UT Austin MS Business Analytics & AI

What is the UT Austin MS Business Analytics & AI?

The UT Austin MS Business Analytics & AI is a 36-credit, full-time master’s route in Austin. Its academic centre is analytics and artificial intelligence for business decisions. This guide covers only the on-campus route for Fall 2027 and does not substitute an online, executive, weekend or concurrent degree with a similar name.

For an Indian applicant, the current all-in planning case is near INR 77.83 lakh. UT Austin has published 2026-27 charges but not 2027-28 rates. Admission, affordability, course fit and work rights are separate decisions, and the financial plan needs refreshing before enrolment.

How much does the UT Austin MS Business Analytics & AI cost for an Indian student?

The full planning case is USD 81,107, about INR 77.83 lakh. It combines UT Austin’s 2026-27 non-resident graduate cost basis with the application, SEVIS and visa fees. This is a comparison case, not an invoice. It excludes flights and a housing deposit, and future rates may change.

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 95.96, using ECB reference rates dated 16 September 2026.

Many graduate programs offer teaching and research assistantships.UT Austin graduate funding

The tuition line uses the programme-specific published amount across the 10-month route. It isn’t a future invoice, and course loads can change.

The living line prorates UT Austin’s published housing, food, transport, books and personal allowance. Actual rent won’t match every student’s case.

The estimate includes the international application, SEVIS and F-1 visa fees because they affect the applicant’s budget. They don’t all reach UT Austin.

Funding, assistantships and resident-tuition entitlements are competitive. The headline doesn’t assume an award.

UT Austin has not published 2027-28 charges, so the current evidence can’t predict the final bill. The official programme and Texas One Stop pages should be refreshed before a deposit.

Can an Indian applicant meet UT Austin MS Business Analytics & AI entry rules?

The first check uses the degree-specific admission page. It asks for a bachelor’s degree from a recognised institution and at least 3.00 on a 4.00 scale. Then check subject preparation, English work sample and every listed document separately. The degree name alone will not establish preparation.

The first transcript audit should find concrete proof of predictive modelling. A useful audit records the module title, mark, credit weight and syllabus topic so an assessor does not have to infer readiness from the institution name.

Next, isolate work sample for machine learning. A laboratory, project or substantial assignment is stronger than a list of buzzwords because it shows what was built, measured or decided and what limitations remained.

The statement should explain why data management is the next academic step and why UT Austin’s analytics communication route serves it. Repeating the degree webpage does not answer that personal progression question.

Finally, English, recommendations and translations remain independent document checks. A file that clears the academic match can remain incomplete if an accepted score, literal translation or required referee response is missing.

Current admission and course structure work sample is linked from https://www.mccombs.utexas.edu/graduate/specialized-masters/ms-business-analytics-and-artificial-intelligence/on-campus

MS Business Analytics and AI uses Predictive Modelling as a 9-credit checkpoint. Readiness for predictive modelling is visible before Predictive Modelling, not repaired automatically by enrolment. A transcript item, syllabus topic and assessed result together make that preparation easier to recognise.

MS Business Analytics and AI uses Machine Learning as a 9-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 Business Analytics and AI uses Data Management as a 9-credit checkpoint. Readiness for data management is visible before Data Management, not repaired automatically by enrolment. A transcript item, syllabus topic and assessed result together make that preparation easier to recognise.

MS Business Analytics and AI uses Analytics Communication as a 9-credit checkpoint. Readiness for analytics communication is visible before Analytics Communication, not repaired automatically by enrolment. A transcript item, syllabus topic and assessed result together make that preparation easier to recognise.

MS Business Analytics and AI uses Predictive Modelling as a 9-credit checkpoint. Readiness for predictive modelling is visible before Predictive Modelling, not repaired automatically by enrolment. A transcript item, syllabus topic and assessed result together make that preparation easier to recognise.

Within MS Business Analytics and AI, the relationship between predictive modelling and data management is a readiness test for data scientist ambitions. A file showing only machine learning leaves the analytics communication part of this academic progression unexplained.

A future decision scientist still needs documented preparation in statistics, programming, calculus and business analysis. For MS Business Analytics and AI, career intent cannot substitute for that academic base, while the published GPA remains a floor rather than a complete selection model.

Admissions and 2026-27 deadlines: Applications opened 26 August 2026. Round 1 application 13 October 2026; Round 2 12 January 2027 (final international round); Round 3 30 March 2027; Round 4 4 May 2027, with supporting items due one week after each application deadline. GMAT/GRE submission is optional; the application also includes transcripts, résumé, essays, recommendations and the stated interview/video components (application process).

