Programming for Analytics

Paper Code: 
DAC 233
Credits: 
04
Periods/week: 
02
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

DAC233

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
Unit I: 

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

Flowchart based on simple computations, iterations

 

12.00
Unit II: 

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

Loop control statements. 

 

12.00
Unit III: 

Functions & string manipulation

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

12.00
Unit IV: 

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

 

12.00
Unit V: 

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: 
2021-22
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