PYTHON PROGRAMMING

Paper Code: 
24SCAI301
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

Course Objectives:

This Course enables the students to

  1. Define the basic concepts of python programming.
  2. Understand the concepts of python functions and its uses.
  3. Learn different python libraries and their functionalities
  4. Working with data files and analysing results

 

Course Outcomes: 

Course

Course Outcomes

(at course level)

Learning and teaching strategies

Assessment Strategies

Course

Code

Course

title

24SCAI 301

PYTHON PROGRAMMING

 (Practical)

 

CO1. Identify and run the Python interpreter

CO2. Design python programs using control statements and functions to tackle any decision making scenario.

CO3.Create python program for string manipulation

CO4. Apply data structures (lists, dictionaries, tuples, sets) for solving diverse problems.

CO5. Develop and validate data solutions using files and visualization.

CO6. Contribute effectively in course-specific interaction

Approach in teaching:

Discussions Interactive Lectures, Demonstrations

 

Learning activities for the students:

 

Self-learning assignments, Practical questions

Class test, Semester end examinations, Quiz, Practical Assignments,  Problem Solving Assignments, Presentation

 

Exercises given will be covering entire syllabi as follows:

  • Jupyter Installation for Python, Features of Python, Python Applications
  • Basics of Python: variables, data types, operators & expressions, decision statements.
  • Loop control statements.
  • Functions, Advance features of function
  • Understand the difference between a function and an object.
  • String manipulation
  • Tuples, sets and dictionaries:  Operations and in-built functions
  • Loading data from files, plotting data, Testing and Debugging

 

ESSENTIAL READINGS: 

Suggested Text Books:

  1. Goodrich,Michael, “Introduction to Computing and Problem Solving Using Python”, WILEY, 2016
  2. Brown,Martin C.”The Complete Reference Python : Indian Edition”, McGraw Hill Education (India) Private Limited, 2018

 

REFERENCES: 

Suggested Readings:

  1. Miller, Curtis. Hands-On Data Analysis with NumPy and Pandas: Implement Python Packages from Data Manipulation to Processing. United Kingdom: Packt Publishing, 2018. (Latest editions of the above books are to be referred)

 

e Resources including links:

  1. https://www.jigsawacademy.com/blogs/business-analytics/
  2. NOC: Python for Data Science, IIT Madras ,https://nptel.ac.in/courses/106106212
  3. Python, w3scool, https://www.w3schools.com/
  4. Jupiter :www.jupiter.com
  5. Googlecolab: www.googlecolab.com

 

Reference 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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