Department Events
PYTHON PROGRAMMING
Course Objectives:
This Course enables the students to
- Define the basic concepts of python programming.
- Understand the concepts of python functions and its uses.
- Learn different python libraries and their functionalities
- Working with data files and analysing results
Course | Course Outcomes (at course level) | Learning and teaching strategies | Assessment Strategies | |
Course Code | Course title | |||
24SCAI 501 | 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: Interactive Lectures, Discussions, Demonstrations
Learning activities for the students: Self-learning assignments, Practical questions
| Assessment Strategies Class tests, Semester end examinations, Quizzes, Assignments, Presentations, Individual and group projects
|
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
Suggested Text Books:
- Goodrich,Michael, “Introduction to Computing and Problem Solving Using Python”, WILEY, 2016
- Brown,Martin C.”The Complete Reference Python : Indian Edition”, McGraw Hill Education (India) Private Limited, 2018
Suggested Reference Books:
1. McKinney (2017). Python for Data Analysis. O’ Reilly Publication
2. Madhavan (2015),“Mastering Python for Data Science”,Packt
Reference Journals:
1. https://vciba.springeropen.com/
2. https://appliednetsci.springeropen.com/
3. https://epjdatascience.springeropen.com/
e-Resources including links
1. https://www.python.org/downloads/
2. https://jupyter.org/
3. https://www.jigsawacademy.com/blogs/business-analytics/
4. https://nptel.ac.in/courses/106106182
