IGNOU MCSL 65 SOLVED ASSIGNMENT
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MCSL 65: Data Science Lab
| Title Name | IGNOU MCSL 65 SOLVED ASSIGNMENT |
|---|---|
| Type | Soft Copy (E-Assignment) .pdf |
| University | IGNOU |
| Degree | MASTER DEGREE PROGRAMMES |
| Course Code | MSCDSA |
| Course Name | Master of Science (M.Sc.) (Data Science and Analytics) (ODL) |
| Subject Code | MCSL 65 |
| Subject Name | Data Science Lab |
| Year | 2026 |
| Session | - |
| Language | English Medium |
| Assignment Code | MCSL 65/Assignment-1/2026 |
| Product Description | Assignment of MSCDSA (Master of Science (M.Sc.) (Data Science and Analytics) (ODL)) 2026. Latest MCSL 065 2026 Solved Assignment Solutions |
| Last Date of IGNOU Assignment Submission | Last Date of Submission of IGNOU BEGC-131 (BAG) 2025-26 Assignment is for January 2026 Session: 30th September, 2026 (for December 2025 Term End Exam). Semester Wise January 2025 Session: 30th March, 2026 (for June 2026 Term End Exam). July 2025 Session: 30th September, 2025 (for December 2025 Term End Exam). |
| Format | Ready-to-Print PDF (.soft copy) |
📅 Important Submission Dates
- January 2026 Session: 30th April, 2026
- July 2026 Session: 31st October, 2026
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• Guidelines: Strictly follows 2025-26 official word limits.
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MCSL 065 (January 2026 - July 2026) - ENGLISH
Course Code :
MCSL-065
Course Title
: :
Data Science Lab
Assignment Number
: :
MSCDSA(I)/L-065/Lab_Assign/26
Maximum Marks
: :
100
Weightage
: :
30%
Last Date of Submission :
30th April, 2026 (for January session) 31 October, 2026 (for July session)
There are two Sections in this assignment carrying a total of 40 marks. Your Lab Record will carry 40 Marks. Rest 20 marks are for viva voce. You may use illustrations and diagrams to enhance the explanations. Please go through the guidelines regarding assignments given in the Programme Guide for the format of presentation. Submit the screenshots along with the coding and documentation.
Dataset: To attempt the problems given in this assignment, refer to the Dataset: Students' Exam Scores
given below:
| Student_ID | Student_Name | Physics | Chemistry | Maths | English |
|---|---|---|---|---|---|
| S01 | Rahul | 78 | 72 | 85 | 80 |
| S02 | Ananya | 88 | 91 | 90 | 86 |
| S03 | Amit | 65 | 69 | 70 | 72 |
| S04 | Neha | 92 | 89 | 94 | 90 |
| S05 | Karan | 55 | 60 | 58 | 62 |
| S06 | Priya | 81 | 85 | 88 | 84 |
| S07 | Rohit | 70 | 68 | 75 | 73 |
| S08 | Simran | 90 | 92 | 89 | 91 |
| S09 | Arjun | 60 | 65 | 63 | 67 |
| S10 | Pooja | 85 | 88 | 90 | 87 |
SECTION-1 DATA SCIENCE LAB-GUI TOOLS
(20 Marks)
EXCEL BASED PROBLEM:
1. Use Excel to perform following tasks:
(6 Marks)
a. Create a Histogram for Maths marks
b. Plot a Line Chart for Maths scores against Student_ID.
c. Calculate average marks for each subject, and Create a Bar Chart comparing subject-wise average scores.
d. Using average marks, create a Pie Chart showing percentage contribution of each subject.
e. Use data analysis toolpak to perform following:
i. Generate descriptive statistics
ii. Perform a two-sample t-test to check whether there is a significant difference between the marks of Physics and Chemistry.
iii. Perform an F-test to compare the variance of marks in Maths&English
iv. Calculate Pearson Correlation Coefficient between
Maths and Physics
Maths and Chemistry
TABLEAU BASED PROBLEM:
(7 Marks)
2. Use Tableau to perform following tasks:
a. Save the above data given in Dataset: Students' Exam Scores in an excel file or CSV file, save it, and perform following:
Import the Students' Exam Scores dataset into Tableau.
Verify data types for each subject.
Rename fields appropriately (if required).
b. Create a Bar Chart showing average marks for:
Physics
Chemistry
Maths
English
Label axes and add chart title.
c. Create a Line Chart showing Maths scores across students. (Use Student_ID on X-axis.)
d.
Create a Pie Chart showing percentage contribution of each subject based on average marks.
(Display percentage labels.)
e. Create a Table View displaying: Average, Minimum, Maximum, and Standard Deviation. (For all four subjects).
f. Now, create a single dashboard that includes:
i. Bar Chart (Subject-wise Average)
ii. Line Chart (Maths Trend)
iii. Pie Chart (Subject Contribution)
iv. Summary Statistics Table
POWER BI BASED PROBLEM:
(7 Marks)
3. Use Power BI to perform following tasks:
a. Save the above data given in Dataset: Students' Exam Scores in an excel file or CSV file, save it, and perform following:
Import the Students' Exam Scores dataset into Power BI.
Verify data types for each subject.
Rename fields appropriately (if required).
b. Create a Bar Chart showing average marks for:
Physics
Chemistry
Maths
English
Identify the subject with the highest and lowest average score.
c. Create a Line Chart showing Maths scores across students. (Use Student_ID on X-axis.)
d. Create a Pie Chart showing percentage contribution of each subject based on average marks.
(Display percentage labels.)
e. Create the following DAX measures: Average, Minimum, Maximum, and Standard Deviation.
(For all four subjects).
f. Create a Table View displaying: Average, Minimum, Maximum, and Standard Deviation. (For all four subjects).
g.
Now, create a single dashboard that includes:
i. Bar Chart (Subject-wise Average)
ii. Line Chart (Maths Trend)
iii. Pie Chart (Subject Contribution)
iv. Summary Statistics Table
SECTION-2 DATA SCIENCE LAB-PROGRAMMING BASED
(20 Marks)
PYTHON PROGRAMMING BASED PROBLEM:
(10 Marks)
4. Use Python programming language to perform the tasks given below:
Note: Save the above data given in Dataset: Students' Exam Scores in a CSV file (students_scores.csv), save it, and Now Write a program in Python to perform following tasks:
a) Read students_scores.csv into a panda DataFrame. and Display:
First 5 rows
Column names
Data types
b) Generate descriptive statistics, For each subject (Physics, Chemistry, Maths, English), compute:
Mean
Median
Mode
Minimum
Maximum
Range
Variance
Standard Deviation
c) Create a new column Total = sum of 4 subjects; also Also, Create Percentage = (Total/400)* 100
Find:
Topper (max Total)
Lowest scorer (min Total)
d) Plot a scatter diagram and histogram of Maths scores and interpret whether scores are concentrated or spread out.
e) Compute average marks of each subject, and plot a bar chart of subject-wise averages
f) Plot a line chart of Total marks vs Student_ID, andIdentify performance variations (ups/downs).
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