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Programming for Analytics [1]

Paper Code: 
BAC 233
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

This module introduces students to Python and form foundation for further analysis of Datasets.

12.00
Unit I: 
Data Science

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

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

Loop control statements.

12.00
Unit III: 
Functions & string manipulation

Functions & string manipulation

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

12.00
Unit IV: 
Introduction to tuples

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

12.00
Unit V: 
Introduction to 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.

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: 
2019-2020 [2]

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Source URL: https://businessanalytics.iisuniv.ac.in/courses/subjects/programming-analytics

Links:
[1] https://businessanalytics.iisuniv.ac.in/courses/subjects/programming-analytics
[2] https://businessanalytics.iisuniv.ac.in/academic-year/2019-2020