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https://github.com/m-muecke/awesome-data-science
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Data science and programming resources for daily work
Host: GitHub
URL: https://github.com/m-muecke/awesome-data-science
Owner: m-muecke
License: mit
Created: 2020-07-21T16:11:12.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2024-01-07T11:29:34.000Z (12 months ago)
Last Synced: 2024-01-07T17:10:25.588Z (12 months ago)
Topics: awesome-list, bash, data-science, linux, machine-learning, python, r, r-programming, sql
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Watchers: 3
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
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ultimate-awesome - awesome-data-science - Data science and programming resources for daily work. (Other Lists / Monkey C Lists)
README

        # Collection of data science focused resources

Useful programming and data science focused resources for daily work.

[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](./CONTRIBUTING.md)

- [Collection of data science focused resources](#collection-of-data-science-focused-resources)

  - [Python](#python)

    - [Books](#books)

    - [Courses](#courses)

    - [Links](#links)

  - [R](#r)

    - [Books](#books-1)

    - [Blogs](#blogs)

    - [Links](#links-1)

    - [Courses](#courses-1)

  - [SQL](#sql)

    - [Books](#books-2)

    - [Courses](#courses-2)

  - [Data Science](#data-science)

    - [Books](#books-3)

    - [Python](#python-1)

      - [Books](#books-4)

      - [Courses](#courses-3)

      - [Links](#links-2)

    - [R](#r-1)

      - [Books](#books-5)

      - [Links](#links-3)

      - [Courses](#courses-4)

  - [Finance](#finance)

    - [Books](#books-6)

    - [Python](#python-2)

      - [Books](#books-7)

      - [Courses](#courses-5)

    - [R](#r-2)

      - [Books](#books-8)

      - [Links](#links-4)

      - [Courses](#courses-6)

      - [Books](#books-9)

  - [Quantitative Economics](#quantitative-economics)

    - [Python](#python-3)

      - [Packages](#packages)

      - [Lectures](#lectures)

  - [Time Series](#time-series)

    - [Books](#books-10)

    - [R](#r-3)

      - [Books](#books-11)

      - [Links](#links-5)

  - [Econometrics](#econometrics)

    - [R](#r-4)

      - [Books](#books-12)

      - [Link](#link)

  - [NLP](#nlp)

    - [Python](#python-4)

      - [Books](#books-13)

      - [Courses](#courses-7)

  - [Deep Learning](#deep-learning)

    - [Books](#books-14)

    - [Python](#python-5)

      - [Books](#books-15)

      - [Courses](#courses-8)

  - [Web Development](#web-development)

    - [Python](#python-6)

      - [Packages](#packages-2)

      - [Books](#books-16)

      - [Courses](#courses-9)

    - [R](#r-5)

      - [Packages](#packages-3)

  - [Linux and Shell/Bash](#linux-and-shellbash)

    - [Books](#books-17)

    - [Links](#links-6)

    - [Courses, Guides, Lectures, etc](#courses-guides-lectures-etc)

  - [Documentation and Style Guide](#documentation-and-style-guide)

  - [Data Sets](#data-sets)

  - [Computer Science](#computer-science)

## Python

### Books

- [Automate the Boring Stuff with Python - Al Sweigart](https://automatetheboringstuff.com/) `Free`

- [Fluent Python - Luciano Ramalho](https://www.oreilly.com/library/view/fluent-python/9781491946237/) `Paid`

- [Python for Everybody - Charles R. Severance](https://www.py4e.com/book.php) `Free`

- [The Hitchhiker’s Guide to Python! - Kenneth Reitz and Tanya Schlusser](https://docs.python-guide.org/) `Free`

- [Think Python 2e - Allen B. Downey](https://greenteapress.com/wp/think-python-2e/) `Free`

- [Whirwind Tour of Python - Jake VanderPlas](https://github.com/jakevdp/WhirlwindTourOfPython) `Free`

