Problem Solving with Python Programming

Paper Code: 
24CBDA112
Credits: 
03
Periods/week: 
06
Max. Marks: 
100.00
Objective: 

The course will enable the students to:

  1. Understand concepts of programming in python
  2. Implementing programming concepts using PYTHON

 

Course Outcomes: 

Course

Course Outcomes

Learning and teaching strategies

Assessment Strategies

Course

Code

Course

Title

 

24CBDA 112

 

 

Problem Solving with Python Programming

 (Practical)

CO7. Install and run the Python interpreter.

CO8. Implement Conditionals and Loops for Python Programs.

CO9. Create, Test and Debug Python Programs.

CO10. Develop functions and represent compound data using Lists, Tuples, sets and Dictionaries.

CO11. Analyse data files using file operations in Python.

CO12 Contribute effectively in

course-specific interaction

Approach in teaching:

Interactive Lectures, Group Discussion,  Case Study, Demonstration

 

Learning activities for the students:

Self-learning assignments, presentations, practical exercise

Class test, Semester end examinations, Quiz, Assignments, Presentation, Peer Review

 

Exercises given will be based on following topics:

  • Flow control statements (conditional statements and looping structures)
  • Lists, tuples, lists and dictionaries.
  • Functions and string manipulation.
  • Finding descriptive statistics for the input data.
  • Importing numpy and pandas libraries.
  • File operations (Creating, opening ,reading and writing files)

 

ESSENTIAL READINGS: 

SUGGESTED TEXT BOOKS

  1. Madhavan, “Mastering Python for Data Science”, Packt, 2015.
  2. McKinney, Python for Data Analysis. O’ Reilly Publication, 2017.
  3. Miller, Curtis. Hands-On Data Analysis with NumPy and Pandas: Implement Python Packages from Data Manipulation to Processing. United Kingdom: Packt Publishing, 2018.

 

REFERENCES: 

SUGGESTED REFERENCE BOOKS

  1. Bhasin, Harsh. Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python. India: Manish Jain, 2020.

e-RESOURCES:

  1. NOC: Python for Data Science, IIT Madras ,https://nptel.ac.in/courses/106106212
  2. https://www.w3schools.com/python/default.asp
  3. https://jupyter.org/

JOURNALS:

  1. Journal of Machine Learning Research (JMLR),ACM, https://dl.acm.org/journal/jmlr
  2. International Journal of Machine Learning and Cybernetics, springer : https://www.springer.com/journal/13042

 

Academic Year: 
2024-25
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