{"id":58096,"url":"https://github.com/DataKind-BLR/resources","name":"resources","description":"A bunch of collated useful resources.","projects_count":307,"last_synced_at":"2026-08-04T13:00:22.289Z","repository":{"id":46534029,"uuid":"111935790","full_name":"DataKind-BLR/resources","owner":"DataKind-BLR","description":"A bunch of collated useful resources.","archived":false,"fork":false,"pushed_at":"2022-02-10T14:59:55.000Z","size":62,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":10,"default_branch":"master","last_synced_at":"2026-07-16T08:04:13.142Z","etag":null,"topics":["awesome-list","data-science","links","resources","volunteering"],"latest_commit_sha":null,"homepage":"https://datakind-blr.github.io/resources","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DataKind-BLR.png","metadata":{},"created_at":"2017-11-24T16:05:12.000Z","updated_at":"2023-05-14T09:38:47.000Z","dependencies_parsed_at":"2022-09-07T04:51:37.483Z","dependency_job_id":null,"html_url":"https://github.com/DataKind-BLR/resources","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/DataKind-BLR/resources","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataKind-BLR%2Fresources","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataKind-BLR%2Fresources/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataKind-BLR%2Fresources/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataKind-BLR%2Fresources/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DataKind-BLR","download_url":"https://codeload.github.com/DataKind-BLR/resources/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataKind-BLR%2Fresources/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":36275583,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-07-20T02:08:10.276Z","status":"online","status_checked_at":"2026-08-04T02:00:06.901Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-04-08T00:00:27.751Z","updated_at":"2026-08-04T13:00:22.289Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Datasets","Books","Podcasts","Courses","Cheatsheets","Competitions","Blogs","Start From Here","Tools"],"sub_categories":["Data Lists","Languages","Python Programming","R Programming","Data Repositories","Open Data Portals","Statistics","Machine Learning","Concepts","Databases","Data Science","Mathematics","APIs","Data Visualization","Math","Misc","Frameworks"],"readme":"# Resources\n\nWelcome to resources, we are glad you ended up here. Whether you are looking to begin your contributing journey or you want to master your data science skills or whether you found an issue and want to notify us, this list will help you get onward with your journey.\n\n---\n\n## Start From Here\n  - [Definitive Guide to contributing to open source](https://www.freecodecamp.org/news/the-definitive-guide-to-contributing-to-open-source-900d5f9f2282/)\n  - [Introduction to Gihub and Open Source](https://www.digitalocean.com/community/tutorial_series/an-introduction-to-open-source)\n  - [A great project to being your contribution to open source](https://github.com/danthareja/contribute-to-open-source)\n\n---\n\n\n## Blogs\n  - [Data Science Central](https://www.datasciencecentral.com/)\n  - [Flowing Data](http://flowingdata.com/)\n  - [Stats \u0026 Bots - Data stories on machine learning and analytics](https://blog.statsbot.co/)\n\n---\n\n\n## Tools\n  - [dataviz.tools (a curated guide to the best tools, resources and technologies for data visualization)](http://dataviz.tools/)\n  - [GitHub Desktop](https://help.github.com/desktop/guides/)\n  - [What-If Tool](https://pair-code.github.io/what-if-tool/)\n\n---\n\n\n## Podcasts\n\n### Data Science\n  - [Adversarial Learning](http://adversariallearning.com/)\n  - [Artificial Intelligence in Industry](https://www.techemergence.com/category/primary-content-type/artificial-intelligence-podcast/)\n  - [Becoming a Data Scientist](https://www.becomingadatascientist.com/category/podcast/)\n  - [Data Crunch](http://vaultanalytics.com/datacrunch/)\n  - [Data Science at Home](http://podcast.datascienceathome.com/)\n  - [Digital Analytics Power Hour](http://www.analyticshour.io/)\n  - [Not So Standard Deviations](https://soundcloud.com/nssd-podcast)\n  - [Partially Derivative](http://partiallyderivative.com/)\n  - [O’Reilly Data Show Podcast](https://www.oreilly.com/topics/oreilly-data-show-podcast)\n  - [SuperDataScience](https://www.superdatascience.com/podcast/)\n  - [The All Analytics Podcast](http://allanalytics.libsyn.com/)\n\n### Data Visualization\n  - [Data Stories](http://datastori.es/)\n  - [The Tableau Wannabe Podcast](https://thetwpodcast.blogspot.com/)\n\n### Machine Learning\n  - [Learning Machines 101](http://www.learningmachines101.com/)\n  - [Talking Machines](http://www.thetalkingmachines.com/)\n  - [This Week in Machine Learning \u0026 AI](https://twimlai.com/)\n\n### Python Programming\n  - [Podcast.init](https://www.podcastinit.com/)\n  - [Talk Python To Me](https://talkpython.fm/)\n\n### R Programming\n  - [The R-Podcast](https://r-podcast.org/)\n\n### Statistics\n  - [Data Skeptic](https://dataskeptic.com/podcast)\n  - [Linear Digressions](http://lineardigressions.com/)\n  - [Stats + Stories](http://www.cas.miamioh.edu/statsandstories/archives.html)\n\n---\n\n\n### Frameworks\n  - [Ethics \u0026 Algorithms Toolkit](ethicstoolkit.ai)\n  - [Initializing a Data Science Project / Repository](https://drivendata.github.io/cookiecutter-data-science/)\n\n\n---\n\n\n## Datasets\n\n### APIs\n  - [Data.gov](https://www.data.gov/developers/apis)\n  - [Meta-list](https://github.com/toddmotto/public-apis)\n  - [VISA](https://developer.visa.com/)\n\n### Data Lists\n  - [awesome-public-datasets](https://github.com/awesomedata/awesome-public-datasets)\n  - [KD Nuggets (Datasets for Data Mining and Data Science)](https://www.kdnuggets.com/datasets/index.html)\n  - [Wikipedia (List of datasets for machine learning research)](https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research)\n\n### Data Repositories\n  - Open\n    - [Archive-It](https://www.archive-it.org/)\n    - [Amazon Wev Services (AWS) Public Datasets](https://aws.amazon.com/opendata/public-datasets/)\n    - [Enigma](https://public.enigma.com/)\n    - [Humanitarian Data](https://data.humdata.org/)\n    - [OSDC](https://www.opensciencedatacloud.org/publicdata/)\n    - [Zanran](http://www.zanran.com/q/)\n  - Others\n    - [DatastreamX (Commercial Data)](https://www.datastreamx.com/)\n    - [Factual (Location Data)](https://www.factual.com/)\n    - [Figshare (Research Based)](https://figshare.com/)\n    - [PlanetOS (Renewable Energy Data)](https://planetos.com/)\n\n### Open Data Portals\n\n  - Country-specific / Government Affiliated\n    - [Australia](https://data.gov.au/)\n    - [Berlin, Germany](https://daten.berlin.de/)\n    - [Canada](https://open.canada.ca/en)\n    - [European Data Portal](https://www.europeandataportal.eu/)\n    - [India](https://data.gov.in/)\n    - [Mexico](https://datos.gob.mx/)\n    - [New South Wales](https://data.nsw.gov.au/data/dataset)\n    - [Swiss](https://opendata.swiss/en/)\n    - [United Kingdom](https://data.gov.uk/)\n    - [United States](https://www.data.gov/)\n\n  - Independent\n    - [data.world (Datasets for Analysis \u0026 Download)](https://data.world)\n    - [Datahub](https://datahub.io/)\n    - [Data Portals (A Comprehensive List of Open Data Portals from Around the World)](http://datacatalogs.org/)\n    - [Google Dataset Search](https://toolbox.google.com/datasetsearch)\n    - [Knoema](https://knoema.com/)\n    - [Quandl (Financial Data)](https://www.quandl.com/)\n    - [Socrata](https://opendata.socrata.com/)\n    - [World Bank](https://data.worldbank.org/)\n    - [UN](http://data.un.org/)\n\n---\n\n\n## Courses\n  - [codecademy](https://www.codecademy.com/)\n  - [Coursera](https://www.coursera.org)\n  - [edX](https://www.edx.org)\n  - [Fast.ai](http://course.fast.ai)\n  - [FutureLearn](https://www.futurelearn.com)\n  - [Iversity](https://iversity.org/)\n  - [Lynda](https://www.lynda.com/)\n  - [MIT OCW](http://ocw.mit.edu)\n  - [NPTEL](https://onlinecourses.nptel.ac.in)\n  - [openHPI](https://open.hpi.de)\n  - [Platzi](https://courses.platzi.com)\n  - [Stanford](https://online.stanford.edu/)\n  - [Udacity](https://www.udacity.com)\n  - [Udemy](https://www.udemy.com/)\n\n---\n\n\n## Competitions\n  - [Challenge Data](https://challengedata.ens.fr/en/home)\n  - [crowd.ai](https://www.crowdai.org/)\n  - [Crowd Analytix](https://crowdanalytix.com/community)\n  - [Driven Data](https://www.drivendata.org/)\n  - [Kaggle](https://www.kaggle.com/competitions)\n  - [Numerai](https://numer.ai/homepage)\n\n---\n\n\n## Cheatsheets\n\n### Databases\n  - [MySQL](http://www.dusun-think.net/dosya/czyayin/mysqlcheatsheet.pdf)\n  - [SQL](http://www.sql-tutorial.net/sql-cheat-sheet.pdf)\n\n### Languages\n  - MATLAB\n    - [Basic Functions](http://web.mit.edu/18.06/www/Spring09/matlab-cheatsheet.pdf)\n    - [Statistics \u0026 Machine Learning](https://drive.google.com/file/d/0B5_LfelhJL6rZU9wVnhjclgyclU/view)\n  - Python\n    - Methodologies\n      - [Importing Data](http://datacamp-community.s3.amazonaws.com/50d31142-3de0-4159-89b9-18b718a728ef)\n      - [Jupyter Notebook](http://datacamp-community.s3.amazonaws.com/f9511cf4-abb9-4f52-9663-ea93b29ee4b7)\n      - [Python Basics](http://datacamp-community.s3.amazonaws.com/e30fbcd9-f595-4a9f-803d-05ca5bf84612)\n      - [Python Snippets](https://www.pythonsheets.com/)\n    - Packages\n      - [bokeh (data visualization)](http://datacamp-community.s3.amazonaws.com/f9511cf4-abb9-4f52-9663-ea93b29ee4b7)\n      - [keras](http://datacamp-community.s3.amazonaws.com/94fc681d-5422-40cb-a129-2218e9522f17)\n      - [matplotlib (Plotting)](http://datacamp-community.s3.amazonaws.com/28b8210c-60cc-4f13-b0b4-5b4f2ad4790b)\n      - [numpy (Data Analysis)](http://datacamp-community.s3.amazonaws.com/e9f83f72-a81b-42c7-af44-4e35b48b20b7)\n      - [pandas (Data Wrangling)](http://datacamp-community.s3.amazonaws.com/9f0f2ae1-8bd8-4302-a67b-e17f3059d9e8)\n      - [pySpark (Spark DataFrames / SQL Basics)](http://datacamp-community.s3.amazonaws.com/65076e3c-9df1-40d5-a0c2-36294d9a3ca9)\n      - [pySpark (RDD Basics)](http://datacamp-community.s3.amazonaws.com/4d91fcbc-820d-4ae2-891b-f7a436ebefd4)\n      - [scikit-learn (Machine Learning)](http://datacamp-community.s3.amazonaws.com/5433fa18-9f43-44cc-b228-74672efcd116)\n      - [scipy (Linear Algebra)](http://datacamp-community.s3.amazonaws.com/5710caa7-94d4-4248-be94-d23dea9e668f)\n      - [seaborn (statistical data visualization)](http://datacamp-community.s3.amazonaws.com/f9f06e72-519a-4722-9912-b5de742dbac4)\n  - R\n    - Methodologies\n      - [Advanced R](https://www.rstudio.com/wp-content/uploads/2016/02/advancedR.pdf)\n      - [Basic R](http://github.com/rstudio/cheatsheets/raw/master/base-r.pdf)\n      - [Data Management](https://sites.ualberta.ca/~ahamann/teaching/renr690/R_Cheat_Data.pdf)\n      - [Data Mining](https://cran.r-project.org/doc/contrib/YanchangZhao-refcard-data-mining.pdf)\n      - [Data Wrangling](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf)\n      - [Functions for Regression Analysis](https://cran.r-project.org/doc/contrib/Ricci-refcard-regression.pdf)\n      - [Functions for Time Series Analysis](https://cran.r-project.org/doc/contrib/Ricci-refcard-ts.pdf)\n      - [Graph Sizing](https://github.com/rstudio/cheatsheets/raw/master/how-big-is-your-graph.pdf)\n      - [Importing Data](https://s3-ap-south-1.amazonaws.com/av-blog-media/wp-content/uploads/2017/02/17090748/R_dataimport.pdf)\n      - [Keras](https://github.com/rstudio/cheatsheets/raw/master/keras.pdf)\n      - [LaTeX](https://wch.github.io/latexsheet/)\n      - [Regular Expressions and Pattern Matching Basics](https://www.rstudio.com/wp-content/uploads/2016/09/RegExCheatsheet.pdf)\n      - [R Markdown](https://github.com/rstudio/cheatsheets/raw/master/rmarkdown-2.0.pdf)\n      - [RStudio