{"id":75297,"url":"https://github.com/m-muecke/awesome-data-science","name":"awesome-data-science","description":"Data science and programming resources for daily work","projects_count":201,"last_synced_at":"2026-10-05T11:00:29.653Z","repository":{"id":65416125,"uuid":"281445320","full_name":"m-muecke/awesome-data-science","owner":"m-muecke","description":"Data science and programming resources for daily work","archived":false,"fork":false,"pushed_at":"2026-03-14T10:27:27.000Z","size":67,"stargazers_count":22,"open_issues_count":0,"forks_count":3,"subscribers_count":3,"default_branch":"master","last_synced_at":"2026-10-01T21:43:07.969Z","etag":null,"topics":["awesome-list","bash","data-science","linux","machine-learning","python","r","r-programming","sql"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/m-muecke.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2020-07-21T16:11:12.000Z","updated_at":"2026-03-14T10:27:30.000Z","dependencies_parsed_at":"2024-10-28T03:22:20.517Z","dependency_job_id":"e10d97fc-3f5c-4021-9874-180b79b28402","html_url":"https://github.com/m-muecke/awesome-data-science","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/m-muecke/awesome-data-science","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/m-muecke%2Fawesome-data-science","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/m-muecke%2Fawesome-data-science/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/m-muecke%2Fawesome-data-science/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/m-muecke%2Fawesome-data-science/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/m-muecke","download_url":"https://codeload.github.com/m-muecke/awesome-data-science/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/m-muecke%2Fawesome-data-science/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":343170754,"owners_count":38036166,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-10-03T21:59:58.778Z","status":"online","status_checked_at":"2026-10-05T02:00:06.766Z","response_time":83,"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":"2024-10-28T02:22:46.373Z","updated_at":"2026-10-05T11:00:29.653Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Python","R","Linux and Shell/Bash","Documentation and Style Guide","Web Development","Data Science","Time Series","Deep Learning","SQL","NLP","Data Sets","Finance","Computer Science","Quantitative Economics","Docker","Econometrics"],"sub_categories":["Links","R","Python","Books","Courses","Blogs"],"readme":"# Collection of data science focused resources\n\nUseful programming and data science focused resources for daily work.\n\n[![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)\n\n- [Collection of data science focused resources](#collection-of-data-science-focused-resources)\n  - [Python](#python)\n    - [Books](#books)\n    - [Courses](#courses)\n    - [Links](#links)\n  - [R](#r)\n    - [Books](#books-1)\n    - [Blogs](#blogs)\n    - [Links](#links-1)\n    - [Courses](#courses-1)\n  - [SQL](#sql)\n    - [Books](#books-2)\n    - [Courses](#courses-2)\n  - [Data Science](#data-science)\n    - [Books](#books-3)\n    - [Python](#python-1)\n      - [Books](#books-4)\n      - [Courses](#courses-3)\n      - [Links](#links-2)\n    - [R](#r-1)\n      - [Books](#books-5)\n      - [Links](#links-3)\n      - [Courses](#courses-4)\n  - [Finance](#finance)\n    - [Books](#books-6)\n    - [Python](#python-2)\n      - [Books](#books-7)\n      - [Courses](#courses-5)\n    - [R](#r-2)\n      - [Books](#books-8)\n      - [Links](#links-4)\n      - [Courses](#courses-6)\n      - [Books](#books-9)\n  - [Quantitative Economics](#quantitative-economics)\n    - [Python](#python-3)\n      - [Packages](#packages)\n      - [Lectures](#lectures)\n  - [Time Series](#time-series)\n    - [Books](#books-10)\n    - [R](#r-3)\n      - [Books](#books-11)\n      - [Links](#links-5)\n  - [Econometrics](#econometrics)\n    - [R](#r-4)\n      - [Books](#books-12)\n      - [Link](#link)\n  - [NLP](#nlp)\n    - [Python](#python-4)\n      - [Books](#books-13)\n      - [Courses](#courses-7)\n  - [Deep Learning](#deep-learning)\n    - [Books](#books-14)\n    - [Python](#python-5)\n      - [Books](#books-15)\n      - [Courses](#courses-8)\n  - [Web Development](#web-development)\n    - [Python](#python-6)\n      - [Packages](#packages-2)\n      - [Books](#books-16)\n      - [Courses](#courses-9)\n    - [R](#r-5)\n      - [Packages](#packages-3)\n  - [Linux and Shell/Bash](#linux-and-shellbash)\n    - [Books](#books-17)\n    - [Links](#links-6)\n    - [Courses, Guides, Lectures, etc](#courses-guides-lectures-etc)\n  - [Documentation and Style Guide](#documentation-and-style-guide)\n  - [Data Sets](#data-sets)\n  - [Computer Science](#computer-science)\n\n## Python\n\n### Books\n\n- [Architecture Patterns with Python - Harry Percival, Bob Gregory](https://www.cosmicpython.com/) `Free`\n- [Automate the Boring Stuff with Python - Al Sweigart](https://automatetheboringstuff.com/) `Free`\n- [Effective Python - Brett Slatkin](https://effectivepython.com/) `Paid`\n- [Fluent Python - Luciano Ramalho](https://www.oreilly.com/library/view/fluent-python/9781491946237/) `Paid`\n- [Python for Everybody - Charles R. Severance](https://www.py4e.com/book.php) `Free`\n- [The Hitchhiker’s Guide to Python! - Kenneth Reitz and Tanya Schlusser](https://docs.python-guide.org/) `Free`\n- [Think Python 2e - Allen B. Downey](https://greenteapress.com/wp/think-python-2e/) `Free`\n- [Whirwind Tour of Python - Jake VanderPlas](https://github.com/jakevdp/WhirlwindTourOfPython) `Free`\n\n### Courses\n\n- [Python for Everybody (PY4E) - Charles R. Severance](https://www.py4e.com/lessons) `Free`\n- [Python for Everybody Specialization - Coursera](https://www.coursera.org/specializations/python) `Paid`\n\n### Links\n\n- [GitHub Curated List: Awesome Python](https://github.com/vinta/awesome-python)\n- [Real Python](https://realpython.com/)\n\n## R\n\n### Books\n\n- [Advanced R - Hadley Wickham](https://adv-r.hadley.nz/) `Free`\n- [Advanced R Solutions - Malte Grosser, Henning Bumann, Hadley Wickham](https://advanced-r-solutions.rbind.io/) `Free`\n- [An Introduction to R - Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto, David Lusseau](https://intro2r.com/) `Free`\n- [Cookbook for R - Winston Chang](http://www.cookbook-r.com/) `Free`\n- [Efficient R Programming - C. Gillespie and R. Lovelace](https://csgillespie.github.io/efficientR/) `Free`\n- [Functional Programming - Sara Altman, Bill Behrman, Hadley Wickham](https://dcl-prog.stanford.edu/index.html) `Free`\n- [Happy Git and GitHub for the useR - Jenny Bryan](https://happygitwithr.com/) `Free`\n- [Hands-On Programming with R - Garrett Grolemund](https://rstudio-education.github.io/hopr/) `Free`\n- [ggplot2: Elegant Graphics for Data Analysis (3e) - Hadley Wickham](https://ggplot2-book.org/) `Free`\n- [Mastering Software Development in R - Roger D. Peng](https://bookdown.org/rdpeng/RProgDA/) `Free`\n- [R Markdown Cookbook - Yihui Xie, Christophe Dervieux, Emily Riederer](https://bookdown.org/yihui/rmarkdown-cookbook/) `Free`\n- [R Markdown: The Definitive Guide - Yihui Xie, J. J. Allaire, Garrett Grolemund](https://bookdown.org/yihui/rmarkdown/) `Free`\n- [R Packages: Organize, Test, Document, and Share Your Code - Hadley Wickham and Jennifer Bryan](https://r-pkgs.org/) `Free`\n- [R for Everyone - Jared Lander](https://www.jaredlander.com/r-for-everyone/) `Paid`\n- [R in Production - Hadley Wickham](https://r-in-production.org) `Free`\n- [Seamless R and C++ Integration with Rcpp - Dirk Eddelbuettel](https://link.springer.com/book/10.1007/978-1-4614-6868-4) `Paid`\n- [Tidy design principles - Hadley Wickham](https://design.tidyverse.org) `Free`\n- [What They Forgot to Teach You About R - Jenny