Python for Data Science

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A brief summary of the topics covered in this course is as below. This is 30 hours course, it is suggested to complete this course in 3 weeks. After the completion of this course, you will have a solid foundation in Python programming. Following that, you need to practice it consistently for a minimum of 6 months.

Setup and Introduction

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1. Intro to Python
2. Setup, Download, Installation Jupyter notebook
3. Comments & Markdown in Jupyter

Basics of Python of Programming

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1. Variable & Variable Types
2. Data Structure (list, dictionary, set, tuple)
3. Operators
4. Conditions
5. Loop
6. Break, Continue
7. Error Handling
8. Debugging
9. Functions

Advance Python Programming

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1. List Comprehensions
2. Parameter Packing
3. Package Management
4. File Handling
   • Working with files
   • Reading and writing files
   • Buffered read and write
   • Other file methods.
   • Logging, debugger
   • Modules and import statements
5. Numpy
   • Numpy – ND array object.
   • Numpy – data types.
   • Numpy – array attributes.
   • Numpy – array creation routines.
   • Numpy – array from existing.
   • Data array from numerical ranges.
   • Numpy – indexing & slicing.
   • Numpy – advanced indexing.
   • Numpy – broadcasting.
   • Numpy – iterating over array.
   • Numpy – array manipulation.
   • Numpy – binary operators.
   • Numpy – string functions.
   • Numpy – mathematical functions.
   • Numpy – arithmetic operations.
   • Numpy – statistical functions.
   • Sort, search & counting functions.
   • Numpy – byte swapping.
   • Numpy – copies &views.
   • Numpy – matrix library.
   • Numpy – linear algebra
6. Class
   • OOPS Basic concepts.
   • Creating classes
   • Pillars of oops
   • Inheritance
   • Polymorphism
   • Encapsulation
   • Abstraction
   • Decorator
   • Class methods and static methods
   • Special (magic/dunder) methods
   • Property decorators – getters, setters, and deletes
7. Introduction to Pandas 

Project & Resources

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1. Resources for practice
2. A Final Project in Python