Python, machine learning and analytics
Data science tutorials you can follow from start to finish.
Step-by-step guides built on code we run ourselves, with the real outputs, so you get the same results on your own machine.
Popular: pandas, SQL, XGBoost, TensorFlow, Power BI
342 real penguins, measured three ways
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New to data science? Start here
Four tutorials, in this order, take you from installing Python to your first model.
- Install Python and Jupyter Notebook
Set up everything you need in one sitting.
- Data manipulation with pandas
Load, filter, group and reshape tables.
- Clean and preprocess data
Missing values, duplicates and outliers.
- Your first model with scikit-learn
Train, test and evaluate a model.
Recently rewritten
Every one rebuilt around code we ran, with the real results.
Data science vs machine learning, on one dataset
The same 6,433 taxi rides, first analysed to explain tipping, then used to predict fares.
How to use APIs to collect data with Python
Requests, JSON, rate limits and pagination, ending with a real dataset of 300 GitHub repositories.
The dark side of big data, tested in Python
32% of people in an anonymous census dataset are one of a kind, and deleting a column does not remove bias.
Data science in logistics, with a routing model
Use cases, then delivery routes with time windows that drive 44% less than a nearest-neighbour plan.
Data science in manufacturing, with a failure model
Use cases, then a predictive maintenance model that caught 89% of machine failures in test data.
Data science in insurance, with a claims model
A Poisson GLM against gradient boosting on 677,991 real motor policies, then a risk-based price.
Free data tools
Converters and calculators that run in your browser. Your data is never uploaded.
Learning paths
Pick a topic and work through it in order.
Python for data analysis
Beginner to intermediate
Machine learning
Intermediate
Deep learning
All levels
Data science by industry
How companies in different fields put data to work.
About Pulse Data Hub
We write practical tutorials on Python, machine learning and analytics. Every tutorial is built around code we run before publishing, so you can follow along and get the same results.
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