Tuition/fees: For 2026-27, the programme publishes total tuition and fees of $55,600 resident and $59,700 nonresident, including the $2,000 pre-enrolment fee. The listed split is summer $10,600/$11,900 and $21,500/$22,900 in each of fall and spring (tuition and aid). Living expenses, books, insurance and other personal costs should not be silently included in that figure.

Three checks that can block a MS Business Analytics and AI application

1. A GPA floor is not an admission promise

   The 3.00 figure is a minimum under UT Austin’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 name may hide whether statistics, programming, calculus and business analysis was studied. Add syllabi or official descriptions when course titles do not make the preparation clear.

3. English rules can be degree-specific

   The working reference is TOEFL iBT 79 or IELTS 6.5 unless the degree sets a higher minimum. UT Austin says individual programmes may set higher rules or restrict test formats, so the exact degree page controls.

4. The published cost is a planning estimate

   The cost of attendance is an estimate of what it costs the typical student to attend UT.Texas One Stop, cost of attendance

   That wording is why this page keeps tuition, living costs and federal immigration payments separate. An admitted student’s account remains the billing authority.

How should an Indian applicant apply for UT Austin MS Business Analytics & AI?

Apply through UT Austin graduate admission for the exact programme. Use 12 january 2027 final international deadline and start earlier for transcript review, funding evidence, I-20 production and the visa process. The application, department materials and recommendations all need time to reach the file.

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

The allowances are Nbyula planning estimates, not processing times published by The University of Texas at Austin.

Build the file around Predictive Modelling and Machine Learning, not around a generic UT Austin statement. Use earlier coursework or employment to show readiness, then identify the gap that the published curriculum is meant to close.

The programme reports 12 january 2027 final international deadline. A priority date can be followed by space-available review, while a final date closes the published window. Neither should be confused with the separate international I-20 timing needed to reach campus.

After admission, read the offer and MyUT 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 UT Austin MS Business Analytics & AI?

The course structure supports directions such as data scientist, analytics consultant, machine learning analyst and decision scientist. The university presents this course as STEM designated. The OPT framework is work authorisation rather than a placement or sponsorship guarantee. Each direction still needs role-specific, inspectable work samples.

These are course structure-linked directions for MS Business Analytics and AI, not a measured probability of employment, salary, visa sponsorship or promotion.

For a data scientist application, preserve the brief, inputs, method, decisions and limitations from Predictive Modelling. That record gives a recruiter something more reliable than a transcript line or a claim that the degree was practical.

The analytics consultant route needs a different proof item from Machine Learning. Explain the trade-off made, the work sample rejected and the effect of uncertainty so the work shows judgement rather than only tool familiarity.

A candidate aiming at machine learning analyst should use the culminating assessment to join both samples around one problem. A coherent portfolio can then show progression across the degree without asking the UT Austin name to stand in for capability.

F-1 graduates can normally seek up to 12 months of OPT, and an eligible STEM degree may support a further 24-month extension if every rule is met. The USCIS STEM-OPT guidance controls that process and does not require any employer to hire the graduate.

Current title and award: Master of Science in Business Analytics and AI (MSBAi), McCombs School of Business. McCombs’ navigation and current copy use “Business Analytics and AI”; older pages, reports and image text may still say MS Business Analytics/MSBA (programme page).

Format, duration and credits: 10 months, full-time, in person in Austin, 36 credits; STEM-designated. The same page separately labels the online working-professional option as 23 months, so the formats must not be conflated (programme page).

Who is UT Austin MS Business Analytics & AI for, and who should avoid it?

A strong fit already has statistics, programming, calculus and business analysis, wants assessed work sample in data management and can fund INR 77.83 lakh without promised employment. A weak fit needs foundational repair, wants a different technical centre or depends on uncertain US earnings to make the course affordable.

The positive academic test begins with Predictive Modelling. A suitable entrant recognises its foundation from prior work but still needs UT Austin’s graduate-level treatment to solve harder problems in analytics and artificial intelligence for business decisions.

The next fit question concerns Machine Learning. It should add a method or system that the applicant cannot already demonstrate. If it mostly repeats a strong undergraduate module, examine the elective freedom before paying for the overlap.

