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This page provides a categorized guide to Data School's blog posts, videos, courses, Jupyter notebooks, and webcast recordings.

• Featured content is highlighted in yellow.
• Paid content is marked with a 💲(everything else is 100% free!)

Learning data science

• How to launch your data science career (with Python): Step-by-step guide
• Can a dentist become a data scientist?: Thoughts about changing careers and what it takes to learn data science
• How to get better at data science: Advice for students, including tons of resources
• Lessons learned from teaching an 11-week data science course: Advice for data science educators
• Should you teach Python or R for data science?: Why I (mostly) prefer Python
• Should I learn pandas before scikit-learn?: Video to help you decide how you should spend your time
• How do I stay up-to-date as a data scientist?: Video listing the sites and people I recommend following
• Find the perfect dataset for your Data Science project: Curated list of sources for unique, high-quality datasets

Python

• 💲 Python Essentials for Data Scientists: 2-hour course that will get you up-to-speed quickly with Python's most important features (beginner level)
• Quick reference to Python: Reference guide to 15 core language features, with a script and Jupyter notebook
• How to use Python's f-strings with pandas: Brief introduction to f-strings
• Make your own private GPT with Python: Learn how to use the power of GPT to interact with your private documents
• Simulate the Monty Hall problem in Python: Solve a classic probability problem
• How to write a great Stack Overflow question: Step-by-step guide
• Write Pythonic Code for Better Data Science: Webcast recording

Python: pandas library (data analysis, manipulation, visualization)

• Easier data analysis in Python with pandas: 37 videos (beginner/intermediate level), with a Jupyter notebook
• My top 25 pandas tricks: Video (intermediate level), with a Jupyter notebook
• Master Python's pandas library with these 100 tricks: Tips and tricks that will save you time and energy every time you use pandas!
• Data science best practices with pandas: Tutorial recording (intermediate level), with a Jupyter notebook
• 💲 Analyzing Police Activity with pandas: Online DataCamp course (intermediate level) with 16 videos and 34 interactive coding exercises, structured like a "case study" in which you answer interesting questions about a real dataset
• How to merge DataFrames in pandas: Video (intermediate level), with a Jupyter notebook
• Should you use "dot notation" or "bracket notation" with pandas?: What's the best way to select a Series from a DataFrame?
• Working with Time Zones & Daylight Saving Time in pandas: Learn how to create "timezone-aware" data so that your dataset cooperates with Daylight Saving Time.
• Best practices with pandas: 10 videos (intermediate level), with a Jupyter notebook
• 9 new pandas updates that will save you time: 2 videos (intermediate level), with Jupyter notebooks
• What's the future of the pandas library?: Discussion of what's coming in version 1.0 and beyond
• Your pandas questions answered!: Webcast recording
• Top 8 resources for learning data analysis with pandas: Other recommended resources
• Visualization with pandas and Matplotlib: Lesson notebook demonstrating different types of plots

Python: Regular expressions

• Building a dataset of Python versions with regular expressions: Learn how to use pandas, requests, and regular expressions as part of a web scraping project
• 💲 Become a Regex Superhero: 3-hour course that will teach you how to solve tricky text problems with regular expressions

Python: Beautiful Soup library (web scraping)

• Web scraping the President's lies in 16 lines of Python: 4 videos (beginner level), with a Jupyter notebook

Python: scikit-learn library (machine learning)

• Introduction to Machine Learning with scikit-learn: 4-hour course covering the fundamentals of Machine Learning in Python (beginner level)
• 50 scikit-learn tips: 3-hour course covering assorted scikit-learn functionality and Machine Learning best practices (intermediate level)
• 💲 Building an Effective Machine Learning Workflow with scikit-learn: 8-hour course covering data preparation, feature selection, hyperparameter tuning, missing value handling, and pipelining (intermediate/advanced level)
• 💲 Machine Learning with Text in Python: 14-hour course covering Natural Language Processing, feature engineering, advanced machine learning techniques, and regular expressions (intermediate/advanced level)
• Machine Learning with Text: Tutorial recording (intermediate level), with Jupyter notebooks
• Answering 59 scikit-learn questions: Webcast recording
• How to update your scikit-learn code for 2018: Guide to updating your scikit-learn code to be compatible with version 0.19.1
• How to encode categorical features with scikit-learn: Video covering how to "dummy" or "one-hot" encode categorical data, with a Jupyter notebook
• Using pandas with scikit-learn to create Kaggle submissions: Video covering the basic workflow for making predictions, with a Jupyter notebook
• Getting started with machine learning in Python: Webcast recording

