A curated collection of the best free resources for supply chain professionals who work with data: R, Python, forecasting, operations, and AI. Everything here is free to read, and every link was checked in August 2026.

Last updated: August 2026

Jump to a section: Learning R · Data Wrangling · Data Visualization · Interactive Dashboards · Forecasting & Time Series · Python for SCM · Supply Chain & Operations · Statistics & ML · AI & Large Language Models

R Basics & Learning R

Start with the first entry. It teaches the modern tidyverse workflow this blog’s code is written in.

• R for Data Science (2nd Edition): Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund. The essential introduction to modern R and the tidyverse: importing, tidying, transforming, visualizing, and modeling data.
• Hands-On Programming with R: Garrett Grolemund. A beginner-friendly introduction built on hands-on projects: functions, data structures, and simulation.
• R Cookbook (2nd Edition): JD Long, Paul Teetor. Over 275 practical recipes for data analysis, statistics, and graphics.
• Advanced R (2nd Edition): Hadley Wickham. R internals for when the basics stop being enough: environments, functional programming, metaprogramming, and performance.
• R Packages (2nd Edition): Hadley Wickham, Jennifer Bryan. How to create, test, document, and share your own R packages.
• Big Book of R: Oscar Baruffa (curator). A directory of 400+ free R books organized by topic. The master index of free R resources.

Data Wrangling & Tidyverse

• R for Data Science: Transform Section: The best starting point for dplyr, tidyr, readr, stringr, and the pipe operator.
• Tidyverse Skills for Data Science: Carrie Wright, Shannon Ellis, Stephanie Hicks, Roger Peng. The Johns Hopkins course covering the tidyverse end to end.
• Learn the Tidyverse: Official curated resources from the tidyverse maintainers: books, cheatsheets, and tutorials for each package.
• R Markdown: The Definitive Guide: Yihui Xie, J.J. Allaire, Garrett Grolemund. The reference for reproducible reports and documents.

Data Visualization

The first two teach ggplot2. The last three help you pick the right chart before you write any code.

• ggplot2: Elegant Graphics for Data Analysis (3rd Edition): Hadley Wickham, Danielle Navarro, Thomas Lin Pedersen. The reference on the grammar of graphics in R.
• R Graphics Cookbook (2nd Edition): Winston Chang. 150+ practical recipes for publication-quality graphs with ggplot2.
• The R Graph Gallery: Hundreds of charts with full reproducible R code, organized by chart type. Find the chart you want, copy the code, adapt.
• From Data to Viz: A decision tree that leads from your data’s format to sensible chart types, with the common mistakes for each one.
• Financial Times Visual Vocabulary: The FT graphics desk’s poster of chart types grouped by what you want to show: deviation, correlation, ranking, flow.

Interactive Dashboards

• Mastering Shiny: Hadley Wickham. Interactive web apps in R: reactive programming, modules, testing, and deployment.
• Interactive Web-Based Data Visualization with R, plotly, and Shiny: Carson Sievert. Interactive charts, maps, 3D plots, animations, and linked views with plotly.
• htmlwidgets for R: Framework and gallery for 50+ interactive widget packages (Leaflet, DT, dygraphs, networkD3, and more).
• Quarto Dashboards: Official guide for building dashboards in Quarto, the modern successor to flexdashboard.
• Shiny Official Site: Documentation, tutorials, gallery, and deployment guides for Shiny.
• Quarto Guide: Full documentation for Quarto, Posit’s next-generation publishing system (successor to R Markdown).

Forecasting & Time Series

The subject closest to this blog’s heart. If you only ever read one link on this page, read the first one.

• Forecasting: Principles and Practice (3rd Edition): Rob J. Hyndman, George Athanasopoulos. The gold-standard forecasting textbook: ETS, ARIMA, dynamic regression, and hierarchical forecasting with the tidyverts ecosystem. Most of this blog’s forecasting series walks chapters of this book.
• Hyndsight: Rob J. Hyndman’s blog. Short, opinionated posts on forecasting practice from the field’s most cited author.
• Makridakis Open Forecasting Center: Home of the M competitions, the empirical benchmark for what actually works in forecasting since 1982.
• M5 Forecasting Dataset (Kaggle): 42,840 real Walmart daily sales series. The best free dataset for practicing retail demand forecasting at scale.
• A Little Book of R for Time Series: Avril Coghlan. A concise, beginner-friendly introduction: decomposition, exponential smoothing, and ARIMA basics.