### Courses

- [Python for Everybody (PY4E) - Charles R. Severance](https://www.py4e.com/lessons) `Free`

- [Python for Everybody Specialization - Coursera](https://www.coursera.org/specializations/python) `Paid`

### Links

- [GitHub Curated List: Awesome Python](https://github.com/keon/awesome-nlp)

## R

### Books

- [Advanced R - Hadley Wickham](https://adv-r.hadley.nz/) `Free`

- [Advanced R Solutions - Malte Grosser, Henning Bumann, Hadley Wickham](https://advanced-r-solutions.rbind.io/) `Free`

- [An Introduction to R - Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto, David Lusseau](https://intro2r.com/) `Free`

- [Cookbook for R - Winston Chang](http://www.cookbook-r.com/) `Free`

- [Efficient R Programming - C. Gillespie and R. Lovelace](https://csgillespie.github.io/efficientR/) `Free`

- [Functional Programming - Sara Altman, Bill Behrman, Hadley Wickham](https://dcl-prog.stanford.edu/index.html) `Free`

- [Hands-On Programming with R - Garrett Grolemund](https://rstudio-education.github.io/hopr/) `Free`

- [Mastering Software Development in R - Roger D. Peng](https://bookdown.org/rdpeng/RProgDA/) `Free`

- [R Markdown Cookbook - Yihui Xie, Christophe Dervieux, Emily Riederer](https://bookdown.org/yihui/rmarkdown-cookbook/) `Free`

- [R Markdown: The Definitive Guide - Yihui Xie, J. J. Allaire, Garrett Grolemund](https://bookdown.org/yihui/rmarkdown/) `Free`

- [R Packages: Organize, Test, Document, and Share Your Code - Hadley Wickham and Jennifer Bryan](https://r-pkgs.org/) `Free`

- [R for Everyone - Jared Lander](https://www.jaredlander.com/r-for-everyone/) `Paid`

- [R in Production - Hadley Wickham](https://r-in-production.org) `Free`

- [Tidy design principles - Hadley Wickham](https://design.tidyverse.org) `Free`

- [bookdown: Authoring Books and Technical Documents with R Markdown - Yihui Xie](https://bookdown.org/yihui/bookdown/) `Free`

### Blogs

- [r-consortium](https://www.r-consortium.org/blog)

- [r-weekly](https://rweekly.org)

- [r-bloggers](https://www.r-bloggers.com)

- [tidyverse](https://www.tidyverse.org/blog/)

- [Posit](https://posit.co/blog/)

- [data.table](https://rdatatable-community.github.io/The-Raft/)

### Links

- [CRAN Task Views](https://cran.r-project.org/web/views/)

- [GitHub Curated List: Awesome R](https://github.com/qinwf/awesome-R)

### Courses

- [Mastering Software Development in R Specialization - Coursera](https://www.coursera.org/specializations/r) `Paid`

## SQL

### Books

- [SQL Cookbook: Query Solutions and Techniques for All SQL Users](https://www.oreilly.com/library/view/sql-cookbook-2nd/9781492077435/) `Paid`

### Courses

- [PostgreSQL for Everybody](https://www.pg4e.com) `Free`

- [SQL for Data Analysis - Udacity](https://www.udacity.com/course/sql-for-data-analysis--ud198) `Free`

## Data Science

### Books

- [Advanced Statistical Computing - Roger D. Peng](https://leanpub.com/advstatcomp) `Free`

- [Convex Optimization - Stephen Boyd and Lieven Vandenberghe](http://stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf) `Free`

- [Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani and Jerome Friedman](https://web.stanford.edu/~hastie/Papers/ESLII.pdf) `Free`

- [Interpretable Machine Learning - Christoph Molnar](https://christophm.github.io/interpretable-ml-book/) `Free`

- [Linear Algebra Review - J. Zico Kolter](https://www.cs.cmu.edu/~zkolter/course/linalg/) `Free`

