BASIC PROGRAMMING FOR ANALYTICS

Paper Code: 
24MBB322
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

The course will enable students to proficiently utilize Python for data science and analytics, understand foundational mathematical concepts, master Python programming basics, manipulate data structures, handle files, and apply descriptive statistical techniques for data analysis.

Course Outcomes: 

Course

Learning outcome

(at course level)

Learning and teaching

strategies

Assessment Strategies

Course Code

Course Title

24MBB322

Basic Programming For Analytics

(Practical)

 

CO369: Analyze  the                           mathematical

concepts of data science to frame and compute an abstract of the business problem.

CO370: Install and run the Python interpreter.

CO371: Write python programs using programming and looping constructs to tackle any decision-making scenario.

CO372: Identify and resolve coding errors in a program.

CO373: Illustrate the process of structuring the data using lists, dictionaries, tuples and sets.

CO374: Contribute effectively in course-specific interaction

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

 

12.00
Unit I: 
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.

 

12.00
Unit II: 
Basics of Python

variables, data types, operators & expressions, decision statements. Loop control statements.

 

12.00
Unit III: 
Functions and String

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

 

12.00
Unit IV: 
Tuples

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

 

12.00
Unit V: 
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.

*Case studies related to entire topics are to be taught.

 

Essential Readings: 
  • 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)

 

References: 

Suggested readings

  • Curtis Miller, ”Hands-On Data Analysis with NumPy and Pandas",Packt, 2015
  • (Latest editions of the above books are to be referred)

E resources

Journals

 

Academic Year: 
2024-2025
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