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FRIENDLY DATA SCIENCE TUTORIALS

Python Tutorials

Data wrangling with pandas

Learn how to use the pandas package in python to explore, select, filter and sort your data, create new variables and produce summary statistics.

An introduction to scikit-learn in python

A tutorial on how to use one of the main machine learning packages in python to create and compare powerful predictive models and modelling pipelines.

Product associations with Yule's Q and hierarchical clustering in Python

Learn about the Yule's Q measure of association and use hierarchical clustering to visualise product clusters.

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Building a segmentation in Python with k-means clustering and PCA

Learn how to build a segmentation in Python using the k-means algorithm and principal component analysis from the sklearn package.

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EASE the Embarrassingly Shallow Autoencoder recommender model

Learn how to build the state of the art EASE recommender model with just a few lines of numpy

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Item and User KNN Recommender models with similaripy in Python

Learn how to build KNN recommender models in Python using the similaripy package. Experiment with different similarity measures as well graph based methods that yield state of the at results.

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Feature Selection, Hyperparameter Tuning and Model Stacking with sklearn

Learn how to use the scikit-learn package to perform feature selection, hyperparameter tuning and model stacking. We'll also use SHAP to understand the contribution of individual features in our final model.

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LightFM tutorial building a wine recommender in Python

Learn how to build a hybrid matrix factorisation model to create recommendations in Python with the LightFM package.

DBSCAN and t-SNE tutorial in Python

Use density based clustering in Python to spot credit card fraud and visualise the results with t-SNE.

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Joining data in Python with pandas

Learn how to merge data in Python using the pandas package. Learn how to do joins, filtering and unions as well as some tips and ticks for common challenges.

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Market Basket Analysis in Python with pandas

Learn how to perform market basket analysis using pandas and understand from first principles how the associations are calculated.

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Alternating Least Squares and Bayesian Personalized Ranking

Build a recommendation engine using collaborative filtering, matrix factorisation models Alternating Least Squares and Bayesian Personalized Ranking

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Causal analysis with the tfcausalimpact package in Python

In this tutorial we'll cover the theory behind the tfcausalimpact package in Python and how detecting cause and effect differs from looking at correlations. We'll use real life data to show the impact that a competitor opening had on store sales.

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RP3Beta Recommender Model and Item Similarities in Python

A step-by-step tutorial on how to build a state-of-the-art RP3Beta graph recommender from scratch in Python, understand the maths, calculate item similarity and experiment with tuning the beta parameter to control popularity bias.

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