2023 Python Data Analysis Visualization Masterclass

2023 Python Data Analysis Visualization Masterclass

What you’ll learn

Master Pandas Dataframes and Series
Create beautiful visualizations with Seaborn
Analyze dozens of real-world datasets
Practice with tons of exercises and challenges
Learn the ins and outs of Matplotlib
Organize, filter, clean, aggregate, and analyze DataFrames
Master Hierarchical Indexing
Merge datasets together in Pandas
Create line, bar, box, scatter, pie, violin, rug, swarm, strip, and other plots!
Work with Jupyter Notebooks

Requirements

Basic Python Knowledge (variables, conditionals, etc)

Description

Welcome to (what I think is) the web’s best course on Pandas, Matplotlib, Seaborn, and more! This course will level up your data skills to help you grow your career in Data Science, Machine Learning, Finance, Web Development, or any tech-adjacent field.This is a tightly structured course that covers a ton, but it’s all broken down into human-sized pieces rather than an overwhelming reference manual that throws everything at you at once. After each and every new topic, you’ll have the chance to practice what you’re learning and challenge yourself with exercises and projects. We work with dozens of fun and real-world datasets including Amazon bestsellers, Rivian stock prices, Presidential Tweets, Bitcoin historic data, and UFO sightings.If you’re still reading, let me tell you a little about the curriculum.. In the course, you’ll learn how to:Work with Jupyter NotebooksUse Pandas to read and manipulate datasetsWork with DataFrames and Series objectsOrganize, filter, clean, aggregate, and analyze DataFramesExtract and manipulate date, time, and textual information from dataMaster Hierarchical IndexingMerge datasets together in PandasCreate complex visualizations with MatplotlibUse Seaborn to craft stunning and meaningful visualizationsCreate line, bar, box, scatter, pie, violin, rug, swarm, strip, and other plots!What makes this course different from other courses on the same topics?  First and foremost, this course integrates visualizations as soon as possible rather than tacking it on at the end, as many other courses do.  You’ll be creating your first plots within the first couple of sections!  Additionally, we start using real datasets from the get go, unlike most other courses which spend hours working with dull, fake data (colors, animals, etc) before you ever see your first real dataset.  With all of that said, I feel bad trash talking my competitors, as there are quite a few great courses on the platform 🙂 I think that about wraps it up! The topics in this courses are extremely visual and immediate, which makes them a joy to teach (and hopefully for you to learn).   If you have even a passing interest in these topics, you’ll likely enjoy the course and tear through it quickly.  This stuff might seem intimidating, but it’s actually really approachable and fun! I’m not kidding when I say this is my favorite course I’ve ever made. I hope you enjoy it too.

Overview

Section 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

Section 2: Setup & Installation

Lecture 6 Introducing Jupyter Notebook!

Lecture 7 Mac Installation Walkthrough

Lecture 8 Windows Installation Walkthrough

Lecture 9 “Installing” Pandas & Matplotlib (Mac & Windows)

Section 3: Working With Jupyter Notebook

Lecture 10 Creating Notebooks & Running Cells

Lecture 11 Shutting Down The Notebook Server

Lecture 12 How Cell Output Works

Lecture 13 Command Mode Shortcuts

Lecture 14 Cell Types: Markdown Time!

Lecture 15 Restarting The Kernel

Lecture 16 Viewing The Docs Inside A Notebook

Lecture 17 EXERCISE: Jupyter Notebook

Lecture 18 SOLUTION: Jupyter Notebook

Section 4: Dataframes & Datasets

Lecture 19 Datasets & CSV

Lecture 20 pd.read_csv & DataFrames

Lecture 21 Inspecting DataFrames: head(), tail(), etc.

Lecture 22 DataTypes and info()

Lecture 23 The House Sales Dataset Walkthrough

Lecture 24 The Titanic Passenger Dataset Walkthrough

Lecture 25 Non-comma Separators: Netflix Dataset

Lecture 26 Overriding Headers: Country Population Dataset

Lecture 27 EXERCISE: DataFrames & Datasets

Lecture 28 SOLUTION: DataFrames & Datasets

Section 5: Basic DataFrame Methods & Computations

Lecture 29 Min & Max

Lecture 30 Sum & Count

Lecture 31 Mean, Median, & Mode

Lecture 32 Describe With Numeric Values

Lecture 33 Describe With Objects (Text) Values

Lecture 34 EXERCISE: Basic DataFrame Methods

Lecture 35 SOLUTION: Basic DataFrame Methods

Section 6: Series & Columns

Lecture 36 Selecting A Single Column

Lecture 37 A Closer Look At Series

Lecture 38 Important Series Methods

Lecture 39 unique & nunique

Lecture 40 nlargest & nsmallest

Lecture 41 Selecting Multiple Columns

Lecture 42 The powerful value_counts() method

Lecture 43 Using plot() to visualize!

