IGNOU MCS-066 Assignment 2026-27
Mathematical Foundations for Data Science-II
Last updated by IGNOU: 29 Jul 2026. Guide written from the official PDF on 28 Oct 2026, 04:24 pm.
| Course code | MCS-066 |
|---|---|
| Course title | Mathematical Foundations for Data Science-II |
| Programme | Master of Science Data Science and Analytics (MSCDSA)MSCDSA |
| School | School of Computer and Information Sciences |
| Assignment code | MSCDSA (II)/066/Assign/2026-27 |
| Session | July 2026 and January 2027 |
| Maximum marks | 100 |
| Weightage | 30% |
| Last date (June exam) | 30 Apr 2027 |
| Last date (December exam) | 31 Oct 2026 |
| Medium | English |
About the MCS-066 assignment
The MCS-066 assignment covers descriptive statistics, probability, statistical distributions, sampling, estimation and hypothesis testing. It also includes ANOVA, goodness-of-fit tests, optimisation methods and pseudo-random number generation, combining numerical problems with explanations of statistical concepts.
There are four main questions, each covering one block and carrying 20 marks, for a written total of 80 marks. A compulsory viva voce carries the remaining 20 marks. The questions contain multiple subparts, and illustrations or diagrams may be used where helpful.
What the assignment asks
| Question | Marks | Topic |
|---|---|---|
| Q1 (Block 1) | 20 | Identify populations, sampling approaches and measurement scales, present data through tables and plots, calculate measures of location and dispersion, and explain distribution shape. |
| Q2 (Block 2) | 20 | Solve arrangement, conditional probability and distribution problems, calculate expectation and variance, and explain joint, marginal, discrete and continuous probability distributions. |
| Q3 (Block 3) | 20 | Explain the Central Limit Theorem and estimation methods, calculate sampling probabilities and confidence intervals, test a population mean, and discuss hypothesis-testing concepts. |
| Q4 (Block 4) | 20 | Apply ANOVA and chi-square testing, explain goodness-of-fit procedures, compare gradient-based optimisation methods, and describe pseudo-random number generation. |
This is a summary of each question. Download the official PDF above for the exact questions and word limits.
How to write and submit it
- Submit the assignment to the Coordinator of your Study Centre by the applicable deadline.
- Timely assignment submission is compulsory for eligibility to appear in the corresponding term-end examination.
- Attend the compulsory assignment viva voce; submitting the written assignment without attending the viva results in zero marks and unsuccessful completion.
- Use illustrations and diagrams where they help explain your work.
Submission dates: 30 April 2027 (June term-end exam; January 2027 session); 31 October 2026 (December term-end exam; July 2026 session).
Frequently asked questions
Which sessions does the MCS-066 assignment cover?
It covers the July 2026 and January 2027 sessions of MSCDSA.
What are the submission deadlines for MCS-066?
The deadline is 31 October 2026 for the December term-end exam and 30 April 2027 for the June term-end exam, corresponding to the July 2026 and January 2027 sessions respectively.
How are the MCS-066 assignment marks divided?
The assignment carries 100 marks: four written questions worth 20 marks each and a viva voce worth 20 marks. Its weightage is 30%.
Is the MCS-066 assignment viva compulsory?
Yes. If you submit the assignment but do not attend the viva voce, the assignment is treated as unsuccessfully completed and awarded zero marks.
Where should I submit the MCS-066 assignment?
Submit it to the Coordinator of your Study Centre on or before the applicable due date.
What is the assignment code for MCS-066?
The course page gives the assignment code as MSCDSA (II)/066/Assign/2026-27.
Other IGNOU MSCDSA assignments
- MCS-061: Mathematical Foundations-I
- MCS-067: Data Wrangling and Visualization
- MCS-068: Predictive Data Analysis
- MCS-207: Database Management Systems
- MCS-224: Artificial Intelligence & Machine Learning
- MCSL-069: Artificial Intelligence & Machine Learning Lab
- MCSL-070: Data Analysis Lab
Assignment details and PDFs come from IGNOU's official website; the guide is a summary of the official PDF. Always check the latest instructions and last dates on ignou.ac.in or with your Regional Centre before submitting.