2024 Python Data Analysis Visualization Masterclass

2024 Python Data Analysis & Visualization Masterclass, available at $109.99, has an average rating of 4.64, with 202 lectures, based on 2882 reviews, and has 22934 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginner Python devs curious about data analysis, data visualization, or data science It is particularly useful for Beginner Python devs curious about data analysis, data visualization, or data science.

Enroll now: 2024 Python Data Analysis & Visualization Masterclass

Summary

Title: 2024 Python Data Analysis & Visualization Masterclass

Price: $109.99

Average Rating: 4.64

Number of Lectures: 202

Number of Published Lectures: 202

Number of Curriculum Items: 202

Number of Published Curriculum Objects: 202

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 Notebooks

Use Pandasto read and manipulate datasets

Work with DataFrames and Series objects

Organize, filter, clean, aggregate, and analyze DataFrames

Extract and manipulate date, time, and textual information from data

Master Hierarchical Indexing

Merge datasets together in Pandas

Create complex visualizations with Matplotlib

Use Seaborn to craft stunning and meaningful visualizations

Create 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 馃檪 

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.

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()

Instructors

Colt Steele
Developer and Bootcamp Instructor

Rating Distribution

1 stars: 15 votes

2 stars: 18 votes

3 stars: 153 votes

4 stars: 797 votes

5 stars: 1899 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!