Integral University, Lucknow
Effective from Session:2025-26
Course Code CS161 Title of the Course Python Programming L T P C
Year 1st Semester 1st 3 0 2 4
Pre-Requisite Co-requisite
• Understand the process of installing and configuring Python along with its IDEs. To introduce Python
fundamentals with a focus on syntax, control structures, and functions.
Course Objectives • To develop strong understanding of data structures and modular coding.
• To cultivate efficient coding habits for data analysis and AI applications.
• To be able to understand the concept of data visualization techniques and its implementations.
Course Outcomes
CO1 Understand the process of python installation, IDEs, Python syntax, data types, and control structures.
CO2 Develop basic python programs using data structures, demonstrating foundational programming skills.
CO3 Apply, and evaluate functions & modules through object-oriented programming principles in Python. Utilize Python function and modular
programming approach for problem-solving.
CO4 Analyze, apply, and optimize Python codes using built-in functions for developing efficient solutions. Debug Python code effectively using
standard techniques and tools
CO5 Create, Design, and assess solution to real world problems using data visualization techniques, integrating various python-programming concepts
for practical implementation.
THEORY
Unit Title of the Contact Mapped
Content of Unit
No. Unit Hrs. CO
Fundamentals of Python: Overview of Python programming language and its significance,
Introduction to Python IDEs and setting-up the environment, Introduction to Jupiter notebooks, Basic Syntax,
1 8 CO1
Python variables, data types (int, float, str, bool), and, handling Input/output functions, Control structures:
if statements, loops
Elementary Data Structures: Lists, tuples, sets, and dictionaries, List comprehensions and dictionary
Data comprehensions, Basic operations on data structures (Lists, Tuples, Dictionaries); Advanced data
2 7 CO2
Structures in
Python manipulation: comprehensions, dictionary merging, slicing, and nested structures.
Functions & Modules: Defining and calling functions, Function arguments and return values,
Functions &
3 Importing and using modules, Creating and using custom modules. Functions, scope, default args, 7 CO3
Modules
lambda, and modules
Efficient
Coding & Debugging: Code Optimization Techniques in Python: Code optimization using built-
Coding
4 in functions: map, filter, zip; Refactoring for performance. 9 CO4
& Debugging
Debugging with try-except, assertions; Introduction to Python debugging tools (e.g., pdb).
Techniques
Data Introduction to Matplotlib: Basic plotting with Matplotlib, Customizing plots (titles, labels,
Visualization legends), Subplots and grid plots. Seaborn for Statistical Plots: Introduction to Seaborn,
5 9 CO5
& Real-world Creating various plots (histograms, box plots, violin plots), Interactive plots with Plotly. Applying
Applications learned concepts to solve simple data analysis or AI-related problems.
PRACTICAL
Contact Mapped CO
S.No. List of Experiments
Hrs.
Understanding Python installation and its Integrated Development Environments (IDEs).
1 2 CO1
Write a program to illustrate various data types & concepts of variables/Constant in Python.
Write a program to perform various Arithmetic Operations on numbers in Python (Addition, Subtraction,
2 2 CO1
Multiplication, Division, etc.) along with conditional statements.
3 Perform operations on lists, tuples, and dictionaries. 2 CO2
4 Use comprehensions and nested structures for compact code. 2 CO2
Write a program implement the concept of “Functions” in python and sort „n‟ numbers in ascending and descending CO3
5 2
order after taking input (Integer number) from user. Define and use functions with default and keyword arguments.
6 Practice optimization using map, filter, and lambda functions. 2 CO3
7 Handle errors using try-except and raise exceptions deliberately. 2 CO4
8 Debug a faulty Python script using built-in debuggers and logging. 2 CO4
9 Extract text from sample social media data and count word frequency. 2 CO5
10 Perform basic sentiment analysis using keyword-based matching. 2 CO5
Reference Books:
1. Zelle, J. M. Python Programming: An Introduction to Computer Science, Franklin, Beedle & Associates.
2. Lutz, M. Learning Python, O'Reilly Media.
3. Downey, A. Think Python: How to Think Like a Computer Scientist, O'Reilly Media.
4. Bird, S., Klein, E., & Loper, E. Natural Language Processing with Python, O'Reilly Media.
e-LearningSource:
https://nptel.ac.in/courses/106105077
Course Articulation Matrix: (Mapping of COs with POs and PSOs)
PO-
PSO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PSO1 PSO2 PSO3
CO
CO1 1 1 1 1 - - 1 1 1 - 1 1 -
CO2 1 1 1 1 1 - 1 1 2 1 1 2 1
CO3 2 2 2 1 1 - 1 1 2 1 1 2 1
CO4 2 3 2 1 1 - 1 1 3 1 1 2 2
CO5 3 3 3 1 2 - 2 2 3 2 1 3 3
1-Low Correlation; 2- Moderate Correlation; 3- Substantial Correlation
