2025 Python Data Analysis & Visualization Masterclass from Udemy

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.

What's inside

Learning objectives

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

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

Syllabus

Introduction

Course Welcome & Curriculum Walkthrough

Join The Community!

What Do You Need To Know To Take This Course?

Downloading The Course Materials IMPORTANT!!

How The Exercises Work

Setup & Installation

Introducing Jupyter Notebook!

Mac Installation Walkthrough

Windows Installation Walkthrough

"Installing" Pandas & Matplotlib (Mac & Windows)

Working With Jupyter Notebook

Creating Notebooks & Running Cells

Shutting Down The Notebook Server

How Cell Output Works

Command Mode Shortcuts

Cell Types: Markdown Time!

Restarting The Kernel

Viewing The Docs Inside A Notebook

EXERCISE: Jupyter Notebook

SOLUTION: Jupyter Notebook

Dataframes & Datasets

Datasets & CSV

pd.read_csv & DataFrames

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

DataTypes and info()

The House Sales Dataset Walkthrough

The Titanic Passenger Dataset Walkthrough

Non-comma Separators: Netflix Dataset

Overriding Headers: Country Population Dataset

EXERCISE: DataFrames & Datasets

SOLUTION: DataFrames & Datasets

Basic DataFrame Methods & Computations

Min & Max

Sum & Count

Mean, Median, & Mode

Describe With Numeric Values

Describe With Objects (Text) Values

EXERCISE: Basic DataFrame Methods

SOLUTION: Basic DataFrame Methods

Series & Columns

Selecting A Single Column

A Closer Look At Series

Important Series Methods

unique & nunique

nlargest & nsmallest

Selecting Multiple Columns

The powerful value_counts() method

Using plot() to visualize!

EXERCISE: Series & Plotting

SOLUTION: Series & Plotting

Indexing & Sorting

Set_Index Basics

set_index: The World Happiness Index Dataset

setting index with read_csv

sort_values intro

sorting by multiple columns

sorting text columns

sort_index

Sorting and Plotting!

loc

iloc

loc & iloc with Series

EXERCISE: Indexes & Sorting

SOLUTION: Indexes & Sorting

Filtering DataFrames

Filtering DataFrames With A Boolean Series

Filtering With Comparison Operators

The Between Method

The isin() Method

Combining Conditions Using AND (&)

Combining Conditions Using OR (|)

Bitwise Negation

isna() and notna() Methods

Filtering + Plotting Examples

EXERCISE: Filtering

SOLUTION: Filtering Exercise

Adding & Removing Columns

Dropping Columns

Dropping Rows

Adding Static Columns

Creating New "Dynamic" Columns

Finding The Highest price/sqft homes

Finding Largest Bitcoin Price Changes

EXERCISE: Adding/Removing Columns & Rows

SOLUTION: Adding/Removing Columns & Rows

Updating Values

Renaming Columns and Index Labels

The replace() method

Updating Values Using loc[]

Updating Multiple Values Using loc[]

Making Updates With loc[] and Boolean Masks

EXERCISE: Updating Values

SOLUTION: Updating Values Exercise

Working With Types and NA Values

Casting Types With astype()

Introducing the Category Type

Casting With pd.to_numeric()

dropna() and isna()

fillna()

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers Pandas, Matplotlib, and Seaborn, which are essential tools for data analysis and visualization in various tech fields

Includes a comprehensive introduction to Jupyter Notebooks, which is a standard environment for Python-based data analysis and visualization

Emphasizes practice with exercises and projects using real-world datasets, such as Amazon bestsellers and Bitcoin historic data

Integrates visualization techniques early in the course, allowing learners to create plots and charts from the beginning

Teaches how to manipulate data using Pandas, including organizing, filtering, cleaning, aggregating, and analyzing DataFrames

Requires installing Pandas and Matplotlib, which may require learners to have some familiarity with package management in Python

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in 2025 Python Data Analysis & Visualization Masterclass with these activities:

Review Basic Statistics

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Strengthen your understanding of fundamental statistical concepts. This will provide a solid foundation for data analysis and visualization techniques used in the course.

Browse courses on Descriptive Statistics

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Review key statistical measures like mean, median, and standard deviation.
Practice calculating these measures using sample datasets.
Familiarize yourself with different types of data distributions.

Brush Up on Python Fundamentals

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Reinforce your Python programming skills. This will ensure you can effectively use Pandas, Matplotlib, and Seaborn libraries covered in the course.

Browse courses on Python Programming

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Review basic Python syntax and data structures (lists, dictionaries).
Practice writing simple Python functions.
Work through basic Python tutorials or exercises.

