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FRIENDLY DATA SCIENCE TUTORIALS

Tutorials

Data wrangling in Python 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.

Data wrangling in R with dplyr

Learn how to use the dplyr package in R to manipulate and summarise your data with dplyr's 5 main verbs: select, arrange, filter, mutate and summarise.

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Hyperparameter tuning and model stacking in R with caret and caretEnsemble

Learn how to tune model hyperparameters in caret to improve their performance and also stack models to create super learners.

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Machine learning in R with tidymodels

Learn how to use the tidymodels package to pre-process your data and build regression and classification models using a range of different algorithms.

Market Basket Analysis in R using dplyr

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

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

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

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DBSCAN and t-SNE tutorial to identify credit card fraud using R

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

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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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Market Basket Analysis using SQL

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

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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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An introduction to R

Learn about the history of R, different data types and structures, how to create your own objects and functions and install packages.

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Machine learning in R with caret

Learn how to use caret to perform stratified sampling, pre-process your data ready for modelling, conduct resampling to get robust performance estimates and build regression and classification models.

Feature selection in R with caret

Learn how to perform feature selection using caret's selection by filter, recursive feature elimination, simulated annealing and genetic algorithms.

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Hyperparameter tuning and model stacking in R using tidymodels

Learn how to tune the hyperparameters of your models, find the optimal set of parameters using simulated annealing and stack models into a super learner.

Joining data in R with dplyr

Learn how to merge data with dplyr using dplyr's mutating and filtering joins, set operations and binding functions.

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

Learn how to build a segmentation in R using the k-means algorithm and principal component analysis.

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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.

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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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Joining data with SQL

Learn how to merge data with SQL using inner, left and full joins, set operators like union and union all as well as some tips and ticks for common challenges.

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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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