Python for Data Science Course Outline

Course Outline: Introduction to Python

  • Familiarity with the Python programming language and associated development tools
  • Hands-on experience using various Python data structures, and reading and writing files in Python
  • Experience with Object-Oriented design in Python, including polymorphism
  • Exposure to Python tools and techniques used to solve problems in real fields such as data science

Course Description

Python is an open source, versatile cross-platform programming language that has a strong presence in diverse software engineering disciplines such as data science, web development, network scripting, and embedded systems. While Python itself may be a deceptively simple language, the vast array of frameworks and tools available for use across a variety of specialized fields make it a formidable tool in the arsenal of any technologist with areas of focus from Machine Learning to Cybersecurity.

This course will provide a pragmatic and hands-on introduction to the Python programming language, with a focus on practical applications and projects, rather than theoretical topics. Students will understand the fundamental concepts required to become a good Python programmer. Throughout the course, they will be exposed to fundamental concepts of Python which shall be covering data structures, file handling and object-oriented programming among others.


  • Self Paced Learning: 10-12 hours
  • Instructor Led Sessions: 4 hours


Module 1 Python Programming

  • Introduction
  • Python Execution Model
  • Installing python for Windows
  • Working with a Jupyter Notebook
  • Python IDEs and Text Editors

Getting Started

  • Variables, Expressions and Statements
  • Values and Data Types
  • Operators and Operand
  • Escape Sequences
  • Python Strings
  • Triple Quoted Strings
  • Getting Input from User
  • Selection Statements
  • If Statements
  • Nested Conditionals
  • Repetition Statements
  • Augmented Statements

File Handling and Exceptions

  • Introduction
  • Files
  • Standard File Objects
  • Text-File Processing
  • The with Statement
  • Built-in Function open
  • os Module File Processing Functions
  • Handling Exceptions
  • Division by Zero
  • Invalid Input
  • try Statements
  • try Clause
  • except Clause
  • else Clause
  • finally Clause
  • Explicitly Raising an Exception

Module 2 Python Programming


  • Function Definition
  • Functions with multiple parameters
  • Defining a custom Function
  • Calling a Function
  • Defining a Function with an Arbitrary Argument List
  • Methods
  • Scope Rules
  • Passing Arguments to Functions

Python Data Structures

  • Lists
  • Tuples
  • Unpacking Sequences
  • Sequence slicing
  • del Statement
  • Passing lists to Functions
  • List Comprehensions
  • Filter, Map and Reduce
  • 2-D Lists
  • Dictionary
  • Sets

Array Oriented Programming with Numpy

  • Array attributes
  • Filling Arrays with specific values
  • Universal Functions
  • Views
  • Reshaping and Transposing

Strings – A Deeper Look

  • Formatting Strings
  • Concatenating and Repeating Strings
  • Stripping Whitespace
  • Changing character case
  • Comparison operator for strings
  • Searching for Substrings
  • Splitting and Joining strings
  • Raw Strings

Object Oriented Programming

  • Introduction
  • Custom Class Account
  • Properties for Data Access
  • Inheritance: Base Classes and Subclasses
  • Operator Overloading
  • Named Tuples
  • Namespaces and Scopes

Note: Practice Jupyter Notebook shall be provided for practice with each module