Python Lab

Paper Code: 
24SBCA302
Credits: 
02
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

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

24SBCA

302

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

 

 

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: 
  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/
  3. https://epjdatascience.springeropen.com/

 

Academic Year: 
2024-25
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