Read 'Python for Data Analysis' by Wes McKinney

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Deepen your understanding of Pandas with a comprehensive guide. This book provides in-depth explanations and practical examples to enhance your data analysis skills.

View Python for Data Analysis on Amazon

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Read the chapters related to Pandas DataFrames and Series.
Work through the examples and exercises in the book.
Experiment with applying the concepts to different datasets.

Four other activities

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Show all seven activities

Analyze and Visualize a Public Dataset

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Apply your newly acquired skills to a real-world data analysis project. This will solidify your understanding of Pandas, Matplotlib, and Seaborn.

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Find a publicly available dataset that interests you.
Use Pandas to clean, filter, and analyze the data.
Create visualizations using Matplotlib and Seaborn to explore the data.
Write a short report summarizing your findings and insights.

Create a Data Visualization Portfolio

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Showcase your data visualization skills by creating a portfolio. This will help you demonstrate your abilities to potential employers or clients.

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Select your best data visualizations from the course and your projects.
Write a brief description for each visualization, explaining the data and insights.
Create a website or use a platform like GitHub Pages to host your portfolio.

Read 'Storytelling with Data' by Cole Nussbaumer Knaflic

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Improve your data storytelling skills. This book will teach you how to create compelling visualizations that effectively communicate your insights.

View Daphne Draws Data: A Storytelling with Data... on Amazon

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Read the chapters on data visualization principles and storytelling techniques.
Analyze examples of good and bad data visualizations.
Apply the principles to your own data visualizations.

Help Others in Online Forums

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Reinforce your understanding by helping others learn. Explaining concepts to others solidifies your own knowledge and identifies areas where you may need further clarification.

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Find online forums or communities related to Python, Pandas, Matplotlib, or Seaborn.
Answer questions from other learners and provide helpful guidance.
Share your knowledge and insights to help others succeed.

Career center

Learners who complete 2025 Python Data Analysis & Visualization Masterclass will develop knowledge and skills that may be useful to these careers:

Data Scientist

The role of a Data Scientist involves analyzing large datasets to extract meaningful insights. This course helps a Data Scientist learn how to use Pandas to manipulate and analyze data, as well as how to create visualizations with Matplotlib and Seaborn. The course covers important topics such as hierarchical indexing and data merging that can be invaluable for someone working in data science. The course also introduces the learner with hands-on experience with handling real, complex datasets.

See salaries and explore the career path for Data Scientist

Data Analyst

A Data Analyst uses tools such as data visualization and analysis to draw conclusions from datasets. This course helps a Data Analyst learn skills to manipulate data with Pandas, generate graphs using Matplotlib and Seaborn, and practice on real-world data. Proficiency in data cleaning, aggregation, and filtering, which are covered in the course, are essential to any analyst. Anyone interested in manipulating and extracting information from data will find this course particularly helpful as it emphasizes practical data skills.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

A Business Intelligence Analyst leverages data to provide insights for business decisions. This course can help an aspiring Business Intelligence Analyst acquire proficiency in utilizing data analysis and visualization tools. The ability to organize, filter, clean, and aggregate data using Pandas, as taught in this course, is paramount for any analyst who wishes to interpret complex datasets. The course's emphasis on visualizing data with Matplotlib and Seaborn further aids in presenting findings clearly.

See salaries and explore the career path for Business Intelligence Analyst

Financial Analyst

A Financial Analyst examines financial data to guide investment decisions. This course is beneficial for a Financial Analyst because it provides a practical framework for analyzing real-world data. The course teaches how to manipulate data with Pandas and create visualizations using Matplotlib and Seaborn which are skills that are relevant to the work of a financial analyst. The ability to work with time series data, which is included in the course, can also help an analyst who is working with stock prices or other financial data.

See salaries and explore the career path for Financial Analyst

Market Research Analyst

A Market Research Analyst interprets consumer and market data. This course is a great way for a Market Research Analyst to get up to speed on powerful data analysis techniques. The ability to read, manipulate, and analyze data with Pandas, as well as create clear visualizations with Matplotlib and Seaborn, are essential for analysts who want to extract valuable insights from market trends and consumer behavior. The skills taught in this course allow an analyst to make data driven recommendations.

See salaries and explore the career path for Market Research Analyst

Research Analyst

A Research Analyst gathers and analyzes data to support research projects. A research analyst may find this course useful as it helps the learner develop skills in data manipulation and visualization. The course teaches how to process and analyze data with Pandas, how to use Matplotlib and Seaborn to present findings graphically, and how to work with real-world datasets. The practical approach to data handling provided in this course is especially well suited for research related work.

