science in Data Science and Analytics then changes the analytics engineer comparison by adding statistical modelling. For MS Data Science & Analytics, that Data Science Capstone to Machine Learning link matters only when data science, statistical modelling and computational analytics advances beyond the applicant’s existing statistical modelling record.

What does the Georgetown University MS Data Science and Analytics curriculum contain?

The official curriculum requires 30 credit hours. The table preserves every named coded item found on that page and keeps published curriculum areas where Georgetown directs students to an approved list. Availability and prerequisites still need a live check before enrolment.

The note “Published core or representative curriculum requirement” applies to 4 components in this table.

• Complete 30 approved graduate credit hours.
• Follow the published choice among team-based data science capstone.
• Confirm approved electives, prerequisites and the plan of study with the academic unit.

Use the required sequence to establish readiness for Python And Sql, then choose electives that deepen machine learning instead of creating several disconnected introductions. The official plan of study remains the authority for what can count together.

Georgetown can revise course availability and approved lists. Recheck every code, credit value, campus offering and culminating route before accepting an offer, especially where the catalogue publishes an area rather than a closed list of named electives.

Should an Indian applicant shortlist the Georgetown University MS Data Science and Analytics?

Shortlist the Georgetown University MS Data Science and Analytics when your transcript already supports Python and SQL and statistical modelling, your intended work uses machine learning and the full INR 1.47 crore plan is fundable without depending on uncertain work income. Treat those as separate thresholds.

The strongest case connects Python And Sql to Statistical Modelling, then uses the culminating route to create inspectable proof. That is a clearer reason to choose this Georgetown course than a general wish to study at a large US university.

The main drawback is cost: the conservative full-programme case is INR 1.47 crore before flights and a housing deposit. Georgetown does not guarantee a job, salary, OPT approval or employer sponsorship for this exact programme. Applicants who need a guaranteed funding or employment outcome should not shortlist it on the university name alone.

Sources

These sources support the programme, admission, cost, experience and immigration information used on this page.

Sources checked on October 5, 2026. Current intake information follows. Fall 2027 full-time route.