- [Linear Algebra for Data Science with examples in R - Shaina Race Bennett](https://shainarace.github.io/LinearAlgebra/) `Free`

- [Statistical Inference for Data Science - Brian Caffo](https://leanpub.com/LittleInferenceBook/read) `Free`

- [Statistical Learning with Sparsity: The Lasso and Generalization - Trevor Hastie, Robert Tibshirani, Martin Wainwright](https://web.stanford.edu/~hastie/StatLearnSparsity_files/SLS.pdf) `Free`

- [Think Bayes - Allen B. Downey](http://greenteapress.com/wp/think-bayes/) `Free`

### Courses

- [Google Data Analytics Professional Certificate](https://www.coursera.org/professional-certificates/google-data-analytics) `Paid`

- [IBM Data Science Professional Certificate - Coursera](https://www.coursera.org/professional-certificates/ibm-data-science) `Paid`

### Python

#### Books

- [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - Aurélien Géron](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) `Paid`

- [Introduction to Machine Learning with Python - Andreas C. Müller and Sarah Guido](https://www.oreilly.com/library/view/introduction-to-machine/9781449369880/) `Paid`

- [Python Data Science Handbook: Essential Tools for Working with Data - Jake VanderPlas](https://jakevdp.github.io/PythonDataScienceHandbook/) `Free`

- [Python for Data Analysis - Wes McKinney](https://wesmckinney.com/pages/book.html) `Paid`

- [Think Stats 2e - Allen B. Downey](https://greenteapress.com/wp/think-stats-2e/) `Free`

- [Web Scraping with Python - Ryan Mitchell](https://www.oreilly.com/library/view/web-scraping-with/9781491985564/) `Paid`

#### Courses

- [Applied Data Science with Python Specialization - Coursera](https://www.coursera.org/specializations/data-science-python)

- [Foundations of Machine Learning - Bloomberg ML EDU](https://bloomberg.github.io/foml/) `Free`

#### Links

- [GitHub Curated List: Data Science Python](https://github.com/ujjwalkarn/DataSciencePython)

### R

#### Books

- [Flexible and Robust Machine Learning Using mlr3 in R - Lars Kotthoff, Raphael Sonabend, Michel Lang, Bernd Bischl](https://mlr3book.mlr-org.com) `Free`

- [Geocomputation with R - Robin Lovelace, Jakub Nowosad, Jannes Muenchow](https://geocompr.robinlovelace.net/) `Free`

- [Introduction to Statistical Learning - Gareth James, Daniela Witten, Trevor Hastiec, Rob Tibshirani](https://www-bcf.usc.edu/~gareth/ISL/) `Free`

- [Mastering Spark with R - Javier Luraschi, Kevin Kuo, Edgar Ruiz](https://therinspark.com/) `Free`

- [R Graphics Cookbook - Winston Chang](https://r-graphics.org/) `Free`

- [R Programming for Data Science - Roger D. Peng](https://bookdown.org/rdpeng/rprogdatascience/) `Free`

- [R for Data Science - Hadley Wickham](http://r4ds.had.co.nz/) `Free`

- [Report Writing for Data Science in R - Roger D. Peng](https://leanpub.com/reportwriting?utm_source=coursera&utm_medium=syllabus&utm_campaign=CourseraSyllabus) `Free`

- [Statistical Inference via Data Science: A ModernDive into R and the Tidyverse - Chester Ismay and Albert Y. Kim](https://moderndive.com/) `Free`

- [Supervised Machine Learning for Text Analysis in R - Emil Hvitfeldt and Julia Silge](https://smltar.com) `Free`

- [Text Mining with R - Julia Silge and David Robinson](https://www.tidytextmining.com/) `Free`

#### Links

- [GitHub Curated List: Data Science R](https://github.com/ujjwalkarn/DataScienceR)

#### Courses

- [Data Science Specialization - Coursera](https://www.coursera.org/specializations/jhu-data-science) `Paid`