Bryan, Jim Hester, Shannon Pileggi](https://rstats.wtf/) `Free`\n- [bookdown: Authoring Books and Technical Documents with R Markdown - Yihui Xie](https://bookdown.org/yihui/bookdown/) `Free`\n\n### Blogs\n\n- [r-consortium](https://www.r-consortium.org/blog)\n- [r-weekly](https://rweekly.org)\n- [r-bloggers](https://www.r-bloggers.com)\n- [tidyverse](https://www.tidyverse.org/blog/)\n- [Posit](https://posit.co/blog/)\n- [data.table](https://rdatatable-community.github.io/The-Raft/)\n\n### Links\n\n- [Big Book of R](https://www.bigbookofr.com/)\n- [CRAN Task Views](https://cran.r-project.org/web/views/)\n- [GitHub Curated List: Awesome R](https://github.com/qinwf/awesome-R)\n- [Quarto - Next-gen Scientific Publishing](https://quarto.org/)\n- [R-universe](https://r-universe.dev/)\n\n### Courses\n\n- [Mastering Software Development in R Specialization - Coursera](https://www.coursera.org/specializations/r) `Paid`\n\n## SQL\n\n### Books\n\n- [Learning SQL (3e) - Alan Beaulieu](https://www.oreilly.com/library/view/learning-sql-3rd/9781492057604/) `Paid`\n- [SQL Cookbook: Query Solutions and Techniques for All SQL Users](https://www.oreilly.com/library/view/sql-cookbook-2nd/9781492077435/) `Paid`\n- [Use The Index, Luke - Markus Winand](https://use-the-index-luke.com/) `Free`\n\n### Courses\n\n- [Mode SQL Tutorial](https://mode.com/sql-tutorial) `Free`\n- [PostgreSQL for Everybody](https://www.pg4e.com) `Free`\n- [SQL for Data Analysis - Udacity](https://www.udacity.com/course/sql-for-data-analysis--ud198) `Free`\n\n## Data Science\n\n### Books\n\n- [Advanced Statistical Computing - Roger D. Peng](https://leanpub.com/advstatcomp) `Free`\n- [Convex Optimization - Stephen Boyd and Lieven Vandenberghe](http://stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf) `Free`\n- [Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani and Jerome Friedman](https://web.stanford.edu/~hastie/Papers/ESLII.pdf) `Free`\n- [Interpretable Machine Learning - Christoph Molnar](https://christophm.github.io/interpretable-ml-book/) `Free`\n- [Linear Algebra Review - J. Zico Kolter](https://www.cs.cmu.edu/~zkolter/course/linalg/) `Free`\n- [Linear Algebra for Data Science with examples in R - Shaina Race Bennett](https://shainarace.github.io/LinearAlgebra/) `Free`\n- [Statistical Inference for Data Science - Brian Caffo](https://leanpub.com/LittleInferenceBook/read) `Free`\n- [Statistical Learning with Sparsity: The Lasso and Generalization - Trevor Hastie, Robert Tibshirani, Martin Wainwright](https://web.stanford.edu/~hastie/StatLearnSparsity_files/SLS.pdf) `Free`\n- [Think Bayes - Allen B. Downey](http://greenteapress.com/wp/think-bayes/) `Free`\n\n### Courses\n\n- [Google Data Analytics Professional Certificate](https://www.coursera.org/professional-certificates/google-data-analytics) `Paid`\n- [IBM Data Science Professional Certificate - Coursera](https://www.coursera.org/professional-certificates/ibm-data-science) `Paid`\n\n### Python\n\n#### Books\n\n- [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`\n- [Introduction to Machine Learning with Python - Andreas C. Müller and Sarah Guido](https://www.oreilly.com/library/view/introduction-to-machine/9781449369880/) `Paid`\n- [Python Data Science Handbook: Essential Tools for Working with Data - Jake VanderPlas](https://jakevdp.github.io/PythonDataScienceHandbook/) `Free`\n- [Python for Data Analysis - Wes McKinney](https://wesmckinney.com/pages/book.html) `Paid`\n- [Think Stats 2e - Allen B. Downey](https://greenteapress.com/wp/think-stats-2e/) `Free`\n- [Web Scraping with Python - Ryan Mitchell](https://www.oreilly.com/library/view/web-scraping-with/9781491985564/) `Paid`\n\n#### Courses\n\n- [Applied Data Science with Python Specialization - Coursera](https://www.coursera.org/specializations/data-science-python)\n- [Foundations of Machine Learning - Bloomberg ML