PROGRAMMING FOR ANALYTICS

Paper Code: 
BAC 233
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

Objective- This module introduces students to Python and form foundation for further analysis of Datasets.

Course Outcomes (COs):

Learning outcome (at course level)

Learning and teaching strategies

Assessment Strategies

Students will be able to:

CO11. Install and run the Python interpreter

CO12.Write python programs using programming and looping constructs to tackle any decision-making scenario.

CO13. Identify and resolve coding errors in a program

CO14. Illustrate the process of structuring the data using lists, dictionaries, tuples and sets.

CO15. Design and develop real-life applications using python

Approach in teaching:

Interactive Lectures, Demonstrations, Group activities

 

Learning activities for the students:

Effective assignments, Giving tasks.

 

Assessment Strategies

Class test, Semester end examinations, Practical Assignments, Individual and group projects

 

 

 

12.00

Data Science, Why Python for Data Science, Jupyter Installation for Python, Features of Python, Python Applications

Flowchart based on simple computations, iterations

 

12.00

Basics of Python: variables, data types, operators & expressions, decision statements.

Loop control statements. 

 

12.00

Functions & string manipulation

Introduction to list: Need, creation and accessing list.Inbuilt functions for lists.

 

12.00

Introduction to tuples, sets and dictionaries: Need, Creation, Operations and in-built functions 

12.00

Introduction to File Handling: need, operations on a text file (creating, opening a file, reading from a file, writing to a file, closing a file)

Reading and writing from a CSV file.

 

Essential Readings: 
  1. Albert Lukaszewski, “MySQL for Python”,Packt Publishing
  2. Madhavan (2015),“Mastering Python for Data Science”,Packt

 

References: 

SUGGESTED READINGS: 

  1. McKinney (2017). Python for Data Analysis. O’ Reilly Publication
  2. Curtis Miller,”Hands-On Data Analysis with NumPy and Pandas” , Packt Publishing

JOURNALS:

  1. https://epjdatascience.springeropen.com/
  2. https://vciba.springeropen.com/
  3. https://appliednetsci.springeropen.com/
  4. https://www.journals.elsevier.com/science-of-computer-programming

E-RESOURCES: 

  1. https://www.w3schools.com/python/
  2. https://www.python.org/
  3. https://pythonprogramming.net/
  4. https://spoken-tutorial.org/tutorial search/?search_foss=Python+3.4.3&search_language=English

 

 

Academic Year: 
2022-2023
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