PROBLEM SOLVING WITH PYTHON PROGRAMMING

Paper Code: 
CBDA 112
Credits: 
3
Periods/week: 
6
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 (COs). 

Course Outcome (at course level)

Learning and teaching strategies

Assessment Strategies

On completion of this course, the students will:

CO6. Install and run the Python interpreter.

CO7. Implement Conditionals and Loops for Python Programs

CO8. Create, Test and Debug Python Programs.

CO9. Use functions and represent compound data using Lists, Tuples, sets and Dictionaries.

CO10. Read and write data from & to files in Python

Approach in teaching:

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

E-RESOURCES:

 

JOURNALS:

  • Journal of Machine Learning Research (JMLR),ACM, https://dl.acm.org/journal/jmlr
  • International Journal of Machine Learning and Cybernetics, springer : https://www.springer.com/journal/13042
Academic Year: 
2023-24
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