EDU](https://bloomberg.github.io/foml/) `Free`\n- [Machine Learning Specialization - Andrew Ng, Stanford / Coursera](https://www.coursera.org/specializations/machine-learning-introduction) `Paid`\n\n#### Links\n\n- [GitHub Curated List: Data Science Python](https://github.com/ujjwalkarn/DataSciencePython)\n\n### R\n\n#### Books\n\n- [Flexible and Robust Machine Learning Using mlr3 in R - Lars Kotthoff, Raphael Sonabend, Michel Lang, Bernd Bischl](https://mlr3book.mlr-org.com) `Free`\n- [Geocomputation with R - Robin Lovelace, Jakub Nowosad, Jannes Muenchow](https://geocompr.robinlovelace.net/) `Free`\n- [Introduction to Statistical Learning - Gareth James, Daniela Witten, Trevor Hastiec, Rob Tibshirani](https://www-bcf.usc.edu/~gareth/ISL/) `Free`\n- [Mastering Spark with R - Javier Luraschi, Kevin Kuo, Edgar Ruiz](https://therinspark.com/) `Free`\n- [R Graphics Cookbook - Winston Chang](https://r-graphics.org/) `Free`\n- [R Programming for Data Science - Roger D. Peng](https://bookdown.org/rdpeng/rprogdatascience/) `Free`\n- [R for Data Science (2e) - Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund](https://r4ds.hadley.nz/) `Free`\n- [Report Writing for Data Science in R - Roger D. Peng](https://leanpub.com/reportwriting?utm_source=coursera\u0026utm_medium=syllabus\u0026utm_campaign=CourseraSyllabus) `Free`\n- [Statistical Inference via Data Science: A ModernDive into R and the Tidyverse - Chester Ismay and Albert Y. Kim](https://moderndive.com/) `Free`\n- [Supervised Machine Learning for Text Analysis in R - Emil Hvitfeldt and Julia Silge](https://smltar.com) `Free`\n- [The targets R Package User Manual - Will Landau](https://books.ropensci.org/targets/) `Free`\n- [Text Mining with R - Julia Silge and David Robinson](https://www.tidytextmining.com/) `Free`\n- [Tidy Modeling with R - Max Kuhn, Julia Silge](https://www.tmwr.org/) `Free`\n\n#### Links\n\n- [GitHub Curated List: Data Science R](https://github.com/ujjwalkarn/DataScienceR)\n\n#### Courses\n\n- [Data Science Specialization - Coursera](https://www.coursera.org/specializations/jhu-data-science) `Paid`\n- [Introduction to Machine Learning (I2ML) - LMU Munich](https://slds-lmu.github.io/i2ml/) `Free`\n\n## Finance\n\n### Books\n\n- [Bayesian Stability Concepts for Investment Managers - Diethelm Würtz, Tobias Setz](https://www.rmetrics.org/ebooks-stability) `Free`\n- [Numerical Methods and Optimization in Finance - Manfred Gilli, Dietmar Maringer and Enrico Schumann](http://enricoschumann.net/NMOF.htm) `Paid`\n- [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`\n\n### Links\n\n- [GitHub Curated List: Awesome Quant](https://github.com/wilsonfreitas/awesome-quant)\n\n### Python\n\n#### Books\n\n- [Advances in Financial Machine Learning - Marcos López de Prado](https://www.amazon.com/Advances-Financial-Machine-Learning-Marcos/dp/1119482089) `Paid`\n- [Machine Learning for Algorithmic Trading - Stefan Jansen](https://www.amazon.com/Machine-Learning-Algorithmic-Trading-alternative/dp/1839217715) `Paid`\n- [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`\n- [Python for Finance - Yves Hilpisch](https://www.oreilly.com/library/view/python-for-finance/9781492024323/) `Paid`\n\n#### Courses\n\n- [Artificial Intelligence for Trading Nanodegree - Udacity](https://www.udacity.com/course/ai-for-trading--nd880) `Paid`\n- [Financial Engineering and Risk Management Specialization](https://www.coursera.org/specializations/financialengineering) `Paid`\n- [Investment Management with Python and Machine Learning Specialization - Coursera](https://www.coursera.org/specializations/investment-management-python-machine-learning) `Paid`\n\n### R\n\n#### Books\n\n- [Basic R for Finance - Diethelm Würtz, Tobias Setz, Yohan Chalabi, Longhow Lam, Andrew Ellis](https://www.rmetrics.org/ebooks-basicr) `Free`\n- [Financial Data and Models Using R - Clifford Ang](http://www.cliffordang.com) `Paid`\n- [Financial