Syllabus
Discipline Specific Electives: Business Analytics
Unit 1
Basic concepts in Python: python runtime environment, python variables, basic operators, understanding Python blocks, python data types, declaring and using numeric data types and functions. Conditional statements and loop statements in Python.
Unit 2
Python Complex data types: Strings and string functions, List and Tuple manipulation, Dictionary and Set operations.
Unit 3
Functions and modules in Python: defining functions, scope, types of arguments, the anonymous function (lambda), map, filter, reduce, and zip functions, Introduction to Python modules, and creating own modules.
Unit 4
Exception handling in Python. Python File Operations: Reading and writing files in Python and Python directories. Object-oriented programming in Python: Defining classes and instantiating objects. Python Constructors and destructors. Inheritance and polymorphism in Python.
Unit 5
Fundamentals for data science: Introduction to Jupyter Notebook, Programming using Numpy, Pandas, and Matplotlib libraries.
Objectives and Outcomes
Objective:
This course helps students understand Python programming for data analytics Course Outcome
CO1: Demonstrate an understanding of the fundamental concepts of Python programming language. Create and implement Python programs using core data structures like lists, dictionaries, and regular expressions.
CO2: Demonstrate proficiency in Python data types, control structures, and functions.
CO3: Evaluate different Python libraries and modules and apply them to solve complex computational problems.
CO4: Analyse solutions using Python programming paradigms for different locations using Python modules.
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COPO
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PO1
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PO2
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PO3
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PO4
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PO5
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PO6
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PO7
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PO8
|
PO9
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PO10
|
PO11
|
PO12
|
|
CO1
|
3
|
2
|
3
|
1
|
1
|
1
|
1
|
1
|
2
|
3
|
2
|
2
|
|
CO2
|
3
|
2
|
3
|
1
|
1
|
1
|
1
|
1
|
2
|
3
|
2
|
2
|
|
CO3
|
3
|
3
|
3
|
2
|
1
|
1
|
1
|
2
|
2
|
3
|
3
|
2
|
|
CO4
|
3
|
3
|
3
|
2
|
1
|
1
|
1
|
2
|
2
|
3
|
3
|
2
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