Free Data Science courses

Data School offers three free Data Science courses:

All courses include a certificate of completion and lifetime access!

Learn more about each course below πŸ‘‡

  • Free

pandas in 30 days

Learn the fundamentals of data analysis in Python with this FREE 7-hour course!

This is the perfect course for you if:

  • You're new to the pandas library and you want to learn the fundamentals

  • You have some experience with pandas, but you want to fill in the gaps in your knowledge

  • You want to learn the best practices for data analysis with pandas in 2024

Topics covered:

  • Foundational topics:

    • What is pandas?

    • Reading data into pandas

    • Series and DataFrame objects

    • Selecting columns

    • Creating columns

    • Renaming columns

    • Removing columns

    • Filtering rows by one or more criteria

    • Sorting by index or values

    • Methods and attributes

    • Data exploration

  • Intermediate topics:

    • Plotting with pandas

    • Data analysis by category

    • Changing data types

    • String manipulation

    • Working with dates and times

    • Working with categorical data

    • Handling missing values

    • Handling duplicate data

    • Selecting multiple rows and columns

    • In-place operations

    • Using and setting the index

    • Understanding the axis

    • Applying functions

    • Customizing the display

  • Advanced topics:

    • Using a MultiIndex

    • Merging DataFrames

    • Creating pivot tables

    • Reshaping data

    • Reducing memory usage

    • Using multiple aggregation functions

    • Resampling datetime columns

    • Profiling a DataFrame

    • Styling a DataFrame

Join 1,000+ happy students...

E. B. (Senior Data Scientist)

The best video lecture series that I have found. I love how you explain what is going on behind-the-scenes rather than just showing how to write the code.

Nathaniel K.

Your videos were absolutely crucial in helping me understand pandas well enough to take advantage of it for my senior project.

Thank you so very, very much - I may not have completed my project and graduated without your help!

Fred Tsang

This is not just a laundry list of "how to" items, but the fundamentals of how to effectively use pandas.

  • Free

Introduction to Machine Learning with scikit-learn

Learn the fundamentals of Machine Learning in Python with this FREE 4-hour course!

This is the perfect course for you if:

  • You're brand new to Machine Learning

  • You have Machine Learning experience, but you're new to scikit-learn

  • You've used scikit-learn, but you don't really know if you're doing things the "right" way

Topics covered:

  • What is Machine Learning?

  • Why use scikit-learn?

  • Installing scikit-learn & Jupyter notebook

  • Jupyter notebook basics

  • Machine Learning terminology

  • Machine Learning workflow

  • Loading a dataset using pandas

  • Preprocessing categorical features

  • Model training & prediction

  • Regression with Linear Regression

  • Classification with KNN & Logistic Regression

  • Model evaluation with train/test split & cross-validation

  • Metrics for regression & classification

  • Hyperparameter tuning with grid search & randomized search

Join 10,000+ happy students...

Rafael K.

This is the best introduction to Machine Learning I have *EVER* seen. Thank you for fueling my confidence that I can master this subject!

Mo Daghlas

The way you break down steps and deliver new information is fantastic! It doesn’t feel rushed at all, and you take the time to explain all the new terminology, steps and methodology concisely.

Diogo G.

A M A Z I N G ! In one day I've learned what I need to get into Machine Learning in Python and scikit-learn.

  • Free

50 scikit-learn tips

Sharpen your Machine Learning skills with this FREE 3-hour course!

This is the perfect course for you if:

  • You've taken my introductory ML course and you're ready to go deeper into scikit-learn

  • You want to work more efficiently using scikit-learn's latest features

  • You want to learn best practices for Machine Learning code

  • You learn best through short, focused lessons

Topics covered:

  • How to build, evaluate, and tune a Pipeline

  • Two easy ways to visualize a decision tree

  • How to benefit from missing values using a "missing indicator"

  • How to plot an ROC curve in one line of code

  • How to speed up a grid search

  • How to add feature selection to a Pipeline

  • Why you should use scikit-learn (not pandas) for preprocessing

  • How to create an interactive diagram of a Pipeline

  • How to save your best Pipeline for future predictions

  • Why dropping a level when one-hot encoding is usually a bad idea

  • How to create custom transformers for feature engineering

  • Why you should use stratified sampling with train/test split

  • How to build and tune an ensemble of models

  • Why you should try ordinal encoding with tree-based models

  • And much, much more!

Join 3,000+ happy students...

Neil Dias (ML Engineer)

Your new videos are great! I find them as excellent and concise refreshers on ML implementation topics.

Beltran Rovira (Master's student)

Thanks so much for your videos! They have helped me to optimize the Machine Learning workflow and to understand what’s going on underneath the hood!

Lautaro Cisterna (Data Scientist)

This course is a great guide and resource, where you can come back and check how something was done in a very clear and easy way.

πŸ‘‹ Welcome to Data School!

My name is Kevin, and I've taught Data Science in Python to over a million students.

My courses explain data science topics in a clear, thorough, and step-by-step manner.

I'd love to teach you, regardless of your educational background or professional experience.

Thanks for joining me! πŸ™Œ