EPFL MSc Data Science

What is EPFL MSc Data Science?

EPFL’s MSc Data Science is a full-time 120 ECTS master’s in Lausanne. The route moves from HSS : Introduction to towards Foundation models and generative and prepares students for data scientist and machine learning engineer work. It isn’t a conversion degree, so the relevant bachelor’s foundation must be visible before enrolment.

Basics of mobile robotics and Machine learning for behavioral point to different decisions inside MSc Data Science. A strong study plan explains why both serve one defined quantitative analyst problem, what method each contributes and which limitation the final project will test rather than hiding behind the breadth of the catalogue.

The later appearance of Sublinear algorithms helps an applicant work backwards from a graduation output. That academic sequence should be planned beside the INR 68.60 lakh full-programme budget, because changing direction after arrival costs both credits and living time in Lausanne.

How much does EPFL MSc Data Science cost for an Indian student?

The two-year planning total is CHF 59,548, about INR 68.60 lakh. It combines four CHF 2,240 international semester invoices, two years of EPFL’s published foreign-student living budget and the CHF 150 foreign-certificate application fee. It is a planning total, not an invoice issued in one payment.

EPFL's living budget is CHF 25,219 a year for a foreign student and describes a rental deposit as money that may be blocked, not spent. Converted at CHF 1 = INR 115.20, derived from ECB daily reference rates for INR and CHF on 29 September 2026. Flights, exchange spreads and the blocked rental deposit are excluded.

The international invoice contains CHF 2,190 tuition and CHF 50 in membership, sports and social-fund charges. EPFL states the fee rule directly.

Only the CHF 730 semester tuition fee is tripled by this measure.EPFL Study Management

The resulting international tuition is CHF 2,190, before the CHF 50 semester charges.

Living costs create most of the financial exposure. EPFL’s foreign-student table includes CHF 10,800 yearly housing, CHF 7,200 food, CHF 1,400 health insurance, transport, liability cover, residence tax, supplies and other expenses. Architecture has an additional published study-visit allowance that should be added where applicable.

The CHF 10,000-per-semester Excellence Fellowship is competitive and limited. It can cover at most four semesters and doesn’t remove the need to pay tuition, rent and other costs on time, so this budget doesn’t assume the fellowship is won.

Can an Indian applicant meet EPFL MSc Data Science entry requirements?

The academic starting point is a computer science, communication systems, mathematics or closely related quantitative bachelor’s with excellent results. EPFL evaluates the university, curriculum, grades, research references, specialisation, statement and recommendations together. It publishes no universal Indian percentage or CGPA that guarantees admission.

For MSc Data Science, the useful transcript map places the Indian module, credit load, result and official syllabus beside Machine learning. That lets the committee judge substance rather than infer readiness from a translated award title.

EPFL can prescribe extra credits for limited gaps, but Computational neurosciences: neuronal dynamics signals the level at which this master’s operates. The discretion doesn’t promise to rebuild the earlier preparation needed for data scientist work.

A referee supporting the machine learning engineer direction should describe work they actually supervised, its standard and the applicant’s decisions. Evidence tied to Information theory and coding is more useful than praise that could accompany any postgraduate application.

Four checks that decide an EPFL MSc Data Science file

1. Meeting every published condition still does not secure admission

   EPFL states the limitation plainly.

   Please note that meeting all of these criteria is no guarantee of admission.EPFL MSc Data Science admission criteria

   The committee can still compare academic quality, references, specialisation and the overall file.

2. Only one EPFL master's choice can be submitted

   EPFL permits one master’s application per academic year. Choosing MSc Data Science therefore removes the option to submit a second EPFL programme as an internal safety choice.

3. The translated degree title is not the subject map

   For MSc Data Science, list the completed modules that prove the required academic preparation. Match each claim to credits, results and an official syllabus so any missing depth is visible before payment.

4. Reference timing is separate from the form deadline

   The form closes on 15 December or 31 March, but referees receive one further week. The applicant cannot upload those letters personally, and the file is not processed unless EPFL receives at least two.

How should an Indian student apply for EPFL MSc Data Science in 2027?

The route is one online EPFL application followed by a separate Swiss visa and residence process. Submit by 15 December 2026 or 31 March 2027; visa applicants are advised to use December because that result is expected in late March instead of mid-June.