Optimisation with R - Enrico Schumann](http://enricoschumann.net/files/NMOFman.pdf) `Free`\n- [Financial Risk Modelling and Portfolio Optimization with R - Bernhard Pfaff](https://www.pfaffikus.de/books/wiley/) `Paid`\n- [Introduction to Computational Finance and Financial Econometrics with R - Eric Zivot](https://bookdown.org/compfinezbook/introcompfinr/) `Free`\n- [Machine Learning for Factor Investing - Guillaume Coqueret and Tony Guida](http://www.mlfactor.com/) `Free`\n- [Portfolio Management with R - Enrico Schumann ](http://enricoschumann.net/R/packages/PMwR/manual/PMwR.html) `Free`\n- [Portfolio Optimization with R/Rmetrics - Diethelm Würtz, Tobias Setz, Yohan Chalabi, William Chen, Andrew Ellis](https://www.rmetrics.org/ebooks-portfolio) `Free`\n- [Tidy Finance with R - Christoph Scheuch, Stefan Voigt, Patrick Weiss](https://www.tidy-finance.org) `Free`\n- [Topics in Empirical Finance with R and Rmetrics - Patrick Hénaff](https://www.rmetrics.org/ebooks-henaff) `Paid`\n\n#### Links\n\n- [CRAN Task View: Empirical Finance](https://cran.r-project.org/web/views/Finance.html)\n\n#### Courses\n\n- [Applying Data Analytics in Finance - Coursera](https://www.coursera.org/learn/applying-data-analytics-business-in-finance) `Paid`\n- [ECON 424/CFRM 462: Computational Finance and Financial Econometrics - University of Washington](https://faculty.washington.edu/ezivot/econ424/424syllabus.htm) `Free`\n- [FRE7241 Algorithmic Portfolio Management - NYU](https://github.com/algoquant/lecture_slides) `Free`\n- [FRE6871 R in Finance - NYU](https://github.com/algoquant/lecture_slides) `Free`\n\n## Quantitative Economics\n\n### Python\n\n#### Packages\n\n- [QuantEcon - A high performance, open source Python code library for economics](https://github.com/QuantEcon/QuantEcon.py)\n\n#### Lectures\n\n- [Advanced Quantitative Economics with Python - Thomas J. Sargent and John Stachurski](https://python-advanced.quantecon.org/intro.html) `Free`\n- [Introduction to Economic Modeling and Data Science - Chase Coleman, Spencer Lyon, Jesse Perla, et al.](https://datascience.quantecon.org/) `Free`\n- [Python Programming for Economics and Finance - Thomas J. Sargent and John Stachurski](https://python-programming.quantecon.org/intro.html) `Free`\n- [Quantitative Economics with Python - Thomas J. Sargent and John Stachurski](https://python.quantecon.org/intro.html) `Free`\n\n## Time Series\n\n### Books\n\n- [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`\n- [Forecasting for Economics and business - Gloria González-Rivera](https://www.amazon.com/Forecasting-Economics-Business-Pearson/dp/0131474936) `Paid`\n- [Introduction to time series and forecasting - Peter J. Brockwell and Richard A. Davis](https://www.springer.com/de/book/9783319298528) `Paid`\n- [Nonlinear Time Series Analysis - Ruey S. Tsay and Rong Chen](https://www.wiley.com/en-us/Nonlinear+Time+Series+Analysis-p-9781119264071) `Paid`\n- [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`\n\n### R\n\n#### Books\n\n- [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`\n- [Forecasting: Principles and Practice - Rob J Hyndman and George Athanasopoulos](https://otexts.com/fpp3/) `Free`\n- [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`\n- [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`\n- [Time Series Analysis and Its Applications With R Examples - Robert H. Shumway and David S. Stoffer](https://www.stat.pitt.edu/stoffer/tsa4/) `Paid`\n- [Time Series: A Data Analysis Approach Using R - Robert H. Shumway and David S. Stoffer](https://www.stat.pitt.edu/stoffer/tsda/) `Paid`\n\n#### Links\n\n- [CRAN Task View: Time Series Analysis](https://cran.r-project.org/web/views/TimeSeries.html)\n\n## Econometrics\n\n### R\n\n#### Books\n\n- [Introduction to Econometrics with R - Christoph Hanck, Martin Arnold, Alexander Gerber, Martin