- [Introduction to Machine Learning (I2ML) - LMU Munich](https://slds-lmu.github.io/i2ml/) `Free`

## Finance

### Books

- [Bayesian Stability Concepts for Investment Managers - Diethelm Würtz, Tobias Setz](https://www.rmetrics.org/ebooks-stability) `Free`

- [Numerical Methods and Optimization in Finance - Manfred Gilli, Dietmar Maringer and Enrico Schumann](http://enricoschumann.net/NMOF.htm) `Paid`

- [Statistics and Data Analysis for Financial Engineering (with R examples) - David Ruppert and David S. Matteson](https://people.orie.cornell.edu/davidr/SDAFE2/index.html) `Paid`

### Links

- [GitHub Curated List: Awesome Quant](https://github.com/wilsonfreitas/awesome-quant)

### Python

#### Books

- [Advances in Financial Machine Learning - Marcos López de Prado](https://www.amazon.com/Advances-Financial-Machine-Learning-Marcos/dp/1119482089) `Paid`

- [Machine Learning for Algorithmic Trading - Stefan Jansen](https://www.amazon.com/Machine-Learning-Algorithmic-Trading-alternative/dp/1839217715) `Paid`

- [Machine Learning in Finance: From Theory to Practice - Igor Halperin, Matthew F. Dixon, Paul Bilokon](https://www.springer.com/gp/book/9783030410674#:~:text=It%20presents%20a%20unified%20treatment,for%20financial%20data%20modeling%20and) `Paid`

- [Python for Finance - Yves Hilpisch](https://www.oreilly.com/library/view/python-for-finance/9781492024323/) `Paid`

#### Courses

- [Artificial Intelligence for Trading Nanodegree - Udacity](https://www.udacity.com/course/ai-for-trading--nd880) `Paid`

- [Financial Engineering and Risk Management Specialization](https://www.coursera.org/specializations/financialengineering) `Paid`

- [Investment Management with Python and Machine Learning Specialization - Coursera](https://www.coursera.org/specializations/investment-management-python-machine-learning) `Paid`

### R

#### Books

- [Basic R for Finance - Diethelm Würtz, Tobias Setz, Yohan Chalabi, Longhow Lam, Andrew Ellis](https://www.rmetrics.org/ebooks-basicr) `Free`

- [Financial Data and Models Using R - Clifford Ang](http://www.cliffordang.com) `Paid`

- [Financial Optimisation with R - Enrico Schumann](http://enricoschumann.net/files/NMOFman.pdf) `Free`

- [Financial Risk Modelling and Portfolio Optimization with R - Bernhard Pfaff](https://www.pfaffikus.de/books/wiley/) `Paid`

- [Introduction to Computational Finance and Financial Econometrics with R - Eric Zivot](https://bookdown.org/compfinezbook/introcompfinr/) `Free`

- [Machine Learning for Factor Investing - Guillaume Coqueret and Tony Guida](http://www.mlfactor.com/) `Free`

- [Portfolio Management with R - Enrico Schumann ](http://enricoschumann.net/R/packages/PMwR/manual/PMwR.html) `Free`

- [Portfolio Optimization with R/Rmetrics - Diethelm Würtz, Tobias Setz, Yohan Chalabi, William Chen, Andrew Ellis](https://www.rmetrics.org/ebooks-portfolio) `Free`

- [Tidy Finance with R - Christoph Scheuch, Stefan Voigt, Patrick Weiss](https://www.tidy-finance.org) `Free`

- [Topics in Empirical Finance with R and Rmetrics - Patrick Hénaff](https://www.rmetrics.org/ebooks-henaff) `Paid`

#### Links

- [CRAN Task View: Empirical Finance](https://cran.r-project.org/web/views/Finance.html)

#### Courses

- [Applying Data Analytics in Finance - Coursera](https://www.coursera.org/learn/applying-data-analytics-business-in-finance) `Paid`