Choose one EPFL master's, submit the complete online dossier and fee, secure referee letters, meet any offer conditions, then complete the Swiss entry and residence process.

The allowances are Nbyula planning estimates, not processing times published by EPFL (École Polytechnique Fédérale de Lausanne).

The online file accepts English or French documents. A document in another language needs a legalised translation, while the final proof of the qualifying degree may arrive no later than 30 September after admission.

The application fee is CHF 150 for a foreign bachelor’s and isn’t refunded after withdrawal or non-validation. Payment and form validation are both required before the chosen round closes.

The later round is academically available, but its mid-June result reduces the time for the visa, housing and travel. That timing cost matters more for an Indian applicant than any belief that one round has easier academic selection, which EPFL doesn’t state.

What jobs can follow EPFL MSc Data Science?

The programme can support several technical and research directions. EPFL’s institution-wide 2022 survey reported a CHF 88,108 average salary among 315 master’s respondents working in Switzerland, but that isn’t a programme placement rate or a salary promise for this cohort.

data scientist, machine learning engineer and quantitative analyst are curriculum-supported directions for MSc Data Science. They are not a programme-specific placement result, employer commitment or Swiss permit promise.

A data scientist portfolio can use Basics of mobile robotics to state the method and Foundation models and generative AI to show where that method was tested. The valuable evidence is the student’s own decision, result and limitation, not the EPFL label on its own.

Someone targeting machine learning engineer work would need a different emphasis: Machine learning for behavioral data can support the specialism, while Sublinear algorithms can make the final output inspectable. Scheduling and prerequisites still decide whether that combination fits one 120 ECTS plan.

After MSc Data Science, Switzerland gives a third-country graduate six months to seek work matching the qualification. A data scientist offer still goes through authority review; completing Master project in Data science doesn’t itself create permission to work.

For a prospective quantitative analyst, French ability, prior experience and the exact vacancy may matter alongside technical evidence from Computational neurosciences: neuronal dynamics. EPFL publishes no audited MSc Data Science placement percentage with which to predict that outcome.

In MSc Data Science, HSS : Introduction to project, HSS : Project and Semester research project in Data Science make the route towards data scientist concrete. Their position in Published programme options joins HSS : Introduction to project to Semester research project in Data Science, so the academic story is about a connected capability rather than a collection of fashionable titles.

The Block “Projects & SHS” sequence contains Algorithms II, Applied data analysis and Foundations of Data Science. For MSc Data Science, that sequence turns machine learning, statistics, algorithms, natural language processing, databases and information theory into assessed work and exposes whether an applicant’s earlier study reaches the level expected for machine learning engineer problems.

A student shaping MSc Data Science around quantitative analyst work can read Machine learning, Modern natural language processing and Optimization for machine learning as one evidence chain. Machine learning supplies one part of the method, while Optimization for machine learning shows the later application or project context recorded in Block “Projects & SHS”.

EPFL places Systems for data management and data science, Advanced compiler construction and Advanced cryptography (This course is a “depth” for Cyber Security master program and Cyber Security minor.) within Block “Projects & SHS” for this programme. The combination matters because MSc Data Science is built around machine learning, statistics, algorithms, natural language processing, databases and information theory; isolated familiarity with only one topic would not reproduce that breadth.

The published path from Advanced probability and applications to AI for global health passes through Advanced probability and applications, Advanced topics on privacy enhancing technologies (This course is a “depth” for Cyber Security master program and Cyber Security minor.) and AI for global health. In a machine learning engineer portfolio, those assessed components can document technique, implementation and judgement instead of relying on the degree name as evidence.

For MSc Data Science, the cluster AI product management, Algorithmic game theory and Applied biostatistics (Pas donné en 2026-27) sits under Group 1. It links the programme’s machine learning, statistics, algorithms, natural language processing, databases and information theory direction to the kind of decisions a quantitative analyst must explain, test and defend in a project.

The curriculum does not present Applied biostatistics (Pas donné en 2026-27) in isolation: nearby choices include Applied biostatistics (Pas donné en 2026-27), Automatic speech processing and Basics of mobile robotics. That neighbourhood distinguishes MSc Data Science from a broad conversion degree and rewards prior preparation aligned with machine learning, statistics, algorithms, natural language processing, databases and information theory.

