Programming for Analytics

Paper Code: 
DAC 233
Credits: 
04
Periods/week: 
02
Max. Marks: 
100.00
Objective: 

Learning outcome (at course level)

Learning and teaching strategies

Assessment Strategies

 
 

Students will be able to:

  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: 

 

Academic Year: 
2020-21
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