IGNOU MCS-067 Assignment 2026-27
Data Wrangling and Visualization
Last updated by IGNOU: 29 Jul 2026. Guide written from the official PDF on 28 Oct 2026, 04:24 pm.
| Course code | MCS-067 |
|---|---|
| Course title | Data Wrangling and Visualization |
| Programme | Master of Science Data Science and Analytics (MSCDSA)MSCDSA |
| School | School of Computer and Information Sciences |
| Assignment code | MSCDSA(II)/067/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-067 assignment
The MCS-067 assignment covers preparing, cleaning, combining and summarising data with Python and pandas. It also explores statistical plots, dashboards, graph representations and visualisation methods for weather data, social networks, random forests and regression.
There are four main questions worth 20 marks each, making 80 marks for written work, with another 20 marks for a compulsory viva voce. The questions combine explanations, examples, dataset creation and Python programming. Any requested algorithms should use pseudocode close to Python, and illustrations or diagrams may support explanations.
What the assignment asks
| Question | Marks | Topic |
|---|---|---|
| Q1 | 20 | Explain data wrangling and profiling, profile customer transactions, create and clean an employee CSV using pandas, and demonstrate missing-data handling, age binning and outlier detection. |
| Q2 | 20 | Demonstrate file merging, hierarchical indexing, reshaping and grouped calculations, then create sales plots and illustrate key plotting components using Python. |
| Q3 | 20 | Create a scatter plot matrix and a multi-plot dashboard, justify weather-data visualisations, and explain graph applications and social-network representations. |
| Q4 | 20 | Explain random-forest visualisations, bivariate histograms, kernel density estimation, the Trellis paradigm and regression visualisations with examples where requested. |
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 assignment without attending the viva results in zero marks and unsuccessful completion.
- Write any requested algorithms in pseudocode that is close to Python.
- Use illustrations and diagrams where needed to support your explanations.
Submission dates: 30 April 2027 (June TEE; January 2027 session); 31 October 2026 (December TEE; July 2026 session)
Frequently asked questions
What is the MCS-067 assignment code for 2026-27?
The course assignment code is MSCDSA(II)/067/Assign/2026-27.
What are the submission deadlines for MCS-067?
The deadline is 31 October 2026 for the July 2026 session and December term-end exam, and 30 April 2027 for the January 2027 session and June term-end exam.
How are the MCS-067 assignment marks divided?
The assignment carries 100 marks: four main questions worth 20 marks each and a viva voce worth 20 marks. Its weightage is 30%.
Is the MCS-067 assignment viva compulsory?
Yes. If you submit the assignment but do not attend the viva voce, the assignment is treated as incomplete and awarded zero marks.
Where should I submit the MCS-067 assignment?
Submit it to the Coordinator of your Study Centre on or before the applicable due date.
Does the MCS-067 assignment require Python programming?
Yes. Several tasks require Python code for data cleaning, merging, grouping and visualisation, including work with pandas and Matplotlib and/or Seaborn. Any requested algorithms should use pseudocode close to Python.
Other IGNOU MSCDSA assignments
- MCS-061: Mathematical Foundations-I
- MCS-066: Mathematical Foundations for Data Science-II
- 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.