Lecture 44 EXERCISE: Series & Plotting

Lecture 45 SOLUTION: Series & Plotting

Section 7: Indexing & Sorting

Lecture 46 Set_Index Basics

Lecture 47 set_index: The World Happiness Index Dataset

Lecture 48 setting index with read_csv

Lecture 49 sort_values intro

Lecture 50 sorting by multiple columns

Lecture 51 sorting text columns

Lecture 52 sort_index

Lecture 53 Sorting and Plotting!

Lecture 54 loc

Lecture 55 iloc

Lecture 56 loc & iloc with Series

Lecture 57 EXERCISE: Indexes & Sorting

Lecture 58 SOLUTION: Indexes & Sorting

Section 8: Filtering DataFrames

Lecture 59 Filtering DataFrames With A Boolean Series

Lecture 60 Filtering With Comparison Operators

Lecture 61 The Between Method

Lecture 62 The isin() Method

Lecture 63 Combining Conditions Using AND (&)

Lecture 64 Combining Conditions Using OR (|)

Lecture 65 Bitwise Negation

Lecture 66 isna() and notna() Methods

Lecture 67 Filtering + Plotting Examples

Lecture 68 EXERCISE: Filtering

Lecture 69 SOLUTION: Filtering Exercise

Section 9: Adding & Removing Columns

Lecture 70 Dropping Columns

Lecture 71 Dropping Rows

Lecture 72 Adding Static Columns

Lecture 73 Creating New “Dynamic” Columns

Lecture 74 Finding The Highest price/sqft homes

Lecture 75 Finding Largest Bitcoin Price Changes

Lecture 76 EXERCISE: Adding/Removing Columns & Rows

Lecture 77 SOLUTION: Adding/Removing Columns & Rows

Section 10: Updating Values

Lecture 78 Renaming Columns and Index Labels

Lecture 79 The replace() method

Lecture 80 Updating Values Using loc[]

Lecture 81 Updating Multiple Values Using loc[]

Lecture 82 Making Updates With loc[] and Boolean Masks

Lecture 83 EXERCISE: Updating Values

Lecture 84 SOLUTION: Updating Values Exercise

Section 11: Working With Types and NA Values

Lecture 85 Casting Types With astype()

Lecture 86 Introducing the Category Type

Lecture 87 Casting With pd.to_numeric()

Lecture 88 dropna() and isna()

Lecture 89 fillna()

Lecture 90 EXERCISE: Dealing With NA Values

Lecture 91 SOLUTION: Dealing With NA Values

Section 12: Working With Dates & Times

Lecture 92 Why Dates Matter

Lecture 93 Converting With pd.to_datetime()

Lecture 94 Specifying Fancy Formats With pd.to_datetime()

Lecture 95 Dates and DataFrames

Lecture 96 The Useful dt Properties

Lecture 97 Comparing Dates

Lecture 98 Finding StarLink Flybys In UFO Dataset

Lecture 99 Date Math & TimeDeltas

Lecture 100 Billboard Charts Dataset Exploration

Lecture 101 EXERCISE: Dates & Times

Lecture 102 SOLUTION: Dates & Times

Section 13: Matplotlib

Lecture 103 Intro to Matplotlib

Lecture 104 Our First Matplotlib Plots!

Lecture 105 Do We Need plt.show() ?

Lecture 106 Anatomy of Plots

Lecture 107 Figsize & Plot Dimensions

Lecture 108 Changing Matplotlib Stylesheets

Lecture 109 Line Styles, Colors, Widths, and More!

