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IGNOU MCSL 65 SOLVED ASSIGNMENT

IGNOU MCSL 65 SOLVED ASSIGNMENT

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).
FormatReady-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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📋 Assignment Content Preview
Included:

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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