Problem Solving with Python Programming

Paper Code: 
25CBDA112
Credits: 
03
Periods/week: 
06
Max. Marks: 
90.00
Objective: 

The course will enable the students to:

1.  Understand concepts of programming in python

2.  Implementing programming concepts using  PYTHON

 

Course Outcomes: 

Course

Learning outcome

(at course level)

Learning and teaching strategies

Assessment

Strategies

Course

Code

Course

Title

 

25CBDA

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  datafiles using  file operations in Python.

CO12 Contribute effectively incourse-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: 

ESSENTIAL READINGS:

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 READINGS:

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: 
2025-26
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