Schmelzer](https://www.econometrics-with-r.org/) `Free`\n- [Panel Data Econometrics with R - Yves Croissant and Givanni Millo](https://onlinelibrary.wiley.com/doi/book/10.1002/9781119504641) `Paid`\n\n#### Link\n\n- [CRAN Task View: Econometrics](https://cran.r-project.org/web/views/Econometrics.html)\n\n## NLP\n\n### Python\n\n#### Books\n\n- [Applied Text Analysis with Python - Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda](https://www.oreilly.com/library/view/applied-text-analysis/9781491963036/) `Paid`\n- [Natural Language Processing with Python - Steven Bird, Ewan Klein, Edward Loper](https://www.nltk.org/book/) `Free`\n\n#### Courses\n\n- [A Code-First Intro to Natural Language Processing - fast.ai](https://github.com/fastai/course-nlp) `Free`\n- [CS224U: Natural Language Understading - Stanford University](https://web.stanford.edu/class/cs224u/2021/index.html) `Free`\n- [CS224n: Natural Language Processing with Deep Learning - Stanford University](http://web.stanford.edu/class/cs224n/) `Free`\n- [Natural Language Processing Nanodegree - Udacity](https://www.udacity.com/course/natural-language-processing-nanodegree--nd892) `Paid`\n\n## Deep Learning\n\n### Books\n\n- [Dive into Deep Learning - Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola](https://d2l.ai/index.html) `Free`\n- [Deep Learning - Ian Goodfellow and Yoshua Bengio and Aaron Courville](https://www.deeplearningbook.org/) `Free`\n\n### Python\n\n#### Books\n\n- [Deep Learning with PyTorch - Eli Stevens, Luca Antiga, and Thomas Viehmann](https://www.manning.com/books/deep-learning-with-pytorch) `Paid`\n\n#### Packages\n\n- [Hugging Face Transformers - Pre-trained models for NLP, vision, and audio](https://github.com/huggingface/transformers)\n- [PyTorch Lightning - High-level framework for PyTorch](https://github.com/Lightning-AI/pytorch-lightning)\n\n#### Courses\n\n- [Deep Learning Specialization - Andrew Ng / DeepLearning.AI, Coursera](https://www.coursera.org/specializations/deep-learning) `Paid`\n- [Practical Deep Learning for Coders - fast.ai](https://course.fast.ai/) `Free`\n- [Yann LeCun’s Deep Learning Course at CDS](https://cds.nyu.edu/deep-learning/) `Free`\n\n## Web Development\n\n### Python\n\n#### Packages\n\n- [Django - High-level Python web framework](https://www.djangoproject.com/)\n- [FastAPI - Modern, fast (high-performance), web framework for building APIs](https://fastapi.tiangolo.com/)\n- [FastHTML - Modern web applications in pure Python](https://www.fastht.ml)\n- [Flask - Lightweight WSGI web application framework](https://flask.palletsprojects.com/)\n- [Gradio - Build \u0026 share delightful ML apps](https://www.gradio.app)\n- [Shiny for Python - Effortless Python web applications](https://shiny.posit.co/py/)\n- [Streamlit - A faster way to build and share data apps](https://streamlit.io/)\n\n#### Books\n\n- [Flask Web Development - Miguel Grinberg](https://www.oreilly.com/library/view/flask-web-development/9781491991725/) `Paid`\n\n#### Courses\n\n- [CS50’s Web Programming with Python and JavaScript - Harvard University](https://cs50.harvard.edu/web/2020/) `Free`\n- [Django for Everybody (DJ4E) - Charles R. Severance](https://www.dj4e.com/lessons) `Free`\n- [Django for Everybody Specialization - Coursera](https://www.coursera.org/specializations/django) `Paid`\n- [Python Django Tutorial Series - Corey Schafer](https://www.youtube.com/watch?v=UmljXZIypDc\u0026list=PL-osiE80TeTtoQCKZ03TU5fNfx2UY6U4p) `Free`\n\n### R\n\n#### Books\n\n- [Engineering Production-Grade Shiny Apps - Colin Fay, Sébastien Rochette, Vincent Guyader, Cervan Girard](https://engineering-shiny.org/index.html) `Free`\n- [Mastering Shiny - Hadley Wickham](https://mastering-shiny.org/) `Free`\n- [Outstanding User Interfaces with Shiny - David Granjon](https://unleash-shiny.rinterface.com/index.html) `Free`\n- [blogdown: Creating Websites with R Markdown - Yihui Xie, Amber