Within Group 1, EPFL lists Brain-style learning in neural networks, Causal inference and Causal thinking. The useful outcome is not exposure to three labels; it is an assessed route in which Brain-style learning in neural networks can feed the analysis behind Causal thinking for future machine learning engineer work.

The MSc Data Science catalogue pairs Computational complexity, Computational linear algebra and Computational neurosciences: neuronal dynamics in Group 1. This pairing shows why an applicant whose prior degree lacks probability, linear algebra, algorithms and programming would face a structural gap: the programme combines foundations and advanced application rather than rebuilding the missing base.

A coherent data scientist direction emerges where Computers and music (Pas donné en 2026-27), Computer vision and Concurrent computing are treated as related evidence. Their shared place in Group 1 connects the stated programme focus, machine learning, statistics, algorithms, natural language processing, databases and information theory, with work that can be inspected after graduation.

Among the published choices for MSc Data Science are Cryptography and security (This course is a “depth” for Cyber Security master program and Cyber Security minor.), Data visualization and Deep learning. Together they move from Cryptography and security (This course is a “depth” for Cyber Security master program and Cyber Security minor.) towards Deep learning, giving a project supervisor or employer a clearer signal than an undifferentiated list of electives.

The later academic value of Group 1 comes from combinations such as Deep learning in biomedicine, Digital education and Distributed algorithms. For a prospective quantitative analyst, this part of MSc Data Science supports a defensible specialism while remaining inside the approved 120 ECTS structure.

In MSc Data Science, Distributed intelligent systems (Pas donné en 2026-27), Ethics and law of AI and Formal verification (This course is a “depth” for Cyber Security master program and Cyber Security minor.) make the route towards data scientist concrete. Their position in Group 1 joins Distributed intelligent systems (Pas donné en 2026-27) to Formal verification (This course is a “depth” for Cyber Security master program and Cyber Security minor.), so the academic story is about a connected capability rather than a collection of fashionable titles.

The Group 1 sequence contains Foundation models and generative AI, Geometric computing (Cours donné en alternance tous les deux ans ) and Graph theory. For MSc Data Science, that sequence turns machine learning, statistics, algorithms, natural language processing, databases and information theory into assessed work and exposes whether an applicant’s earlier study reaches the level expected for machine learning engineer problems.

A student shaping MSc Data Science around quantitative analyst work can read Image analysis and pattern recognition, Image processing I and Image processing II as one evidence chain. Image analysis and pattern recognition supplies one part of the method, while Image processing II shows the later application or project context recorded in Group 1.

EPFL places Information theory and coding, Interactive theorem proving (This course is a “depth” for Cyber Security master program and Cyber Security minor.) and Introduction to quantum cryptography (This course is a « depth » for Cyber Security master program and Cyber Security minor) within Group 1 for this programme. The combination matters because MSc Data Science is built around machine learning, statistics, algorithms, natural language processing, databases and information theory; isolated familiarity with only one topic would not reproduce that breadth.

The published path from Large-scale data science for real-world data to Linear models passes through Large-scale data science for real-world data, Learning theory (Pas donné en 2026-27) and Linear models. In a machine learning engineer portfolio, those assessed components can document technique, implementation and judgement instead of relying on the degree name as evidence.

For MSc Data Science, the cluster Machine learning for behavioral data, Markov chains and algorithmic applications (Pas donné en 2026-27, Cours biennal) and Mathematics of data: from theory to computation sits under Group 1. It links the programme’s machine learning, statistics, algorithms, natural language processing, databases and information theory direction to the kind of decisions a quantitative analyst must explain, test and defend in a project.

The curriculum does not present Mathematics of data: from theory to computation in isolation: nearby choices include Mathematics of data: from theory to computation, Network machine learning and Networks out of control (Pas donné en 2026-27, Cours biennal). That neighbourhood distinguishes MSc Data Science from a broad conversion degree and rewards prior preparation aligned with machine learning, statistics, algorithms, natural language processing, databases and information theory.

Within Group 1, EPFL lists Optional research project in Data Science, Project management and risk analysis and Regression methods. The useful outcome is not exposure to three labels; it is an assessed route in which Optional research project in Data Science can feed the analysis behind Regression methods for future machine learning engineer work.