Lecture 110 Plot Labels & Titles

Lecture 111 Changing X & Y Ticks

Lecture 112 Adding Legends To Plots

Lecture 113 EXERCISE: Matplotlib Challenge #1

Lecture 114 Creating Bar Plots

Lecture 115 Creating Histograms

Lecture 116 EXERCISE: Matplotlib Challenge #2

Lecture 117 Creating Scatter Plots

Lecture 118 Creating Pie Charts

Lecture 119 EXERCISE: Matplotlib Challenge #3

Lecture 120 Working With Subplots

Lecture 121 Putting It All Together

Lecture 122 EXERCISE: Matplotlib Challenge #4

Section 14: Revisiting Pandas Plotting

Lecture 123 A Pandas Plotting Recap

Lecture 124 Changing Pandas Plot Styles

Lecture 125 Adding Labels and Titles to Pandas Plots

Lecture 126 Using rename() When Plotting

Lecture 127 Closer Look at Pandas Bar Plots

Lecture 128 EXERCISE: Pandas Plotting Challenge #1

Lecture 129 Pandas Histograms

Lecture 130 Box Plots

Lecture 131 Pandas Line Plots

Lecture 132 EXERCISE: Pandas Plotting Challenge #2

Lecture 133 Pandas Scatter Plots

Lecture 134 Multiple Plots On The Same Axes

Lecture 135 UFOS Plotting Challenge!

Lecture 136 EXERCISE: Pandas Plotting Challenge #3

Lecture 137 Pandas Automatic Subplots

Lecture 138 Manual Subplots With Pandas

Lecture 139 EXERCISE: Pandas Plotting Challenge #4

Lecture 140 EXERCISE: Pandas Plotting Challenge #5

Lecture 141 Exporting Figures With savefig()

Section 15: Grouping & Aggregating

Lecture 142 Introducing Groupby

Lecture 143 Exploring Groups

Lecture 144 Split-Apply-Combine

Lecture 145 Using The Agg Method

Lecture 146 Agg with Custom Functions

Lecture 147 Named Aggregation

Lecture 148 EXERCISE: Groupby

Lecture 149 SOLUTION: Groupby

Section 16: Hierarchical Indexing

Lecture 150 Groupby With Multiple Columns

Lecture 151 Creating a MultiIndex With set_index

Lecture 152 Sorting A MultiIndex

Lecture 153 Using .loc[] With A MultiIndex

Lecture 154 Cross Sections With The XS Method

Lecture 155 get_level_values()

Lecture 156 Hierarchical Columns

Lecture 157 Stack() and Unstack()

Lecture 158 Plotting With Unstack()

Lecture 159 Grouping By Index

Section 17: Working With Text

Lecture 160 The String Datatype Vs. Object Datatype

Lecture 161 Upper(), Lower(), and Capitalize()

Lecture 162 Indexing String Series With []

Lecture 163 Stripping Whitespace With Strip()

Lecture 164 Splitting Text Values With Split()

Lecture 165 Replacing Portions of Strings With Replace()

Lecture 166 Testing Strings With Contains()

Section 18: Apply, Map, & Applymap

Lecture 167 Applying Functions To Series

Lecture 168 Apply() With Lambdas & Arguments

Lecture 169 Apply() w/ DataFrames: Columns

Lecture 170 Apply() w/ DataFrames: Rows

Lecture 171 The Series Map() Method

Lecture 172 The ApplyMap() Method

Section 19: Combining Series & DataFrames

Lecture 173 Concatenating Series

Lecture 174 Concatenating Series By Index

Lecture 175 Inner vs. Outer Joins

Lecture 176 Concatenating DataFrames By Columns

Lecture 177 Concatenating DataFrames By Index

Lecture 178 The DataFrame Merge() Method

Lecture 179 Merge() w/ Left, Right, Inner, & Outer Joins

Lecture 180 Merge() On and Suffixes Arguments

Section 20: Seaborn

Lecture 181 Intro to Seaborn

Lecture 182 The Helpful load_dataset() method

Lecture 183 Seaborn Scatterplots

Lecture 184 Seaborn Lineplots

Lecture 185 The relplot() Method

Lecture 186 Resizing Seaborn Plots: Aspect & Height

Lecture 187 Seaborn Histograms

Lecture 188 KDE Plots

Lecture 189 Bivariate Distribution Plots

Lecture 190 Rugplots

Lecture 191 The Amazing displot() Method

Section 21: Seaborn Categorical Plots

Lecture 192 Countplot

Lecture 193 Strip & Swarm Plots

Lecture 194 Boxplots

Lecture 195 Boxenplots

Lecture 196 Violinplots

Lecture 197 Barplots

Lecture 198 The Big Boy Catplot Method

Section 22: Controlling Seaborn Aesthetics

Lecture 199 Changing Seaborn Themes

Lecture 200 Customizing Styles with set_style()

Lecture 201 Altering Spines With despine()

Lecture 202 Changing Color Palettes

Beginner Python devs curious about data analysis, data visualization, or data science

Course Information:

Udemy | English | 20h 30m | 11.56 GB
Created by: Colt Steele

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