Machine learning overview

• What is machine learning, and how does it work?: Video covering supervised and unsupervised learning, with a Jupyter notebook
• In-depth introduction to machine learning in 15 hours of expert videos: Videos from Stanford University professors, with slides, R code, and a free PDF book
• Getting started in scikit-learn with the famous iris dataset: Video covering machine learning terminology, with a Jupyter notebook
• How to create useful features for Machine Learning: Introduction to feature engineering
• How do I select features for machine learning?: Video covering feature selection tactics
• Comparing supervised learning algorithms: Comparison chart of eight common models, which is also available as a list of advantages and disadvantages

Machine learning models in Python

• K-nearest neighbors (KNN): Video using scikit-learn, with a Jupyter notebook
• Linear regression: Video using scikit-learn, pandas, and seaborn, with a Jupyter notebook
• A friendly introduction to linear regression: Lesson using Statsmodels and scikit-learn, with a Jupyter notebook
• Example of logistic regression in Python using scikit-learn: Example classification problem, with a Jupyter notebook
• Guide to an in-depth understanding of logistic regression: Lesson using scikit-learn, with a Jupyter notebook
• Decision trees for regression and classification: Lesson notebook using scikit-learn
• Ensembling, bagging, and Random Forests: Lesson notebook using scikit-learn
• Regularized regression and classification: Lesson notebook using scikit-learn
• K-means and DBSCAN clustering: Lesson notebook using scikit-learn

Machine learning model evaluation

• Comparing machine learning models: Video covering train/test split using scikit-learn, with a Jupyter notebook
• Cross-validation: Video covering parameter tuning, model selection, and feature selection using scikit-learn, with a Jupyter notebook
• Efficiently searching for optimal tuning parameters: Video covering grid search and randomized search using scikit-learn, with a Jupyter notebook
• Evaluating a classification model: Video covering classification metrics, confusion matrix, and ROC/AUC using scikit-learn, with a Jupyter notebook
• ROC curves and Area Under the Curve explained: Animated video with transcript
• Simple guide to confusion matrix terminology: Reference guide to classification metrics
• Making sense of the confusion matrix: Video covering the confusion matrix, including advanced topics
• Comparing model evaluation procedures and metrics: Reference guide to three procedures and seven metrics

Other machine learning topics

• Quick reference guide to applying and interpreting linear regression: Step-by-step guide
• Exploring the bias-variance tradeoff: Lesson notebook using Seaborn
• Feature scaling/standardization: Lesson notebook using scikit-learn
• Dummy variables: Video using pandas, with a Jupyter notebook
• Handling missing values: Video using pandas, with a Jupyter notebook

Machine learning applications

• Detecting Fraudulent Skype Users via Machine Learning: Presentation with slides
• My first Kaggle competition: Allstate Purchase Prediction Challenge: Presentation with slides, video, and R code

R: Machine learning models

• Example of linear regression and regularization in R: Example using lasso and ridge regression, with an R Markdown document

R: dplyr package (data analysis, manipulation)

• Hands-on dplyr tutorial for faster data manipulation in R: Video (beginner level), with an R Markdown document
• Going deeper with dplyr: New features in 0.3 and 0.4: Video (beginner/intermediate level), with an R Markdown document

Git and GitHub (version control)

• Git and GitHub videos for beginners: 11 videos (beginner level)
• Step-by-step guide to contributing on GitHub: Learn how to make your first open source contribution
• Git quick reference for beginners: Reference guide to common commands
• Simple guide to forks in GitHub and Git: Visual guide to forking, syncing, and pull requests
• GitHub is "just" Dropbox for Git: High-level explanation of Git and GitHub

Jupyter notebook (coding environment)

• Six easy ways to run your Jupyter Notebook in the cloud: Comparison of free cloud services for running the Jupyter Notebook (no installation required).
• Fly through Jupyter with keyboard shortcuts: Visual guide to the 25 most useful shortcuts for Jupyter Notebook and JupyterLab
• Setting up Python for machine learning: Video covering basic usage of the Jupyter notebook (formerly called the IPython notebook)
• IPython Notebook is now called Jupyter Notebook: Briefly explains the history of the Jupyter notebook
• Reproducibility is not just for researchers: Explains why the Jupyter notebook is a critical tool for data analysis

Other

• Why I offer location-based pricing for my courses: I offer a discount on my courses (up to 85%) to people in 160+ countries, to account for the differences in "purchasing power" across the globe.

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