IDE](https://github.com/rstudio/cheatsheets/raw/master/rstudio-ide.pdf)\n      - [Syntax Comparison](https://github.com/rstudio/cheatsheets/raw/master/syntax.pdf)\n    - Packages\n      - [caret (Modeling and Machine Learning)](https://github.com/rstudio/cheatsheets/raw/master/caret.pdf)\n      - [cartograpy (Thematic Maps with Spatial Objects)](https://github.com/rstudio/cheatsheets/raw/master/cartography.pdf)\n      - [data.table](http://datacamp-community.s3.amazonaws.com/6fdf799f-76ba-45b1-b8d8-39c4d4211c31)\n      - [devtools (Package Development)](https://github.com/rstudio/cheatsheets/raw/master/package-development.pdf)\n      - [dplyr (Data Transformation)](https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf)\n      - [eurostat (eurostat Database)](https://github.com/rstudio/cheatsheets/raw/master/eurostat.pdf)\n      - [ggplot2 (Data Visualization)](https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf)\n      - [h2o (Big Data and Parallel Processing)](https://github.com/rstudio/cheatsheets/raw/master/h2o.pdf)\n      - [leaflet (Interactive Maps)](https://github.com/rstudio/cheatsheets/raw/master/leaflet.pdf)\n      - [mlr](https://github.com/rstudio/cheatsheets/raw/master/mlr.pdf)\n      - [mosaic](https://github.com/mlaviolet/Mosaic-cheatsheets/raw/master/mosaic-cheatsheet-gf.pdf)\n      - [quanteda (Quantitative Analysis of Textual Data)](https://github.com/rstudio/cheatsheets/raw/master/quanteda.pdf)\n      - [randomizr (Random Assignment and Sampling)](https://github.com/rstudio/cheatsheets/raw/master/randomizr.pdf)\n      - [shiny](https://github.com/rstudio/cheatsheets/raw/master/shiny.pdf)\n      - [sparlyr](https://github.com/rstudio/cheatsheets/raw/master/sparklyr.pdf)\n      - [survminer (Survival Plots)](https://github.com/rstudio/cheatsheets/raw/master/survminer.pdf)\n      - [testthat](https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf)\n      - Tidyverse\n        - [lubridate (Dates and Times)](https://github.com/rstudio/cheatsheets/raw/master/lubridate.pdf)\n        - [purrr (Apply Functions)](https://github.com/rstudio/cheatsheets/raw/master/purrr.pdf)\n        - [stringr (String)](http://stringr.tidyverse.org/)\n        - [Summary](http://datacamp-community.s3.amazonaws.com/e63a8f6b-2aa3-4006-89e0-badc294b179c)\n      - [xplain (Statistical functions for XML data)](https://github.com/rstudio/cheatsheets/raw/master/xplain.pdf)\n      - [xts (Time Series)](http://datacamp-community.s3.amazonaws.com/e04c5a6b-4aca-46f5-8cd5-803d975ccc4b)\n\n### Math\n  - [Probabalistic Distributions](http://web.cs.elte.hu/~mesti/valszam/kepletek)\n  - [Probability](https://www.sas.upenn.edu/~astocker/lab/teaching-files/PSYC739-2016/probability_cheatsheet.pdf)\n\n### Misc\n  - [Choosing the Right Estimator](http://scikit-learn.org/stable/tutorial/machine_learning_map/)\n  - [Hive Functions](https://www.qubole.com/resources/hive-function-cheat-sheet/)\n  - [Neural Network Zoo](https://raw.githubusercontent.com/kailashahirwar/cheatsheets-ai/master/Neural%20Networks%20Zoo.png)\n  - [Stanford CS 229 - Machine Learning](https://stanford.edu/~shervine/teaching/cs-229.html)\n\n\n---\n\n\n\n## Books\n\n### Concepts\n\n- Databases\n  - [Big Data Now: Current Perspectives from O'Reilly Radar](http://shop.oreilly.com/product/0636920022640.do)\n  - [Database Explorations](http://www.dcs.warwick.ac.uk/~hugh/TTM/Database-Explorations-revision-2.pdf)\n  - [Database Fundamentals](http://public.dhe.ibm.com/software/dw/db2/express-c/wiki/Database_fundamentals.pdf)\n  - [Databases, Types, and The Relational Model: The Third Manifesto](http://www.dcs.warwick.ac.uk/~hugh/TTM/DTATRM.pdf)\n  - [Foundations of Databases](http://webdam.inria.fr/Alice/)\n  - [Readings in Database Systems, 5th Ed.](http://www.redbook.io)\n  - [Temporal Database Management by Christian S. Jensen](http://people.cs.aau.dk/~csj/Thesis/)\n  - [The Theory of Relational Databases](http://web.cecs.pdx.edu/~maier/TheoryBook/TRD.html)\n  - [What is Database Design, Anyway?](http://www.oreilly.com/data/free/what-is-database-design-anyway.csp)\n- Data Mining\n  - [A Programmer's Guide to Data Mining by Ron Zacharski](http://guidetodatamining.com)\n  - [Data Jujitsu: The Art of Turning Data into Product](http://www.oreilly.com/data/free/data-jujitsu.csp) (email address *requested*, not required)\n  - [Data Mining Algorithms In R - Wikibooks](https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R)\n  - [Internet Advertising: An Interplay among Advertisers, Online Publishers, Ad Exchanges and Web Users](http://arxiv.org/pdf/1206.1754v2.pdf)\n  - [Introduction to Data Science by Jeffrey Stanton](https://docs.google.com/file/d/0B6iefdnF22XQeVZDSkxjZ0Z5VUE/edit?pli=1)\n  - [Mining of Massive Datasets](http://www.mmds.org)\n  - [School of Data Handbook](http://schoolofdata.org/handbook/)\n  - [Theory and Applications for Advanced Text Mining](http://www.intechopen.com/books/theory-and-applications-for-advanced-text-mining)\n- Information Retrieval\n  - [Information Retrieval: A Survey](http://www.csee.umbc.edu/csee/research/cadip/readings/IR.report.120600.book.pdf)\n  - [Introduction to Information Retrieval](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\n- Licensing\n  - [Creative Commons: a user guide by Simone Aliprandi](http://www.aliprandi.org/cc-user-guide/)\n  - [Open Source Licensing Software Freedom and Intellectual Property Law by Lawrence Rosen](http://rosenlaw.com/oslbook/)\n  - [The Public Domain: Enclosing the Commons of the Mind by James Boyle](http://www.thepublicdomain.org/download/)\n- Machine Learning\n  - [A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone](https://arxiv.org/pdf/1709.02840.pdf)\n  - [A Brief Introduction to Neural Networks](http://www.dkriesel.com/en/science/neural_networks)\n  - [A Course in Machine Learning](http://ciml.info/dl/v0_9/ciml-v0_9-all.pdf)\n  - [A First Encounter with Machine Learning](https://www.ics.uci.edu/~welling/teaching/ICS273Afall11/IntroMLBook.pdf)\n  - [Algorithms for Reinforcement Learning by Csaba Szepesvári](https://sites.ualberta.ca/~szepesva/RLBook.html)\n  - [An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani](http://www-bcf.usc.edu/~gareth/ISL/)\n  - [Bayesian Reasoning and Machine Learning by David Barber](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage)\n  - [Computer Vision by Dana Ballard, Chris Brown](http://homepages.inf.ed.ac.uk/rbf/BOOKS/BANDB/bandb.htm)\n  - [Computer Vision: Algorithms and Applications by Richard Szeliski](http://szeliski.org/Book/)\n  - [Computer Vision: Models, Learning, and Inference by Simon J.D. Prince](http://www.computervisionmodels.com)\n  - [Convex Optimization – Boyd and Vandenberghe](http://web.stanford.edu/~boyd/cvxbook/)\n  - [Deep Learning  by Ian Goodfellow and Yoshua Bengio and Aaron Courville](deeplearningbook.org)\n  - [Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams](http://www.gaussianprocess.org/gpml/)\n  - [Information Theory, Inference, and Learning Algorithms](http://www.inference.phy.cam.ac.uk/itila/)\n  - [Introduction to Machine Learning by Amnon Shashua](http://arxiv.org/abs/0904.3664v1)\n  - [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)\n  - [Machine Learning](http://www.intechopen.com/books/machine_learning)\n  - [Machine Learning, Neural and Statistical Classification](http://www1.maths.leeds.ac.uk/~charles/statlog/)\n  - [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com)\n  - [The Elements of Statistical Learning: Data Mining, Inference, and Prediction - Second Edition, February 2009 by Trevor Hastie, Robert Tibshirani, and Jerome Friedman](https://web.stanford.edu/~hastie/ElemStatLearn/)\n  - [The LION Way: Machine Learning plus Intelligent Optimization](http://www.e-booksdirectory.com/details.php?ebook=9575)\n- Regular Expressions\n  - [Learn Regex The Hard Way by Zed. A. Shaw](http://regex.learncodethehardway.org/book/)\n  - [RexEgg](http://www.rexegg.com)\n  - [The 