Python for Supply Chain Analytics

This blog went bilingual in 2026: R where R is strongest, Python where the library you need is Python-native. These are the equivalents of the R canon above.

• Python for Data Analysis (3rd Edition, Open Access): Wes McKinney. The pandas book, by the creator of pandas. Free to read online.
• Forecasting: Principles and Practice, the Pythonic Way: The Python edition of Hyndman and Athanasopoulos’s forecasting textbook, on the same OTexts site.
• Nixtla statsforecast: Fast classical forecasting models (ETS, ARIMA, Theta, and more) with a scikit-learn-style API. Built for running thousands of series at once.
• sktime: The scikit-learn-compatible framework for time series: forecasting, classification, and pipelines.
• pandas Documentation: The official reference, including the “10 minutes to pandas” starter and the user guide.
• scikit-learn User Guide: The official guide to the standard Python ML library. Unusually well written for reference documentation.

Supply Chain & Operations

• Lokad Supply Chain Lectures: Joannes Vermorel’s free video lecture series on quantitative supply chain. Opinionated, rigorous, and unlike anything taught in standard courses.
• MITx MicroMasters in Supply Chain Management: MIT’s graduate-level supply chain courses on edX. The certificate costs money; auditing the course content is free.
• planr: Tools for Supply Chain Management: R package for demand and supply planning: projected inventories, coverages, and replenishment plans at any time bucket granularity.
• SCperf: Supply Chain Performance: R package implementing inventory models (newsboy, reorder point, Wagner-Whitin) and bullwhip effect calculations.

Statistics & Machine Learning

• An Introduction to Statistical Learning (2nd Edition): James, Witten, Hastie, Tibshirani. The most accessible introduction to statistical learning and ML. Free PDF, all examples in R (a Python edition exists on the same page).
• The Elements of Statistical Learning (2nd Edition): Hastie, Tibshirani, Friedman. The advanced companion to ISLR: boosting, SVMs, neural networks, and random forests in depth. Free PDF.
• Tidy Modeling with R: Max Kuhn, Julia Silge. The official guide to the tidymodels framework: preprocessing, fitting, tuning, resampling, and evaluation.
• Modern Statistics with R (2nd Edition): Måns Thulin. A modern statistics textbook in R, from data wrangling to inference, regression, and prediction.
• Supervised Machine Learning for Text Analysis in R: Emil Hvitfeldt, Julia Silge. NLP with tidymodels: tokenization, embeddings, classification, and regression on text data.
• Text Mining with R: A Tidy Approach: Julia Silge, David Robinson. Tidy text analysis: sentiment analysis, tf-idf, and topic modeling.

AI & Large Language Models

The AI Corner of this blog covers models, agents, and what they mean for supply chain work. These are the sources behind it.

• Best Free Places to Learn AI (Interactive Dashboard): My own filterable collection of 53 free AI learning resources and 14 podcasts, by skill level and format. Built for this blog, updated August 2026.
• 3Blue1Brown: Neural Networks: Grant Sanderson. The best visual explanation of how neural networks and transformers actually work. Watch this before anything else.
• Neural Networks: Zero to Hero: Andrej Karpathy. Build a GPT from scratch, in code, one video at a time. The deep end, worth every hour.
• Anthropic Courses: Free courses from Anthropic on prompt engineering, evaluations, and tool use, as runnable notebooks.
• Hugging Face Learn: Free hands-on courses on LLMs, agents, and deep learning, built around open-weight models.
• Simon Willison’s Weblog: The single best way to keep up with what LLMs can actually do this week, tested hands-on rather than announced.
• Artificial Analysis: Independent benchmarks comparing models on quality, price, and speed. The pricing data behind several of this blog’s AI posts.
• Epoch AI: Research and data on AI trends: compute, training costs, and model capabilities over time.

Found a broken link, or know a free resource that belongs here? Tell me and I will add it.