R for Data Science
Hadley Wickham & Garrett Grolemund guide you through importing, wrangling, exploring, modelling and communicating data with R, RStudio and the tidyverse — a complete picture of the data-science cycle.
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The books, references and tools our team relies on for Data Science, statistical learning and Machine Learning. A good starting point for anyone exploring the field.
R
R for Data Science
ggplot2: Elegant Graphics for Data Analysis
A powerful, layered system for publication-quality graphics in R based on the Grammar of Graphics — automatic legends, common scales, smoothers and custom themes.
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Advanced R
An essential reference for intermediate and advanced R programmers: data types, functional programming, metaprogramming and fast, memory-efficient code.
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The R Inferno
A guided tour through the common traps, pitfalls and surprising behaviours of R — and how to avoid them.
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Python
Python for Data Science
Python for Data Analysis
By Wes McKinney, creator of pandas. The nuts and bolts of manipulating, processing, cleaning and crunching data in Python, packed with practical case studies.
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Introducing Python
Bill Lubanovic takes you from the basics to more involved topics, mixing tutorials with cookbook-style recipes — a strong foundation for beginners and newcomers to the language.
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Automate the Boring Stuff with Python
Write programs that do in minutes what would take hours by hand — searching files, scraping the web, editing spreadsheets and PDFs — with no prior programming experience required.
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Machine Learning
Machine Learning & Statistical Learning
Machine Learning: A Probabilistic Perspective
A comprehensive, self-contained introduction to ML using probabilistic models and inference as a unifying approach, with worked examples across many domains.
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An Introduction to Statistical Learning
The practical "how-to" of statistical learning by James, Witten, Hastie & Tibshirani, with hands-on labs — accessible without a heavy maths or CS background.
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Python Machine Learning
A practical tour of the Python ML ecosystem — scikit-learn, TensorFlow and Keras — covering everything from sentiment analysis to neural networks.
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