M.Sc. Data Science and Analytics
Build a foundation for working with data, from mathematical reasoning and programming to advanced analytics. MSCDSA combines theory, laboratory work and an independent project, making it useful for learners who want to understand how data becomes evidence for decisions.
An independent student guide to eligibility, fees, syllabus and academic preparation.
Programme overview & eligibility
Academic home
School of Computer and Information Sciences (SOCIS)
Programme code: MSCDSA
Qualification: Master’s degree
Mode: Open and Distance Learning
Who can apply?
A bachelor’s degree of at least three years from a recognized university or institution.
Learning focus: Statistical and computational approaches to data analysis.
Sources: Official MSCDSA programme page and programme guide / prospectus. Check your registration record for the curriculum applicable to your admission session.
Fees & duration
| Item | Verified information |
|---|---|
| Published programme fee | ₹13,000 per semester |
| Programme tuition budget | ₹52,000 for four semesters |
| Completion window | Minimum 24 months; maximum 48 months |
| Additional charges | Registration, development and examination fees are excluded from the semester fee. |
The amount above is a programme-fee snapshot, not a complete admission invoice. Pay only through official IGNOU channels and review the fee shown for your chosen programme and admission cycle before submitting the application.
Fee source: IGNOU programme listing, checked 06 October 2026. Total tuition for semester/year-based fees is calculated from the published rate and minimum programme length.
Syllabus & course structure
Use the course codes below to match your study material, assignments and registration. Where a group says “choose”, complete only the required number of electives. Project and practical credits are part of the degree.
Semester 1
| Code | Course | Credits |
|---|---|---|
| MCS-061 | Mathematical Foundations - I | 4 |
| MCS-062 | Introduction to Data Science | 4 |
| MCS-063 | Data Structures using Python | 4 |
| MCS-207 | Database Management Systems | 4 |
| MCSL-064 | Data Structures using Python Lab | 2 |
| MCSL-065 | Data Science Lab | 2 |
Semester 2
| Code | Course | Credits |
|---|---|---|
| MCS-066 | Mathematical Foundations - II | 4 |
| MCS-067 | Data Wrangling and Visualization | 4 |
| MCS-224 | Artificial Intelligence and Machine Learning | 4 |
| MCS-068 | Predictive Data Analysis | 4 |
| MCSL-069 | Artificial Intelligence and Machine Learning Lab | 2 |
| MCSL-070 | Data Analysis Lab | 2 |
Semester 3
| Code | Course | Credits |
|---|---|---|
| MCS-071 | Big Data Analytics | 4 |
| MCS-072 | Deep Learning | 4 |
| MCS-073 | Soft Computing | 4 |
| MCS-074 | Cloud Computing | 4 |
| MCSL-075 | Big Data Analytics Lab | 2 |
| MCSL-076 | Deep Learning Lab | 2 |
Semester 4
| Code | Course | Credits |
|---|---|---|
| MCS-077 | Natural Language Processing | 4 |
| MCS-078 | Data Security | 4 |
| MCSP-079 | Project Work | 12 |
Curriculum source: 2026 MSCDSA programme guide. Live listings can contain duplicate or older entries; the curriculum above follows the verified structure rather than adding every listed course together.
Assignments, examinations & practical requirements
Assessment rules
The 2026 guide gives assignments 30% and term-end theory/practical examinations 70% for taught courses. The evaluation table requires 40% separately in assignments and term-end examinations. Practical lab attendance is compulsory at 75%; lab examination parts must each be passed.
Projects & field learning
MCSP-079 is a 12-credit project. The 2026 programme guide asks learners to submit the proposal in Semester III and the report in Semester IV. Report evaluation and viva are separate components; each requires 40%.
Before submitting an assignment
- Download the official assignment booklet for your programme, exact subject code and valid session. Read its submission instructions before drafting answers.
- Write answers in your own words, follow the question’s scope and cite sources when needed. Use notes to understand topics and organise revision.
- Check your enrolment details, course code and required cover-page information. Keep a copy of the submission and its acknowledgement.
- Confirm the current submission route and deadline with the official notice or your learner support centre. An old booklet or an informal message may not apply to your session.
Before an examination
Review the official examination-form notice, course eligibility, timetable and hall ticket. Match each selected paper to your registration and retain the payment receipt. For practical examinations, projects or fieldwork, confirm the separate arrangements with your learner support centre.
A practical study plan for MSCDSA
The following are independent study suggestions, rather than university regulations. Adapt them to your registered courses and the time you can devote each week.
- Recreate small data analyses yourself. Keep the raw dataset, cleaning steps, code and interpretation together so you can explain every result.
- Practise Python and database queries alongside your theory reading. A short working example helps you remember an algorithm better than copied code.
- For your project, choose a manageable question and a dataset you can use responsibly. Record data provenance, limitations and reproducible steps.
Build understanding
Start with the learning objectives in a unit, then read the explanation and attempt the self-check questions. Make a short summary using your own examples. Mark difficult ideas for discussion during counselling.
Prepare for revision
Keep a topic checklist and practise answering questions within a time limit. Compare your answer with the question’s command word and expected scope. Leave time to review gaps rather than only rereading familiar topics.
Using notes effectively
Choose notes that match your exact subject code and curriculum. Use them as a companion to official study material, not as evidence that a particular question will appear in an exam. For projects or fieldwork, keep your own records and follow the approved academic process.
Official resources & student checklist
- ↗ MSCDSA official programme details
- ↗ Programme guide / prospectus
- ↗ eGyanKosh study material
- ↗ Official assignment downloads
- ↗ IGNOU ODL admission portal
- ↗ IGNOU notices & student services
Keep your admission confirmation, course-registration details, assignment receipts and practical/project documents together. Check that your selected study centre supports the programme and its practical components. Search digital material by individual subject code if searching only the programme name does not locate it.
Resource availability and notices vary by session. Follow the instructions on the official resource you use.
Frequently asked questions
What is IGNOU MSCDSA?
MSCDSA is the programme code for Master of Science (Data Science and Analytics), offered by School of Computer and Information Sciences (SOCIS).
Who can apply?
A bachelor’s degree of at least three years from a recognized university or institution.
What is the duration and medium?
The minimum duration is two years and the maximum is four years. The medium is English.
What fees should I budget for?
The official programme page lists ₹13,000 per semester. Examination fees are additional. Confirm admission-cycle fees and other applicable charges in the official admission portal.
Are projects or practical work required?
MCSP-079 is a 12-credit project. The 2026 programme guide asks learners to submit the proposal in Semester III and the report in Semester IV. Report evaluation and viva are separate components; each requires 40%.
Where can I find the detailed syllabus and notes?
Use the linked official programme guide/prospectus and IGNOU study material for the authoritative syllabus. The WhatsApp service offers independent notes and study support; it is not an official IGNOU service.