30 Minute Regex Tutorial by Jim Hollenhorst](http://www.codeproject.com/Articles/9099/The-Minute-Regex-Tutorial)\n  - [The Bastards Book of Regular Expressions: Finding Patterns in Everyday Text by Dan Nguyen](https://leanpub.com/bastards-regexes)\n- Spark\n  - [Databricks Spark Knowledge Base](https://www.gitbook.com/book/databricks/databricks-spark-knowledge-base/details)\n  - [Databricks Spark Reference Applications](https://www.gitbook.com/book/databricks/databricks-spark-reference-applications/details)\n  - [Mastering Apache Spark](https://www.gitbook.com/book/jaceklaskowski/mastering-apache-spark/details)\n\n### Languages\n\n- Python\n  - [20 Python Libraries You Aren't Using (But Should)](http://www.oreilly.com/programming/free/20-python-libraries-you-arent-using-but-should.csp)\n  - [A Beginner's Python Tutorial](https://en.wikibooks.org/wiki/A_Beginner%27s_Python_Tutorial)\n  - [A Guide to Python's Magic Methods by Rafe Kettler](https://github.com/RafeKettler/magicmethods)\n  - [A Whirlwind Tour of Python by Jake VanderPlas](http://www.oreilly.com/programming/free/files/a-whirlwind-tour-of-python.pdf)\n  - [Automate the Boring Stuff by Al Sweigart](http://automatetheboringstuff.com/chapter0/)\n  - [Building Machine Learning Systems with Python by Willi Richert \u0026 Luis Pedro Coelho, Packt](https://www.packtpub.com/packt/free-ebook/python-machine-learning-algorithms)\n  - [Code Like a Pythonista: Idiomatic Python](http://python.net/~goodger/projects/pycon/2007/idiomatic/handout.html)\n  - [Data Structures and Algorithms in Python by B. R. Preiss](https://web.archive.org/web/20161016153130/http://www.brpreiss.com/books/opus7/html/book.html)\n  - [From Python to NumPy](http://www.labri.fr/perso/nrougier/from-python-to-numpy/)\n  - [Full Stack Python](http://www.fullstackpython.com)\n  - [Functional Programming in Python](http://www.oreilly.com/programming/free/functional-programming-python.csp)\n  - [Google's Python Style Guide](https://google.github.io/styleguide/pyguide.html)\n  - [Hadoop with Python](http://www.oreilly.com/programming/free/hadoop-with-python.csp)\n  - [High Performance Python](http://ianozsvald.com/HighPerformancePythonfromTrainingatEuroPython2011_v0.2.pdf)\n  - [How to Make Mistakes in Python by Mike Pirnat](http://www.oreilly.com/programming/free/files/how-to-make-mistakes-in-python.pdf)\n  - [Intermediate Python by Muhammad Yasoob Ullah Khalid](http://book.pythontips.com/en/latest/index.html#)\n  - [Kalman and Bayesian Filters in Python](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python)\n  - [Learn Python, Break Python](http://learnpythonbreakpython.com)\n  - [Learn Python in Y minutes](https://learnxinyminutes.com/docs/python/)\n  - [Learn Pandas by Hernan Rojas](https://bitbucket.org/hrojas/learn-pandas)\n  - [Learn to Program Using Python by Cody Jackson](https://www.ida.liu.se/~732A47/literature/PythonBook.pdf)\n  - [Learn Tensorflow (IPython Notebooks)](https://bitbucket.org/hrojas/learn-tensorflow)\n  - [Learning Python by Fabrizio Romano, Packt](https://www.packtpub.com/packt/free-ebook/learning-python)\n  - [Learning to Program](http://www.alan-g.me.uk)\n  - [Math for programmers (using python)](https://akuli.github.io/math-tutorial/)\n  - [Mining the Social Web - 2nd Edition (IPython Notebooks)](http://nbviewer.jupyter.org/github/ptwobrussell/Mining-the-Social-Web-2nd-Edition/tree/master/ipynb/)\n  - [Modeling Creativity: Case Studies in Python by Tom D. De Smedt](http://www.clips.ua.ac.be/sites/default/files/modeling-creativity.pdf)\n  - [Picking a Python Version: A Manifesto](http://www.oreilly.com/programming/free/from-future-import-python.csp)\n  - [Practical Programming in Python by Jeffrey Elkner](https://launchpadlibrarian.net/165489933/PracticalProgrammingPython2014.pdf)\n  - [Programming Computer Vision with Python by Jan Erik Solem](http://programmingcomputervision.com)\n  - [Python Cookbook by David Beazley](http://chimera.labs.oreilly.com/books/1230000000393/index.html)\n  - [Python for Everybody Exploring Data Using Python 3 by Charles Severance](http://py4e.com/book.php)\n  - [Python Practice Projects](http://pythonpracticeprojects.com)\n  - [Scipy Lecture Notes](http://scipy-lectures.github.io)\n  - [Python Data Science Handbook (IPython Notebooks)](https://github.com/jakevdp/PythonDataScienceHandbook)\n  - [The Hitchhiker’s Guide to Python!](https://docs.python-guide.org/)\n  - [The Python Game Book](http://thepythongamebook.com/en%3Astart)\n  - [Think Stats: Probability and Statistics for Programmers by Allen B. Downey](http://greenteapress.com/thinkstats/)\n- R\n  - [Advanced R Programming by Hadley Wickham](http://adv-r.had.co.nz)\n  - [An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani](http://www-bcf.usc.edu/~gareth/ISL/)\n  - [Cookbook for R by Winston Chang](http://www.cookbook-r.com)\n  - [Introduction to Probability and Statistics Using R by G. Jay Kerns](https://github.com/gjkerns/IPSUR)\n  - [Learning Statistics with R by Daniel Navarro](https://web.archive.org/web/20170625184412/http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/)\n  - [Machine Learning with R by Brett Lantz, Packt](https://www.packtpub.com/packt/free-ebook/r-machine-learning)\n  - [ModernDive by Chester Ismay and Albert Y. Kim](https://ismayc.github.io/moderndiver-book/)\n  - [Practical Regression and Anova using R by Julian J. Faraway](http://cran.r-project.org/doc/contrib/Faraway-PRA.pdf)\n  - [Probabilistic Models in the Study of Language (with R Code)](http://idiom.ucsd.edu/~rlevy/pmsl_textbook/text.html)\n  - [R for Data Science by Garrett Grolemund and Hadley Wickham](http://r4ds.had.co.nz)\n  - [R for Spatial Analysis](http://www.columbia.edu/~cjd11/charles_dimaggio/DIRE/resources/spatialEpiBook.pdf)\n  - [R Language for Programmers by John D. Cook](http://www.johndcook.com/blog/r_language_for_programmers)\n  - [R Packages by Hadley Wickham](http://r-pkgs.had.co.nz)\n  - [R Practicals](http://www.columbia.edu/~cjd11/charles_dimaggio/DIRE/resources/R/practicalsBookNoAns.pdf)\n  - [R Programming](https://en.wikibooks.org/wiki/R_Programming)\n  - [R Programming for Data Science by Roger D. Peng](https://leanpub.com/rprogramming)\n  - [R Succinctly, Syncfusion](https://www.syncfusion.com/resources/techportal/ebooks/rsuccinctly)\n  - [The caret Package by Max Kuhn](http://topepo.github.io/caret/index.html)\n  - [The R Inferno by Patrick Burns](http://www.burns-stat.com/pages/Tutor/R_inferno.pdf)\n  - [The R Language](http://stat.ethz.ch/R-manual/R-patched/doc/html)\n  - [The R Manuals](http://cran.r-project.org/manuals.html)\n  - [Tidy Text Mining with R by Julia Silge and David Robinson](http://tidytextmining.com)\n- SQL\n  - [Developing Time-Oriented Database Applications in SQL](http://www.cs.arizona.edu/people/rts/publications.html)\n  - [SQL For Web Nerds](http://philip.greenspun.com/sql/)\n  - [SQL Notes for Professionals](http://books.goalkicker.com/SQLBook/)\n  - [Use The Index, Luke!: A Guide To SQL Database Performance](http://use-the-index-luke.com)\n\n### Mathematics\n- Algebra\n  - [A First Course in Linear Algebra by Robert A. Beezer](http://linear.ups.edu)\n  - [Advanced Algebra by Anthony W. Knapp](http://www.math.stonybrook.edu/~aknapp/download/a2-alg-inside.pdf)\n  - [Basic Algebra by Anthony W. Knapp](http://www.math.stonybrook.edu/~aknapp/download/b2-alg-inside.pdf)\n  - [Basics of Algebra, Topology, and Differential Calculus](http://www.cis.upenn.edu/~jean/math-basics.pdf)\n  - [Lecture Notes of Linear Algebra by Dr. P. Shunmugaraj, IIT Kanpur](http://home.iitk.ac.in/~psraj/mth102/lecture_notes.html)\n  - [Linear Algebra by Dr. Arbind K Lal, IIT Kanpur](http://home.iitk.ac.in/~arlal/book/nptel/pdf/booklinear.html)\n  - [Linear Algebra](https://www.math.ucdavis.edu/~linear/linear-guest.pdf)\n  - [Linear Algebra by Jim Hefferon](http://joshua.smcvt.edu/linearalgebra)\n- Calculus\n  - [Calculus by Gilbert Strang](http://ocw.mit.edu/ans7870/resources/Strang/Edited/Calculus/Calculus.pdf)\n  - [Calculus Made Easy by Silvanus P. Thompson](http://www.gutenberg.org/ebooks/33283)\n  - [Differential Equations by Paul Dawkins](http://tutorial.math.lamar.edu/download.aspx)\n  - [Elementary Differential Equations by William F. Trench](http://ramanujan.math.trinity.edu/wtrench/texts/TRENCH_DIFF_EQNS_I.PDF)\n  - [Ordinary Differential Equations](https://en.wikibooks.org/wiki/Ordinary_Differential_Equations)\n- Misc.