Programming for Analytics

Paper Code: 
BAC 233
Credits: 
2
Contact Hours: 
1.00
Max. Marks: 
100.00
Objective: 

The course will enable the students to

  1. To Understanding the existing working environment and acquire an in-depth technical knowledge of the domain.
  2. To study and learn programming concepts using PYTHON
  3. To Design and develop real-life applications using python

 

Understanding the existing working environment and acquire an in-depth technical knowledge of the domain.

 

Course

Learning outcome (at course level)

Learning and teaching strategies

Assessment Strategies

Paper Code

Paper Title

BAC233

Programming for Analytics

 

Students will:

1)Install and run the Python interpreter

2) Write python programs using programming and looping constructs to tackle any decision-making scenario. 3)Identify and resolve coding errors in a program

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

5) 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: 
  • Albert Lukaszewski, “MySQL for Python”, Packt Publishing
  • Madhavan (2015), “Mastering Python for Data Science”,Packt
  • McKinney (2017). Python for Data Analysis. O’ Reilly Publication
  • Curtis Miller,”Hands-On Data Analysis with NumPy and Pandas” , Packt Publishing
Academic Year: 
2021-2022
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