Course curriculum

  • 1

    Data Science is Easy to Learn with Python

    • Python Is The New King and Pandas Are So Cute
  • 2

    Python Setup

    • Installing Anaconda Distribution For MAC

    • Installing Anaconda Distribution For Windows

    • Installing Python and PyCharm For MAC

    • Installing Python and PyCharm For Windows

    • Installing Jupyter Notebook For MAC

    • Installing Jupyter Notebook For Windows
  • 3

    If there are variables there is Python

    • What is a variable
  • 4

    Math is not so confusing with Python now

    • Numbers and Math Operators with example
  • 5

    Strings

    • String Operations and Useful String Methods

    • Data Type Conversion

    • Exercise : Company Email Generator
  • 6

    Conditionals

    • Conditionals

    • bool() Function

    • Comparison and Logical Operators

    • If Statements

    • Exercise: Calculator

    • Exercise: User Login
  • 7

    Loops

    • Loops

    • While Loops

    • For Loops

    • Range Function

    • Control Statements

    • Exercise: Perfect Numbers

    • Exercise: User Login with Loops
  • 8

    Functions

    • Functions

    • Create A New Function and Function Calls

    • Return Statement

    • Lambda Functions

    • Exercise: Finding Prime Number
  • 9

    Modules

    • Logic of Using Modules

    • How It is Work

    • Create A New Module

    • Exercise: Number Game
  • 10

    Lists

    • Lists and List Operations

    • List Methods

    • List Comprehensions

    • Exercise: Fibonacci Numbers

    • Exercise: Merging Name and Surname
  • 11

    Tuples

    • Tuples
  • 12

    Dictionaries

    • Dictionaries

    • Dictionary Comprehensions

    • Exercise: Letter Counter

    • Exercise: Word Counter
  • 13

    Exceptions

    • What is Exception?

    • Exception Handling

    • Exercise: if Number
  • 14

    Files

    • Files

    • File Operations

    • Exercise: Team Building

    • Exercise: Overlap
  • 15

    Sets

    • Sets and Set Operations and Methods

    • Set Comprehensions
  • 16

    Object Oriented Programming

    • Logic of OOP

    • Constructor

    • Methods

    • Inheritance

    • Overriding and Overloading
  • 17

    Project Project

    • Project: Remote Controller Application
  • 18

    In Foreign Lands: Data Science

    • What Is Data Science?

    • Data Literacy
  • 19

    Using Numpy for Data Manipulation

    • What is Numpy?

    • Array and Features

    • Array Operators

    • Indexing and Slicing

    • Numpy Exercises
  • 20

    Pandas: Using Pandas for Data Manipulation

    • What is Pandas?

    • Series and Features
  • 21

    Data Frame with Pandas

    • Data Frame attributes and Methods Part – I

    • Data Frame attributes and Methods Part – II

    • Data Frame attributes and Methods Part – III

    • Multi Index

    • Groupby Operations

    • Missing Data and Data Munging Part I

    • Missing Data and Data Munging Part II

    • How We Deal with Missing Data?

    • Combining Data Frames Part – I

    • Combining Data Frames Part – II

    • Work with Dataset Files
  • 22

    Data Visualization

    • What is Matplotlib?

    • Using Matplotlib

    • Pyplot – Pylab - Matplotlib

    • Figure, Subplot and Axes

    • Figure Customization

    • Plot Customization
  • 23

    Data Science: Hands-On Projects

    • Analyse Data With Different Data Sets: Titanic Project

    • Titanic Project Answers

    • Project II: Bike Sharing

    • Bike Sharing Project Answers

    • Project III: Housing and Property Sales

    • Answer for Housing and Property Sales Project

    • Project IV : English Premier League

    • Answers for English Premier League Project