- [ECON 424/CFRM 462: Computational Finance and Financial Econometrics - University of Washington](https://faculty.washington.edu/ezivot/econ424/424syllabus.htm) `Free`

- [FRE7241 Algorithmic Portfolio Management - NYU](https://github.com/algoquant/lecture_slides) `Free`

- [FRE6871 R in Finance - NYU](https://github.com/algoquant/lecture_slides) `Free`

## Quantitative Economics

### Python

#### Packages

- [QuantEcon - A high performance, open source Python code library for economics](https://github.com/QuantEcon/QuantEcon.py)

#### Lectures

- [Advanced Quantitative Economics with Python - Thomas J. Sargent and John Stachurski](https://python-advanced.quantecon.org/intro.html) `Free`

- [Introduction to Economic Modeling and Data Science - Chase Coleman, Spencer Lyon, Jesse Perla, et al.](https://datascience.quantecon.org/) `Free`

- [Python Programming for Economics and Finance - Thomas J. Sargent and John Stachurski](https://python-programming.quantecon.org/intro.html) `Free`

- [Quantitative Economics with Python - Thomas J. Sargent and John Stachurski](https://python.quantecon.org/intro.html) `Free`

## Time Series

### Books

- [Analysis of Financial Time Series - Ruey S. Tsay](https://www.wiley.com/en-us/Analysis+of+Financial+Time+Series%2C+3rd+Edition-p-9780470414354) `Paid`

- [Forecasting for Economics and business - Gloria González-Rivera](https://www.amazon.com/Forecasting-Economics-Business-Pearson/dp/0131474936) `Paid`

- [Introduction to time series and forecasting - Peter J. Brockwell and Richard A. Davis](https://www.springer.com/de/book/9783319298528) `Paid`

- [Nonlinear Time Series Analysis - Ruey S. Tsay and Rong Chen](https://www.wiley.com/en-us/Nonlinear+Time+Series+Analysis-p-9781119264071) `Paid`

- [Time series analysis: forecasting and control: George EP Box, et al.](https://www.wiley.com/en-as/Time+Series+Analysis:+Forecasting+and+Control,+5th+Edition-p-9781118675021) `Paid`

### R

#### Books

- [An Introduction to Analysis of Financial Data with R - Ruey S. Tsay](https://www.wiley.com/en-us/An+Introduction+to+Analysis+of+Financial+Data+with+R-p-9780470890813) `Paid`

- [Forecasting: Principles and Practice - Rob J Hyndman and George Athanasopoulos](https://otexts.com/fpp3/) `Free`

- [Multivariate Time Series Analysis: With R and Financial Applications - Ruey S. Tsay](https://www.wiley.com/en-us/Multivariate+Time+Series+Analysis%3A+With+R+and+Financial+Applications-p-9781118617908) `Paid`

- [Nonlinear Time Series: Theory, Methods and Applications with R Examples - David S. Stoffer, Randal Douc, Éric Moulines](https://www.stat.pitt.edu/stoffer/nltsa/) `Paid`

- [Time Series Analysis and Its Applications With R Examples - Robert H. Shumway and David S. Stoffer](https://www.stat.pitt.edu/stoffer/tsa4/) `Paid`

- [Time Series: A Data Analysis Approach Using R - Robert H. Shumway and David S. Stoffer](https://www.stat.pitt.edu/stoffer/tsda/) `Paid`

#### Links

- [CRAN Task View: Time Series Analysis](https://cran.r-project.org/web/views/TimeSeries.html)

## Econometrics

### R

#### Books

- [Introduction to Econometrics with R - Christoph Hanck, Martin Arnold, Alexander Gerber, Martin Schmelzer](https://www.econometrics-with-r.org/) `Free`

- [Panel Data Econometrics with R - Yves Croissant and Givanni Millo](https://onlinelibrary.wiley.com/doi/book/10.1002/9781119504641) `Paid`

#### Link

- [CRAN Task View: Econometrics](https://cran.r-project.org/web/views/Econometrics.html)