Thomas, Alison Presmanes Hill](https://bookdown.org/yihui/blogdown/) `Free`\n\n#### Packages\n\n- [Shiny - Build interactive web applications](https://shiny.rstudio.com/)\n- [blogdown - Create websites with R Mardown](https://github.com/rstudio/blogdown)\n- [plumber - A web API generator for R](https://www.rplumber.io/)\n\n## Docker\n\n### R\n\n- [r-minimal: Minimal Docker images for R](https://github.com/r-hub/r-minimal)\n- [Rocker Project: Docker Containers for the R Environment](https://rocker-project.org)\n\n## Linux and Shell/Bash\n\n#### Books\n\n- [Data Science at the Command Line - Jeroen Janssens](https://www.datascienceatthecommandline.com/index.html) `Free`\n\n#### Links\n\n- [GitHub Curated List: Bash Resources](https://github.com/awesome-lists/awesome-bash)\n- [GitHub Curated List: Linux Ecosystem Overview](https://github.com/aleksandar-todorovic/awesome-linux)\n- [GitHub Curated List: List of Shell command-line frameworks](https://github.com/alebcay/awesome-shell)\n- [GitHub Curated List: Software for Linux](https://github.com/luongvo209/Awesome-Linux-Software)\n\n#### Courses, Guides, Lectures, etc\n\n- [Advanced Bash Scripting Guide - Mendel Cooper](https://www.tldp.org/LDP/abs/html/index.html) `Free`\n- [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`\n- [Bash Scripting Cheat Sheet](https://devhints.io/bash) `Free`\n- [Linux Basics: E-Learning Module on Linux and Bash Fundamentals - bwHPC](https://training.bwhpc.de/ilias/ilias.php?ref_id=310\u0026from_page=5066\u0026obj_id=1\u0026cmd=layout\u0026cmdClass=illmpresentationgui\u0026cmdNode=cn\u0026baseClass=ilLMPresentationGUI) `Free`\n- [The Missing Semester of Your CS Education: MIT lecture for shell, Vim, Git, etc. - MIT](https://missing.csail.mit.edu/) `Free`\n\n## Documentation and Style Guide\n\n- [Black - Python Code Formatter](https://github.com/psf/black)\n- [Google Style Guides](https://google.github.io/styleguide/)\n- [Pandoc - Universal Document Converter](https://pandoc.org/)\n- [Ruff - Fast Python Linter and Formatter](https://github.com/astral-sh/ruff)\n- [Sphinx – Python Documentation Generator](https://www.sphinx-doc.org/en/master/)\n- [uv - Fast Python Package Installer and Resolver](https://github.com/astral-sh/uv)\n- [Tidyverse Style Guide for R](https://style.tidyverse.org/)\n\n## Data Sets\n\n- [European Central Bank’s Data Warehouse](https://sdw.ecb.europa.eu) `Free`\n- [Fama-French Data Library](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) `Free`\n- [Google Data Set Search](https://datasetsearch.research.google.com/) `Free`\n- [Kaggle Data](https://www.kaggle.com/datasets) `Free`\n- [OECD Data Portal](https://data.oecd.org) `Free`\n- [OpinRank Data – Reviews From TripAdvisor \\\u0026 Edmunds](http://kavita-ganesan.com/entity-ranking-data/#.XlQAAmhKiUm) `Free`\n- [Quandl - Financial, Economic, and Alternative Datasets](https://www.quandl.com/) `Free` `Paid`\n- [SNAP – Amazon Reviews](http://snap.stanford.edu/data/web-Amazon.html) `Free`\n- [St. Louis Federal Reserve Bank Economic Data (FRED)](https://fred.stlouisfed.org) `Free`\n- [U.S. Census Bureau Data](https://data.census.gov/cedsci/) `Free`\n- [UCI – Machine Learning Repository](https://archive.ics.uci.edu/datasets) `Free`\n- [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`\n- [World Bank Open Data](https://data.worldbank.org) `Free`\n- [Yelp Academic Data Sets](https://www.yelp.com/dataset) `Free`\n- [eurostat - Statistical Database of the European Commision](https://ec.europa.eu/eurostat/web/main/data/database) `Free`\n\n## Computer Science\n\n- [Think Complexity 2e - Allen B. Downey](https://greenteapress.com/wp/think-complexity-2e/) `Free`\n- [Think DSP: Digital Signal Processing in Python - Allen B. Downey](https://greenteapress.com/wp/think-dsp/) `Free`\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/m-muecke%2Fawesome-data-science/projects"}