Python for Data Science

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

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

Basics of Python of Programming

  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

  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
    1. OOPS Basic concepts.
    2. Creating classes
    3. Pillars of oops
    4. Inheritance
    5. Polymorphism
    6. Encapsulation
    7. Abstraction
    8. Decorator
    9. Class methods and static methods
    10. Special (magic/dunder) methods
    11. Property decorators – getters, setters, and deletes
  7. Introduction to Pandas 

Project & Resources

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