“One way to learn a lot of mathematics is by reading the first chapters of many books.” -- Paul R. Halmos
In this project, we maintain a list of books that we wish to translate to Clojure, mostly around data and science.
A few of us at Scicloj have have started looking into translating books again.
There are quite a few great books for data and science written with another programming language, where the license allows converting them to Clojure and adapting the text.
This is discussed at the Clojurians Zulip chat at the revived topic thread #data-science > translating a book.
These days we are figuring out a workflow for writing books as literate (and testable!) Clojure namespaces. It looks promising.
If you are a Clojurian looking to learn statistics, machine learning, data visualization, or some related algorithms, it can be a good pathway for a steady and insightful process. We can pick one of the relevant books or part of it and start a journey. We will help you out, and we can pair up and explore together.
Please reach out.
Various licenses of books' text and code determine the ways we may use them as inspiration for Clojure books.
- MIT - permissive
- BSD-3 - permissive
- CA BY-SA 3.0 - free to adapt
- CC BY-NC 4.0 - free to adapt
- CC BY-NC-SA 4.0 - free to adapt
- CC BY-NC-ND 3.0 - no derivatives allowed
- GNU GPL 2 - adaptation and reuse should also be GPL, which means some users will have to avoid using the code
- GNU GPL 3 - similar to GNU GPL 2
This is currently an unordered list, collected from Zulip discussions.
| Book | Authors | Source | License | Comments | Translation status |
|---|---|---|---|---|---|
| Python Data Science Handbook | Jake VanderPlas | jakevdp/... | text - CC BY-NC-ND 3.0; code - MIT | started at scicloj-data-science-handbook (2021), recently restarted at python-data-science-handbook-in-clojure | |
| R for Data Science 1st ed. | Hadley Wickham and Garrett Grolemund | ? | CC BY-NC-ND 3.0 | started some drafts in the past | |
| R for Data Science 2nd ed. | Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund | hadley/r4ds | CC BY-NC-ND 3.0 | permission from the authors & publisher | |
| Introduction to Data Science | Rafael A Irizarry | rafalab/dsbook-part-1 | CC BY-NC-SA 4.0 | The author's earlier book has mixed reviews | |
| Advanced Data Science | Rafael A Irizarry | rafalab/dsbook-part-2 | CC BY-NC-SA 4.0 | ||
| Clojure for Data Scince | Henry Harner | - | nonfree | Great teaching, outdated use of libraries, can be a great source of inspiration | |
| IPSUR: Introduction to Probability and Statistics Using R | G. Jay Kerns | gjkerns/IPSUR | GNU GPL 3 | great teaching of probability contepts through code (dedicated package) | |
| Think Stats 2e | Allen B. Downey | AllenDowney/ThinkStats2 | CC BY-NC-SA 4.0, but the repo is GPL3 | The author has agreed we'd convert it. | 1e has been partially converted by differen people; Karthik is looking into 2e; |
| Modern Statistics with R | Måns Thulin | mthulin/mswr-book | CC BY-NC-SA 4.0 | RMarkdown, notebook conversion can be partially automated | Carsten, generateme, Daniel independently started working on some parts |
| Regression and Other Stories (online PDF) | Andrew Gelman, Jennifer Hill, Aki Vehtari | examples in various languages and libraries | the tidyverse port by Bill Behrman is CC BY-NC-SA 4.0 | discussions: Learn Bayes Stats: #20 & #106 | |
| Time Series Forecasting in Python | Marco Peixeiro | marcopeix/... | code - Apache 2.0 | Amer started - xfthhxk/time-series-analysis | |
| Bayesian Methods for Hackers | Cameron Davidson Pilon | CamDavidsonPilon/... - original version (using PyMC3) | MIT | Conversions to other Bayesian libraries exist, e.g. to R + Stan by Josh Duncan | |
| Think Bayes 2e | Allen B. Downey | AllenDowney/ThinkBayes2 | MIT | ||
| Bayesian Modeling and Computation in Python | Osvaldo A Martin, Ravin Kumar, and Lao Junpeng | .../BookCode_Edition1 | text - CC BY-NC-SA 4.0; code - GPL 2 | we had a reading group at the Jointprob community | Alexandru and Daniel are looking |
| Bayesian Data Analysis | Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin | partial (see some Demos links at the book's page) | text - CC-BY-NC 4.0; code - BSD-3 for code | also see the related course by Aki Vehtari | |
| Introduction to Modern Statistics (2e) | Mine Çetinkaya-Rundel and Johanna Hardin | openintrostat/ims | OpenIntro license - CC By-SA 3.0 | WIP; a Quarto book with R Tidyverse code, lots of styling and illustrations | |
| Causal Inference for the Brave and True | Matheus Facure | matheusfacure/... | MIT | ||
| Introduction to Statistical Learning | Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani | ? | GPL? | ||
| The Elements of Statistical Learning | Trevor Hastie , Robert Tibshirani , Jerome Friedman | ? | GPL? | ||
| Machine Learning Foundations | John Krohn | jonkrohn/ML-foundations | MIT | a series of Jupyter notebooks accompanying a video course, offering introductions to core topics at the foundations of ML | |
| First Semester in Numerical Analysis with Julia (pdf) | Giray Ökten | ? | CC BY-NC-SA 4.0 |
- Big Book of R by OScar Baruffa - a large collection of R resources
- Open Intro is a project creating free resources directed at teaching (college, high-school)
- Github project
- OpenIntro License - CC BY-SA 3.0
- Open Textbook Library - a collection of open textbooks