Data School offers three free Data Science courses:
All courses include a certificate of completion and lifetime access!
Learn more about each course below π
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
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.
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!
This is not just a laundry list of "how to" items, but the fundamentals of how to effectively use pandas.
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
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!
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! π