## NLP

### Python

#### Books

- [Applied Text Analysis with Python - Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda](https://www.oreilly.com/library/view/applied-text-analysis/9781491963036/) `Paid`

- [Natural Language Processing with Python - Steven Bird, Ewan Klein, Edward Loper](https://www.nltk.org/book/) `Free`

#### Courses

- [A Code-First Intro to Natural Language Processing - fast.ai](https://github.com/fastai/course-nlp) `Free`

- [CS224U: Natural Language Understading - Stanford University](https://web.stanford.edu/class/cs224u/2021/index.html) `Free`

- [CS224n: Natural Language Processing with Deep Learning - Stanford University](http://web.stanford.edu/class/cs224n/) `Free`

- [Natural Language Processing Nanodegree - Udacity](https://www.udacity.com/course/natural-language-processing-nanodegree--nd892) `Paid`

## Deep Learning

### Books

- [Dive into Deep Learning - Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola](https://d2l.ai/index.html) `Free`

- [Deep Learning - Ian Goodfellow and Yoshua Bengio and Aaron Courville](https://www.deeplearningbook.org/) `Free`

### Python

#### Books

- [Deep Learning with PyTorch - Eli Stevens, Luca Antiga, and Thomas Viehmann](https://www.manning.com/books/deep-learning-with-pytorch) `Paid`

#### Courses

- [Yann LeCun’s Deep Learning Course at CDS](https://cds.nyu.edu/deep-learning/) `Free`

## Web Development

### Python

#### Packages

- [Django - High-level Python web framework](https://www.djangoproject.com/)

- [FastAPI - Modern, fast (high-performance), web framework for building APIs](https://fastapi.tiangolo.com/)

- [FastHTML - Modern web applications in pure Python](https://www.fastht.ml)

- [Flask - Lightweight WSGI web application framework](https://flask.palletsprojects.com/)

- [Gradio - Build & share delightful ML apps](https://www.gradio.app)

- [Shiny for Python - Effortless Python web applications](https://shiny.posit.co/py/)

- [Streamlit - A faster way to build and share data apps](https://streamlit.io/)

#### Books

- [Flask Web Development - Miguel Grinberg](https://www.oreilly.com/library/view/flask-web-development/9781491991725/) `Paid`

#### Courses

- [CS50’s Web Programming with Python and JavaScript - Harvard University](https://cs50.harvard.edu/web/2020/) `Free`

- [Django for Everybody (DJ4E) - Charles R. Severance](https://www.dj4e.com/lessons) `Free`

- [Django for Everybody Specialization - Coursera](https://www.coursera.org/specializations/django) `Paid`

- [Python Django Tutorial Series - Corey Schafer](https://www.youtube.com/watch?v=UmljXZIypDc&list=PL-osiE80TeTtoQCKZ03TU5fNfx2UY6U4p) `Free`

### R

#### Books

- [Engineering Production-Grade Shiny Apps - Colin Fay, Sébastien Rochette, Vincent Guyader, Cervan Girard](https://engineering-shiny.org/index.html) `Free`

- [Mastering Shiny - Hadley Wickham](https://mastering-shiny.org/) `Free`

- [Outstanding User Interfaces with Shiny - David Granjon](https://unleash-shiny.rinterface.com/index.html) `Free`

- [blogdown: Creating Websites with R Markdown - Yihui Xie, Amber Thomas, Alison Presmanes Hill](https://bookdown.org/yihui/blogdown/) `Free`

#### Packages

- [Shiny - Build interactive web applications](https://shiny.rstudio.com/)

- [blogdown - Create websites with R Mardown](https://github.com/rstudio/blogdown)

- [plumber - A web API generator for R](https://www.rplumber.io/)

## Docker

### R

- [r-minimal: Minimal Docker images for R](https://github.com/r-hub/r-minimal)

- [Rocker Project: Docker Containers for the R Environment](https://rocker-project.org)