The MSc Data Science catalogue pairs Seminar on applied cryptography (This course is a “depth” for Cyber Security master program and Cyber Security minor.), Software security (This course is a “depth” for Cyber Security master program and Cyber Security minor.) and Statistical inference in Group 1. This pairing shows why an applicant whose prior degree lacks probability, linear algebra, algorithms and programming would face a structural gap: the programme combines foundations and advanced application rather than rebuilding the missing base.

A coherent data scientist direction emerges where Statistical physics of computation, Statistical theory (Pas donné en 2026-27) and Sublinear algorithms are treated as related evidence. Their shared place in Group 1 connects the stated programme focus, machine learning, statistics, algorithms, natural language processing, databases and information theory, with work that can be inspected after graduation.

Among the published choices for MSc Data Science are Time series, Topics in theoretical computer science and Urban digital twins. Together they move from Time series towards Urban digital twins, giving a project supervisor or employer a clearer signal than an undifferentiated list of electives.

The later academic value of Group 1 comes from combinations such as Visual intelligence, Engineering internship credited with Master project (master in Data science) and Master project in Data science. For a prospective quantitative analyst, this part of MSc Data Science supports a defensible specialism while remaining inside the approved 120 ECTS structure.

The Swiss migration authority gives a third-country MSc Data Science graduate six months to seek work matching the qualification. A data scientist offer still needs the applicable authority process; graduation doesn’t turn the search period into an automatic permit.

The federal guidance for foreign workers also leaves the permit with the employer and authorities. For a quantitative analyst, the vacancy, prior experience and language ability therefore matter alongside the MSc Data Science portfolio.

Who is EPFL MSc Data Science for?

MSc Data Science fits an applicant whose earlier work already supports data scientist methods and a project idea for machine learning engineer. Evidence from Machine learning and Information theory and coding can make that preparation concrete. This isn't a conversion degree: the transcript must carry the foundation before enrolment, not promise to acquire it later.

The positive fit connects earlier evidence, a reason for choosing Foundation models and generative and a final project a future quantitative analyst supervisor can inspect. Admission is one checkpoint in that line, not the purpose of assembling it.

The weak fit combines limited preparation for Project management and risk with dependence on immediate earnings or a career aim outside data scientist work. EPFL’s one-choice rule then favours a neighbouring degree whose academic starting point matches the transcript more closely.

What does the EPFL MSc Data Science curriculum contain?

The official 2026 to 27 study plan publishes 70 named courses, projects and options for MSc Data Science. The table keeps every item once. Students complete an approved 120 ECTS route, so the credits shown across the whole option catalogue are not added together.

The note “Group 1” applies to 58 components in this table.

• Complete the approved 120 ECTS full-time master's structure
• Follow mandatory blocks and any specialisation or orientation conditions
• Use prerequisites and semester availability before fixing electives
• Complete the programme's semester project and master's project requirements
• Treat the official regulations and study plan as legally binding
• Recheck the live plan because options may not run every academic year

The table is the reference inventory for the current catalogue, not a personal credit total. The approved route determines which compulsory, optional and project components count towards graduation.

Availability notes and prerequisites matter before an elective is fixed. A named option can belong to the published plan without running in every year or fitting every specialisation timetable.

A coherent study plan moves from prior preparation to advanced depth and one inspectable final project. It shouldn’t be assembled by adding every credit shown in the option catalogue.

Should an Indian applicant shortlist EPFL MSc Data Science?

Shortlist MSc Data Science when the transcript can support Advanced topics on privacy and the proposed project has a defensible data scientist direction. That academic match matters more than the EPFL name because only one EPFL master’s application can be submitted during the year.

The strongest case for MSc Data Science is a progression from the preparation behind Basics of mobile robotics to a final output a machine learning engineer supervisor could assess. EPFL doesn't publish a programme-level placement rate, so inspectable work carries more weight than a salary assumption.

The case against this choice is a transcript that cannot support advanced work such as Sublinear algorithms. Competitive review won't reconstruct the missing base, while the INR 68.60 lakh plan and first-six-month work restriction make that mismatch costly.

Sources

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

Sources checked on September 29, 2026. The next intake covered here is September 2027; closes 31 March 2027.