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FRIENDLY DATA SCIENCE TUTORIALS

R Tutorials

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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Joining data 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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Hyperparameter tuning and model stacking 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 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 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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Building a segmentation 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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Data wrangling 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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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 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 using tidymodels in R

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.

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Product associations with Yule's Q and hierarchical clustering

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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Causal analysis with the causalimpact package in R

In this tutorial we'll cover the theory behind the causalimpact package in R 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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