Python Lab

Paper Code:

25SBCA302

Credits:

Periods/week:

Max. Marks:

100.00

Objective:

Course Objectives:

The course will enable the students to

1. Define the basic concepts of python programming.

2. Understand the concepts of python functions and its uses.

3. Demonstrate the Modules and packages used in Python Programming.

Course Outcomes:

Course

Learning Outcome

(at course level)

Learning and

teaching

strategies

Assessment

Strategies

Course

Code

Course

Title

25SBCA302

Python Lab (Practical)

CO193. Install and run the Python interpreter.

CO194. Write python programs using programming and looping constructs to tackle any decision- making scenario.

CO195. Identify and resolve coding errors in a program

CO196. Illustrate the process of structuring the data using lists, dictionaries, tuples and sets.

CO197. Design and develop real- life applications using python

CO198. Contribute effectively in course- specific interaction.

Approach in teaching:

Interactive Lectures, Discussion, reading assignments, Demonstrations, G-suite.

Self-Learning activities for the students:

Self-learning assignments, Practical questions Seminar presentation.

Class test, Semester end examinations, Quiz, Assignments, Presentation, Individual and group projects

Unit I:

Exercises given will be covering entire syllabi as follows:

Jupiter Installation for Python, Features of Python, Python Applications
Basics of Python: variables, data types, operators & expressions, decision statements.Loop control statements.
Functions
Understand the difference between a function and an object.
String manipulation
Tuples, sets and dictionaries: Need, Creation, Operations and in-built functions

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. https://www.python.org/downloads/

2. https://jupyter.org/

3. https://www.jigsawacademy.com/blogs/business-analytics/

4. https://nptel.ac.in/courses/106106182

5. https://www.geeksforgeeks.org/

JOURNALS:

1. https://vciba.springeropen.com/

2. https://appliednetsci.springeropen.com/