2024 Python Data Analysis & Visualization Masterclass

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Welcome & Curriculum Walkthrough

Lecture 2: Join The Community!

Lecture 3: What Do You Need To Know To Take This Course?

Lecture 4: Downloading The Course Materials IMPORTANT!!

Lecture 5: How The Exercises Work

Chapter 2: Setup & Installation

Lecture 1: Introducing Jupyter Notebook!

Lecture 2: Mac Installation Walkthrough

Lecture 3: Windows Installation Walkthrough

Lecture 4: "Installing" Pandas & Matplotlib (Mac & Windows)

Chapter 3: Working With Jupyter Notebook

Lecture 1: Creating Notebooks & Running Cells

Lecture 2: Shutting Down The Notebook Server

Lecture 3: How Cell Output Works

Lecture 4: Command Mode Shortcuts

Lecture 5: Cell Types: Markdown Time!

Lecture 6: Restarting The Kernel

Lecture 7: Viewing The Docs Inside A Notebook

Lecture 8: EXERCISE: Jupyter Notebook

Lecture 9: SOLUTION: Jupyter Notebook

Chapter 4: Dataframes & Datasets

Lecture 1: Datasets & CSV

Lecture 2: pd.read_csv & DataFrames

Lecture 3: Inspecting DataFrames: head(), tail(), etc.

Lecture 4: DataTypes and info()

Lecture 5: The House Sales Dataset Walkthrough

Lecture 6: The Titanic Passenger Dataset Walkthrough

Lecture 7: Non-comma Separators: Netflix Dataset

Lecture 8: Overriding Headers: Country Population Dataset

Lecture 9: EXERCISE: DataFrames & Datasets

Lecture 10: SOLUTION: DataFrames & Datasets

Chapter 5: Basic DataFrame Methods & Computations

Lecture 1: Min & Max

Lecture 2: Sum & Count

Lecture 3: Mean, Median, & Mode

Lecture 4: Describe With Numeric Values

Lecture 5: Describe With Objects (Text) Values

Lecture 6: EXERCISE: Basic DataFrame Methods

Lecture 7: SOLUTION: Basic DataFrame Methods

Chapter 6: Series & Columns

Lecture 1: Selecting A Single Column

Lecture 2: A Closer Look At Series

Lecture 3: Important Series Methods

Lecture 4: unique & nunique

Lecture 5: nlargest & nsmallest

Lecture 6: Selecting Multiple Columns

Lecture 7: The powerful value_counts() method

Lecture 8: Using plot() to visualize!

Lecture 9: EXERCISE: Series & Plotting

Lecture 10: SOLUTION: Series & Plotting

Chapter 7: Indexing & Sorting

Lecture 1: Set_Index Basics

Lecture 2: set_index: The World Happiness Index Dataset

Lecture 3: setting index with read_csv

Lecture 4: sort_values intro

Lecture 5: sorting by multiple columns

Lecture 6: sorting text columns

Lecture 7: sort_index

Lecture 8: Sorting and Plotting!

Lecture 9: loc

Lecture 10: iloc

Lecture 11: loc & iloc with Series

Lecture 12: EXERCISE: Indexes & Sorting

Lecture 13: SOLUTION: Indexes & Sorting

Chapter 8: Filtering DataFrames

Lecture 1: Filtering DataFrames With A Boolean Series

Lecture 2: Filtering With Comparison Operators

Lecture 3: The Between Method

Lecture 4: The isin() Method

Lecture 5: Combining Conditions Using AND (&)

Lecture 6: Combining Conditions Using OR (|)

Lecture 7: Bitwise Negation

Lecture 8: isna() and notna() Methods

Lecture 9: Filtering + Plotting Examples

Lecture 10: EXERCISE: Filtering

Lecture 11: SOLUTION: Filtering Exercise

Chapter 9: Adding & Removing Columns

Lecture 1: Dropping Columns

Lecture 2: Dropping Rows

Lecture 3: Adding Static Columns

Lecture 4: Creating New "Dynamic" Columns

Lecture 5: Finding The Highest price/sqft homes

Lecture 6: Finding Largest Bitcoin Price Changes

Lecture 7: EXERCISE: Adding/Removing Columns & Rows

Lecture 8: SOLUTION: Adding/Removing Columns & Rows

Chapter 10: Updating Values

Lecture 1: Renaming Columns and Index Labels

Lecture 2: The replace() method

Lecture 3: Updating Values Using loc[]

Lecture 4: Updating Multiple Values Using loc[]

Lecture 5: Making Updates With loc[] and Boolean Masks

Lecture 6: EXERCISE: Updating Values

Lecture 7: SOLUTION: Updating Values Exercise

Chapter 11: Working With Types and NA Values

Lecture 1: Casting Types With astype()

Lecture 2: Introducing the Category Type

Lecture 3: Casting With pd.to_numeric()

Lecture 4: dropna() and isna()

Lecture 5: fillna()