See salaries and explore the career path for Research Analyst

Quantitative Analyst

A Quantitative Analyst, often called a quant, uses mathematical and statistical methods to develop trading strategies. This course can help a quantitative analyst build a foundation in data handling and visualization using Pandas, Matplotlib, and Seaborn. The course's practical exercises with real-world datasets, including financial data, provide a useful introduction to data analysis techniques and time series, both of which are essential for any quantitative role. This course allows for the learner to gain experience cleaning, managing, manipulating, and visualizing data.

See salaries and explore the career path for Quantitative Analyst

Operations Analyst

An Operations Analyst improves organizational efficiency by analyzing operational data. This course may help an Operations Analyst learn the technical aspects of analyzing operational data. The course focuses on manipulating data with Pandas, and generating meaningful visualizations with Matplotlib and Seaborn. The course's emphasis on practical data analysis and real-world datasets can help an analyst draw meaningful insights from the data regarding operations and improve efficiency.

See salaries and explore the career path for Operations Analyst

Bioinformatician

A Bioinformatician analyzes biological data using computational tools. This course may be helpful for a Bioinformatician because it introduces the use of powerful tools for data analysis and visualization. The course's focus on Pandas for data manipulation, and Matplotlib and Seaborn for visualizations, aligns with the needs of bioinformaticians who are often working with large datasets. Skills like organizing and filtering data are essential to analyzing biological datasets.

See salaries and explore the career path for Bioinformatician

Web Analyst

A Web Analyst examines website data to understand user behavior and optimize website performance. This course may be helpful for a Web Analyst as it helps the learner learn to efficiently process and understand data using Pandas and to visualize trends and statistics with Matplotlib and Seaborn. The course's practical approach can help a web analyst interpret metrics and create reports that improve user engagement.

See salaries and explore the career path for Web Analyst

Statistician

A Statistician collects and interprets numerical data to assist in problem-solving. This course may be useful for a Statistician as it introduces skills in data handling and visualization. The course emphasizes the use of Pandas to efficiently manipulate and prepare datasets for statistical analysis, and the use of Matplotlib and Seaborn to visualize results. The course also covers how to work with real-world datasets, which helps build a foundation for statistical analysis work.

See salaries and explore the career path for Statistician

Machine Learning Engineer

A Machine Learning Engineer develops and implements machine learning models. This course may be useful for an aspiring Machine Learning Engineer as it provides a foundation in data manipulation and visualization. The course's focus on Pandas and data cleaning, along with visualization tools such as Matplotlib and Seaborn, helps build a foundation for more advanced machine learning tasks. Although the course does not go into machine learning, a solid understanding of how to work with data is essential for such work.

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Database Administrator

A Database Administrator manages and maintains databases to ensure data availability and integrity. This course may be helpful, although it does not focus on database management directly. The course teaches how to manipulate data using tools like Pandas, which are important for interacting with and understanding the structure of datasets. While not a primary focus of the role, an understanding of data types, filtering, and aggregation, as covered in this course, can contribute to better database management decisions.

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Consultant

A Consultant advises clients on how to improve their business performance. This course may be helpful to a consultant because it helps build a foundation in data analysis that can be applied to different consulting projects. The course teaches how to use Pandas to manage data, and how to use Matplotlib and Seaborn to communicate their findings. These data skills may be useful for a consultant to better understand a client's position and to offer data backed recommendations.

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Project Manager

A Project Manager oversees projects from start to finish and often analyzes various project related data. This course may be helpful to a Project Manager because data analysis and visualization can help provide insight into project performance. The course utilizes tools such as Pandas, Matplotlib, and Seaborn to aid in this process. Although data analysis is not the primary focus of a Project Manager, the skills taught in this course can help improve performance.

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Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in 2025 Python Data Analysis & Visualization Masterclass.

Python for Data Analysis

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Is written by the creator of the Pandas library. It provides a comprehensive guide to using Pandas for data manipulation, analysis, and cleaning. It valuable reference for anyone working with data in Python and provides additional depth to the Pandas sections of this course. This book is commonly used as a textbook at academic institutions.

Python for Data Analysis

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Python for Data Analysis

Kindle Edition

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Daphne Draws Data

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Focuses on the principles of effective data visualization and communication. It teaches you how to create compelling visuals that tell a story and convey insights clearly. This book is more valuable as additional reading than it is as a current reference. It adds breadth to the visualization sections of this course.

Daphne Draws Data: A Storytelling with Data...

Hardcover

2025 Python Data Analysis & Visualization Masterclass

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What's inside

Learning objectives

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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