2026 Python Data Analysis & Visualization Masterclass

Name: 2026 Python Data Analysis & Visualization Masterclass
Rating: 4.7 (4087 reviews)

Pandas, Matplotlib, Seaborn, & More! Analyze Dozens of Datasets & Create Stunning Visualizations

Created byColt Steele

Last updated 1/2026

English

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

Course content

22 sections • 204 lectures • 20h 30m total length

Course Welcome & Curriculum Walkthrough8:23
Join The Community!0:31
What Do You Need To Know To Take This Course?1:49
Downloading The Course Materials IMPORTANT!!2:38
How The Exercises Work2:14

Min & Max5:24
Sum & Count9:00
Explore sum and count on dataframes, using numeric_only to sum numeric columns, inspect totals such as total sales, total bedrooms, and Titanic survival counts, and count present values across datasets.
Mean, Median, & Mode5:35
Learn how to compute mean, median, and mode with data frame methods that calculate these values for every column in a data frame, with examples from houses and Titanic.
Describe With Numeric Values4:23
Describe With Objects (Text) Values7:47
EXERCISE: Basic DataFrame Methods1:45
SOLUTION: Basic DataFrame Methods4:35
Explore basic DataFrame methods in pandas, including min, max, mean, head, mode, and describe, to analyze bestseller data and identify top authors like Jeff Kinney.

Selecting A Single Column7:21
A Closer Look At Series8:31
Explore how a pandas series, a one-dimensional array with axis labels, differs from a data frame and use common methods like sum, min, and max on a column.
Important Series Methods5:10
unique & nunique5:15
nlargest & nsmallest7:15
Selecting Multiple Columns3:42
The powerful value_counts() method8:13
Explore value_counts to count unique values in a series or data frame, yielding a labeled counts series you can sort, ascending, top-n, and plot to reveal distributions.
Using plot() to visualize!10:50
EXERCISE: Series & Plotting2:56
SOLUTION: Series & Plotting8:49

Set_Index Basics9:33
set_index: The World Happiness Index Dataset5:06
setting index with read_csv2:38
sort_values intro3:54
sorting by multiple columns3:05
sorting text columns3:37
Demonstrate how sorting text columns in Pandas handles string comparisons, revealing case sensitivity, and use a key function (often a lambda) to lowercase values before sorting for consistent order.
sort_index2:21
Sorting and Plotting!5:01
Sort values and sort index on series and value counts to shape bar plots. Use Titanic class data and bedroom counts to practice ascending and descending orders.
loc7:50
iloc4:18
loc & iloc with Series5:51
EXERCISE: Indexes & Sorting4:21
Practice indexing, sorting, and retrieval on a Pokemon dataset to compute averages, identify top attackers, and plot attack values, using in-place operations and name-based indexing.
SOLUTION: Indexes & Sorting9:55

Filtering DataFrames With A Boolean Series8:48
Filter dataframes with boolean series in pandas, using comparison operators to select Titanic rows by conditions like sex, survived, or class, via bracket indexing.
Filtering With Comparison Operators8:15
The Between Method3:05
The isin() Method4:07
Combining Conditions Using AND (&)11:52
Combining Conditions Using OR (|)11:08
Bitwise Negation6:56
isna() and notna() Methods3:36
Filtering + Plotting Examples6:01
Filter Titanic data by sex to compare survival rates and visualize with value counts and bar or pie plots; then filter King County real estate for top multimillion-dollar zip codes.
EXERCISE: Filtering1:43
SOLUTION: Filtering Exercise10:37

Dropping Columns6:02
Dropping Rows6:25
Adding Static Columns5:59
Creating New "Dynamic" Columns6:54
Learn to create dynamic columns in pandas by combining siblings and spouses with parents and children for Titanic data, then analyze via sorting and simple plots.
Finding The Highest price/sqft homes4:01
Finding Largest Bitcoin Price Changes5:13
EXERCISE: Adding/Removing Columns & Rows3:18
Import Joe Biden tweets dataset, index by tweet id, drop url and a row, add a user column, compute ratio and interactions, rank ten by ratio and eight by interactions.
SOLUTION: Adding/Removing Columns & Rows5:09

Renaming Columns and Index Labels4:50
Learn to rename columns and index labels in a data frame using a mapper dictionary with the rename method, applying changes via columns or index and optional in-place updates.
The replace() method7:31
Updating Values Using loc[]7:59
Updating Multiple Values Using loc[]4:11
Making Updates With loc[] and Boolean Masks7:54
EXERCISE: Updating Values2:20
SOLUTION: Updating Values Exercise8:21
Demonstrates a pandas solution for updating a Netflix titles dataset: read with pipe separators, set index to show_id, update director and duration, rename columns, and mark favorites.

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 Notebooks
Use Pandas to 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 :)

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.

Who this course is for:

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

2026 Python Data Analysis & Visualization Masterclass

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 16min

Setup & Installation6 lectures • 23min

Working With Jupyter Notebook9 lectures • 44min

Dataframes & Datasets10 lectures • 1hr 2min

Basic DataFrame Methods & Computations7 lectures • 38min

Series & Columns10 lectures • 1hr 8min

Indexing & Sorting13 lectures • 1hr 8min

Filtering DataFrames11 lectures • 1hr 16min

Adding & Removing Columns8 lectures • 43min

Updating Values7 lectures • 43min

Requirements

Description

Who this course is for: