Python for Data Science: Numpy and Pandas Libraries for Data

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Environment Preparing for Python

Lecture 3: Python2 VS Python3

Lecture 4: Understanding Data Types in Python

Chapter 2: Python Refresher

Lecture 1: Variables, Operators and Data Types in Python

Lecture 2: String Functions in Python

Lecture 3: Control Flow VS Loops

Lecture 4: Data Structures in Python

Lecture 5: Error Handling in Python

Lecture 6: Functions in Python

Lecture 7: Files and Modules in Python

Chapter 3: Object Oriented Programming (OOP) In Python

Lecture 1: Creating Simple Class.

Lecture 2: Overviewing Constructor.

Lecture 3: Learning How to creating Dunder Methods?

Lecture 4: Learning about Inheritance.

Lecture 5: Knowing What is the Encapsulation?

Lecture 6: Learning also about Multiple Inheritance

Lecture 7: Knowing What is the Overriding?

Lecture 8: Learning about Decorators.

Lecture 9: Learning How to use Build-in Decorators?

Chapter 4: Project Color Choices Game

Lecture 1: Project Walk through

Lecture 2: Project Helpful Notes

Lecture 3: Project Solution

Chapter 5: Project Hangman Game

Lecture 1: Project Walk through

Lecture 2: Project Helful Notes

Lecture 3: Project Solution Part 1

Lecture 4: Project Solution Part 2

Chapter 6: Numpy Python Library

Lecture 1: 1. Numpy Intro

Lecture 2: 2. Numpy.shape & Numpy.size

Lecture 3: 3. Creating Numpy nd arrays using Numpy functions

Lecture 4: 4. Numpy.unique( ) & Array slicing

Lecture 5: 5. Numpy Calculations and Operators.

Lecture 6: 6. Numpy Aggregations

Lecture 7: 7. Numpy Reshape and Transposing

Lecture 8: 8. Numpy Comparing

Lecture 9: 9. Numpy Images processing

Chapter 7: Python Pandas Data Analysis & Visualization

Lecture 1: Installing Jupyter Lab & Pandas

Lecture 2: SQL PostgreSQL Down and install

Lecture 3: Database Creation

Lecture 4: Database Restore

Lecture 5: Using Python Pandas Package to load PostgreSQL the Data Output file

Lecture 6: Fetchmany and Fetchall

Lecture 7: Querying Using Python Panadas

Lecture 8: Pandas methods and functions

Lecture 9: Visualizing Data

Lecture 10: Pandas Data Analysis

Lecture 11: Sampling Error

Chapter 8: Web Scraping & Data Analysis Using Python & SQL.

Lecture 1: How to Scrape a website in Python?

Lecture 2: Scrape a Table inside a Webpage using Pandas and LXML Python Modules!

Lecture 3: Data Visualization of the Scraped Data.

Lecture 4: Save The Scraped Data to a Database.

Chapter 9: Project Google App Data Analysis

Lecture 1: 1. Visual Exploring of Google App Store Data.

Lecture 2: 2. Data Cleaning and Preprocessing of Google App Store Data.

Lecture 3: 3. Data Visualization Techniques.

Lecture 4: 4. Statistical Analysis and Hypothesis Testing.

Lecture 5: 5. Data Storytelling.

Lecture 6: 6. Conclusion.

Chapter 10: Bonus

Lecture 1: Thanks