IGNOU MCSL-070 Assignment 2026-27
Data Analysis Lab
Last updated by IGNOU: 29 Jul 2026. Guide written from the official PDF on 28 Oct 2026, 04:26 pm.
| Course code | MCSL-070 |
|---|---|
| Course title | Data Analysis Lab |
| Programme | Master of Science Data Science and Analytics (MSCDSA)MSCDSA |
| School | School of Computer and Information Sciences |
| Assignment code | MSCDSA(II)/L-070/Lab_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 MCSL-070 assignment
The MCSL-070 assignment covers practical data analysis using Python and R. Tasks include data cleaning, aggregation, merging, reshaping, visualisation, statistical testing, regression and classification. Students work with supplied datasets and datasets available in R packages.
There are two sections worth 20 marks each: five Python questions and four R questions. Lab records carry another 40 marks, while the compulsory viva voce carries 20 marks. Programming submissions must include source code, documentation, output and screenshots of execution.
What the assignment asks
| Question | Marks | Topic |
|---|---|---|
| Section 1, Q1 | 3 | Create and inspect a customer transaction DataFrame, handle missing numerical values using column means, remove duplicate records and display the cleaned data. |
| Section 1, Q2 | 3 | Aggregate supermarket sales by region and category, calculate sales and profit summaries, and sort the aggregated results. |
| Section 1, Q3 | 4 | Merge student details with marks, calculate department-level averages through a pivot table, and reshape the data using hierarchical indexing. |
| Section 1, Q4 | 5 | Visualise monthly product sales through line, grouped bar, histogram and scatter plots with suitable labels and annotations. |
| Section 1, Q5 | 5 | Explore advertising expenditure and sales using a scatter plot, simple linear regression, residual plots and a sales density plot. |
| Section 2, Q1 | 5 | Use R to inspect and convert car-data variables, filter records, calculate averages, transform brand names and create price categories. |
| Section 2, Q2 | 5 | Integrate customer, order and review datasets in R, summarise sales by region and product category, and visualise sales and customer ratings. |
| Section 2, Q3 | 5 | Use Cars93 and mtcars in R for a chi-square association test, mileage regression and prediction, and logistic modelling of transmission type. |
| Section 2, Q4 | 5 | Build and compare decision tree and Random Forest classifiers for native-speaker status using readingSkills, including predictions, a confusion matrix and feature importance. |
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 submission is required for the corresponding term-end examination.
- For the term-end practical examination, meet the minimum attendance requirement as well as the assignment submission requirement described in the MSCDSA Programme Guide.
- Attend the compulsory assignment viva voce; submitting the assignment without attending the viva results in zero marks and unsuccessful completion.
- Prepare and present the assignment according to the MSCDSA Programme Guide.
- For programming tasks, include source code, documentation, output and screenshots showing program execution.
- For Section 1, use Python with the specified libraries where needed; Google Colab, Jupyter Notebook or another Python IDE is permitted, and any assumptions must be stated.
- Use diagrams, tables, illustrations and screenshots where appropriate to make the presentation clearer.
Submission dates: 30 April 2027 (June TEE; January 2027 session); 31 October 2026 (December TEE; July 2026 session).
Frequently asked questions
Which sessions does the MCSL-070 assignment cover?
It covers the July 2026 and January 2027 sessions of MSCDSA.
What are the submission deadlines for MCSL-070?
The deadline is 31 October 2026 for the July session and December TEE, and 30 April 2027 for the January session and June TEE. Dates differ by session.
How are the 100 marks distributed?
The two assignment sections carry 20 marks each, lab records carry 40 marks, and viva voce carries 20 marks. The assignment weightage is 30%.
Is viva voce compulsory for MCSL-070?
Yes. If you submit the assignment but do not attend the viva voce, the assignment is treated as incomplete and receives zero marks.
What should programming answers include?
Include source code, documentation, output and screenshots of execution. Prepare the work according to the MSCDSA Programme Guide.
Which programming tools are used in this assignment?
Section 1 uses Python with NumPy, Pandas, Matplotlib and/or Seaborn where required, and permits Colab, Jupyter Notebook or another Python IDE. Section 2 uses R, including datasets and modelling tools from MASS, party and randomForest.
Other IGNOU MSCDSA assignments
- MCS-061: Mathematical Foundations-I
- MCS-066: Mathematical Foundations for Data Science-II
- 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
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.