\n  - [An Introduction to the Theory of Numbers by Leo Moser](http://www.trillia.com/moser-number.html)\n  - [Book of Proof by Richard Hammack](http://www.people.vcu.edu/~rhammack/BookOfProof/)\n  - [Category Theory for the Sciences](http://category-theory.mitpress.mit.edu)\n  - [Computational and Inferential Thinking. The Foundations of Data Science](https://www.inferentialthinking.com)\n  - [Computational Geometry by Sean Luke](http://web.mit.edu/hyperbook/Patrikalakis-Maekawa-Cho/)\n  - [Essentials of Metaheuristics](http://cs.gmu.edu/~sean/book/metaheuristics/)\n  - [Graph Theory](http://compalg.inf.elte.hu/~tony/Oktatas/TDK/FINAL/)\n  - [Introduction to Proofs by Jim Hefferon](http://joshua.smcvt.edu/proofs/)\n  - [Knapsack Problems - Algorithms and Computer Implementations by Silvano Martello and Paolo Toth](http://www.or.deis.unibo.it/knapsack.html)\n  - [Mathematical Logic - an Introduction](http://www.ii.uib.no/~michal/und/i227/book/book.pdf)\n  - [Mathematics, MTH101A by P. Shunmugaraj, IIT Kanpur](http://home.iitk.ac.in/~psraj/mth101/)\n  - [Non-Uniform Random Variate Generation by Luc Devroye](http://luc.devroye.org/rnbookindex.html)\n  - [Number Theory by Holden Lee MIT](https://github.com/holdenlee/number-theory)\n  - [Power Programming with Mathematica by David B. Wagner](http://mathematica.stackexchange.com/questions/16485/are-you-interested-in-purchasing-david-wagners-power-programming-with-mathemat/22724)\n- Probability \u0026 Statistics\n  - [Bayesian Methods for Hackers by Cameron Davidson-Pilon](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\n  - [CK-12 Probability and Statistics - Advanced](http://www.ck12.org/book/Probability-and-Statistics---Advanced-%2528Second-Edition%2529/)\n  - [Collaborative Statistics](http://cnx.org/contents/5e0744f9-9e79-4348-9237-ed012213a2d6%4040.9)\n  - [Concepts \u0026 Applications of Inferential Statistics](http://vassarstats.net/textbook/)\n  - [Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos](https://otexts.org/fpp2/)\n  - [Hyperstat](http://davidmlane.com/hyperstat/)\n  - [Introduction to Probability by Charles M. Grinstead and J. Laurie Snell](http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/book.html)\n  - [Introduction to Probability and Statistics Spring 2014](http://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/)\n  - [Introduction to Statistical Thought by Micheal Lavine](http://people.math.umass.edu/~lavine/Book/book.html)\n  - [Multivariate Statistics: Concepts, Models, and Applications - 3rd Web Edition by David W. Stockburger](http://psychstat3.missouristate.edu/Documents/MultiBook3/mbk.htm)\n  - [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php)\n  - [Probability and Statistics Cookbook](http://statistics.zone)\n  - [Probability and Statistics EBook](http://wiki.stat.ucla.edu/socr/index.php/Probability_and_statistics_EBook)\n  - [Statistics Done Wrong by Alex Reinhart](http://www.statisticsdonewrong.com)\n  - [Statistical Learning with Sparsity: The Lasso and Generalizations by Trevor Hastie, Robert Tibshirani, and Martin Wainwright]\n  - [StatLect](https://www.statlect.com/)\n  - [StatSoft](http://www.statsoft.com/Textbook)\n  - [The Little Handbook of Statistical Practice by Gerard E. Dallal, Ph.D](http://www.jerrydallal.com/LHSP/LHSP.htm)\n  - [Think Bayes: Bayesian Statistics Made Simple by  Allen B. Downey](http://www.greenteapress.com/thinkbayes/)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/datakind-blr%2Fresources/projects"}