The professional test is whether a data scientist hiring manager can inspect output from data management. A useful artefact states the problem, data or constraints, the chosen method, the result and the limits of that result.

Someone pursuing analytics consultant work must also value analytics communication. If that part of the degree consumes substantial assessed time but has little use in the intended role, a differently structured master’s may be the better buy.

The final academic trade-off sits in the choice among project, thesis, portfolio or examination where UT Austin lists them. A thesis serves research depth, an applied project serves delivery work sample, and a portfolio serves synthesis. Only routes actually published for this degree belong in the decision.

Affordability is separate from academic fit. The estimate uses USD 1 at INR 95.96, so exchange movement changes the rupee amount even when UT Austin leaves a dollar charge unchanged.

The campus choice also matters. This page uses Austin and in-person study. An applicant selecting an online version would face different attendance, visa and work-authorisation consequences and should not reuse this page’s F-1 assumptions.

Applicants can ask current MS Business Analytics and AI students about access to preferred electives, team formation, faculty supervision and the weekly load. The research pass did not locate three independent exact-degree accounts, so those lived details remain questions rather than reported facts.

The Data Management choice in MS Business Analytics and AI matters to a future machine learning analyst. Its 9 credits are well spent when data management closes a demonstrated gap. They are poorly spent when Data Management merely repeats work already proven in the admission file.

The Analytics Communication choice in MS Business Analytics and AI matters to a future decision scientist. Its 9 credits are well spent when analytics communication closes a demonstrated gap. They are poorly spent when Analytics Communication merely repeats work already proven in the admission file.

The Predictive Modelling choice in MS Business Analytics and AI matters to a future data scientist. Its 9 credits are well spent when predictive modelling closes a demonstrated gap. They are poorly spent when Predictive Modelling merely repeats work already proven in the admission file.

The Machine Learning choice in MS Business Analytics and AI matters to a future analytics consultant. Its 9 credits are well spent when machine learning closes a demonstrated gap. They are poorly spent when Machine Learning merely repeats work already proven in the admission file.

The Data Management choice in MS Business Analytics and AI matters to a future machine learning analyst. Its 9 credits are well spent when data management closes a demonstrated gap. They are poorly spent when Data Management merely repeats work already proven in the admission file.

Curriculum: 36 credits across summer, fall and spring, with an industry-sponsored capstone. The current page groups training around analytics, statistics, optimisation, data modelling, machine learning/AI and business communication, with financial analytics and supply-chain/marketing analytics elective routes (curriculum).

Outcomes: The official 2025 report and career page publish outcomes under the predecessor MS Business Analytics name. Current role families include analytics, data science, consulting and technology; the page lists average salaries by function, including $129,000 for marketing analytics, $117,000 for IT, $105,000 for business analytics and $85,000 for consulting (career outcomes, 2025 employment report). Function averages can have small denominators; reproduce only with the report’s labels and base counts where available.

What does the UT Austin MS Business Analytics & AI curriculum contain?

The current programme page and graduate catalogue control the 36-credit award. The curriculum areas below help with comparison, but the live plan remains the authority on prerequisites, approved electives and graduation requirements. Use it to compare required foundations, elective freedom and the final research or applied component.

The note “Published study area; exact course credits vary” applies to 4 components in this table.

• Complete 36 approved graduate credit hours.
• Follow the published choice among an applied analytics capstone.
• Confirm approved electives, prerequisites and the plan of study with the academic unit.

Use the required sequence to establish readiness for Predictive Modelling, then choose electives that deepen data management instead of creating several disconnected introductions. The official plan of study remains the authority for what can count together.

UT Austin 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 UT Austin MS Business Analytics & AI?

Shortlist the UT Austin MS Business Analytics & AI when your transcript already supports statistics, programming, calculus and business analysis, your intended work uses data management and the full INR 77.83 lakh plan is fundable without depending on uncertain work income. Treat each of those as a separate threshold.

The strongest case connects Predictive Modelling to Machine Learning, then uses the culminating route to create inspectable proof. That is a clearer reason to choose this degree than a general wish to study at a large US university.

Do not choose this degree for work-authorisation assumptions alone. The published entry floor is 3.00 on a 4.00 scale, but selection can still test subject depth. The conservative cost case is INR 77.83 lakh before flights and a housing deposit, and UT Austin publishes no guaranteed job or sponsorship outcome for this exact degree.

Sources

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

Sources checked on September 19, 2026. Current intake information follows. Fall 2027 full-time in-person.