## Linux and Shell/Bash

#### Books

- [Data Science at the Command Line - Jeroen Janssens](https://www.datascienceatthecommandline.com/index.html) `Free`

#### Links

- [GitHub Curated List: Bash Resources](https://github.com/awesome-lists/awesome-bash)

- [GitHub Curated List: Linux Ecosystem Overview](https://github.com/aleksandar-todorovic/awesome-linux)

- [GitHub Curated List: List of Shell command-line frameworks](https://github.com/alebcay/awesome-shell)

- [GitHub Curated List: Software for Linux](https://github.com/luongvo209/Awesome-Linux-Software)

#### Courses, Guides, Lectures, etc

- [Advanced Bash Scripting Guide - Mendel Cooper](https://www.tldp.org/LDP/abs/html/index.html) `Free`

- [Advanced Bash Scripting Lecture - bwHPC](https://indico.scc.kit.edu/event/410/attachments/1603/2217/01_2018-04-12_bwHPC_course_-_Adv_Bash_Scripting.pdf) `Free`

- [Bash Scripting Cheat Sheet](https://devhints.io/bash) `Free`

- [Linux Basics: E-Learning Module on Linux and Bash Fundamentals - bwHPC](https://training.bwhpc.de/ilias/ilias.php?ref_id=310&from_page=5066&obj_id=1&cmd=layout&cmdClass=illmpresentationgui&cmdNode=cn&baseClass=ilLMPresentationGUI) `Free`

- [The Missing Semester of Your CS Education: MIT lecture for shell, Vim, Git, etc. - MIT](https://missing.csail.mit.edu/) `Free`

## Documentation and Style Guide

- [Black - Python Code Formatter](https://github.com/psf/black)

- [Google Style Guides](https://google.github.io/styleguide/)

- [Pandoc - Universal Document Converter](https://pandoc.org/)

- [Sphinx – Python Documentation Generator](https://www.sphinx-doc.org/en/master/)

- [Tidyverse Style Guide for R](https://style.tidyverse.org/)

## Data Sets

- [European Central Bank’s Data Warehouse](https://sdw.ecb.europa.eu) `Free`

- [Fama-French Data Library](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) `Free`

- [Google Data Set Search](https://datasetsearch.research.google.com/) `Free`

- [Kaggle Data](https://www.kaggle.com/datasets) `Free`

- [OECD Data Portal](https://data.oecd.org) `Free`

- [OpinRank Data – Reviews From TripAdvisor \& Edmunds](http://kavita-ganesan.com/entity-ranking-data/#.XlQAAmhKiUm) `Free`

- [Quandl - Financial, Economic, and Alternative Datasets](https://www.quandl.com/) `Free` `Paid`

- [SNAP – Amazon Reviews](http://snap.stanford.edu/data/web-Amazon.html) `Free`

- [St. Louis Federal Reserve Bank Economic Data (FRED)](https://fred.stlouisfed.org) `Free`

- [U.S. Census Bureau Data](https://data.census.gov/cedsci/) `Free`

- [UCI – Machine Learning Repository](http://archive.ics.uci.edu/ml/datasets.php) `Free`

- [University of Illinois at Chicago - Opinion Mining, Sentiment Analysis, and Opinion Spam Detection](https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#datasets) `Free`

- [University of Texas - Multifaceted Text Classification Datasets](http://www.hlt.utdallas.edu/~sajib/multifacetedText.html) `Free`

- [World Bank Open Data](https://data.worldbank.org) `Free`

- [Yelp Academic Data Sets](https://www.yelp.com/dataset) `Free`

- [eurostat - Statistical Database of the European Commision](https://ec.europa.eu/eurostat/web/main/data/database) `Free`

## Computer Science

- [Think Complexity 2e - Allen B. Downey](https://greenteapress.com/wp/think-complexity-2e/) `Free`

- [Think DSP: Digital Signal Processing in Python - Allen B. Downey](https://greenteapress.com/wp/think-dsp/) `Free`