Basic Programming for Analytics

Paper Code: 
MBB 127
Credits: 
4
Contact Hours: 
90.00
Max. Marks: 
100.00
Objective: 

Course Objective: This module introduces students to

  1. Data science and mathematical concepts in analytics
  2. Programming concepts and Python programming language for analytics

Course Outcomes (COs):  At the end of this course, a student should be able to:.  

Course

Course outcome (at course level)

Learning and teaching strategies

Assessment Strategies

 

Paper Code-

                                  

Paper Title

CLO 37.Analyze the mathematical concepts of data science to frame and compute an abstract of the business problem.

CLO 38.Install and run the Python interpreter.

CLO 39.Write python programs using programming and looping constructs to tackle any decision-making scenario.

CLO 40.Identify and resolve coding errors in a program.

CLO 41.Illustrate the process of structuring the data using lists, dictionaries, tuples and sets.

CLO 42.Design and develop real-life applications using python.

Approach in teaching:

Interactive Lectures, Group Discussion, Tutorials, Case Study, Demonstration

 

Learning activities for the students:

Self-learning assignments, presentations, practical exercise

Class test, Semester end examinations, Quiz, Assignments, Presentation, Peer Review

MBB 127

Basic

 Programming

 for

Analytics

 

 

18.00

Data Science and Python : Introduction to data science and analytics ,Why Python for analytics, Jupyter Installation for Python, Features of Python, Pandas and npumy library, Python Applications. Flowchart based on simple computations, iterations.
Data Analytics and Mathematical concepts: Sets and their representation, subset, type of set, matrix and its operations, Determinants and properties of determinant.

18.00

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

18.00

Functions and String: Functions & string manipulation. Introduction to list: Need, creation and accessing list. Inbuilt functions for lists.

18.00

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

18.00

File handling: 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.
Descriptive statistics: mean, mode, median, standard deviation , missing values and outliers.

References: 

  • Madhavan, “Mastering Python for Data Science”, Packt, 2015.
  • McKinney, Python for Data Analysis. O’ Reilly Publication, 2017.
  • Curtis Miller, ”Hands-On Data Analysis with NumPy and Pandas",Packt, 2015

(Latest editions of the above books are to be referred)

Academic Year: 
2021-2022
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