Resource Center =============== Here a links to a variety of data science and business analytics related resources. General business analytics and data science ------------------------------------------------ * `Analytics Magazine`_ It's published by `INFORMS - the Institute for Operations Research and Management Science `_. They are the premier professional society for analytics and it's inexpensive to join as a student. Full disclosure, I've been an INFORMS member since about 1986 (when it was still ORSA/TIMS). We were doing analytics before it was called analytics. :) * `Awesome Data Science `_ - giant curated list of data science resources * There are numerous groups on Reddit related to analytics and data science. These can be very good resources for unvarnished conversations/opinions about careers, grad school as well as technical advice. - `r/datascience `_ - `r/analytics `_ - `r/OperationsResearch `_ - `r/BusinessIntelligence `_ - `r/DataEngineering `_ Programming tutorial hubs ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * `Software Carpentry `_ and `Data Carpentry `_ - helping scientists learn to do computational work with R, Python, SQL and other tools Online courses ^^^^^^^^^^^^^^^ There are numerous online courses available through DataCamp, Coursera, EdX, Udemy and others. Here's a few Python and R ones I've checked out over the years. * `Intro to Data Science in Python`_ - I did this short course in Feb 2017 (Coursera UMich). Great fun. If you want a good pandas/python learning challenge, try the assignments. * `Python for Everybody course`_ - This site includes a bunch of videos and supplementary files. The whole thing was created by a professor at University of Michigan and is meant to be a totally open set of freely available learning materials for Python in the context of data analysis. * Coursera has some well regarded `R based data science courses `_ Learning R ---------- Online tutorials, books and examples for getting started ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * `R-bloggers`_- The aggregator for R related blogs. * `Quick-R`_ - This is a great site dedicated to helping R newbies get over the somewhat steep R learning curve. * `Cookbook for R`_ - Another great site for learning R. In their words: "The goal of the cookbook is to provide solutions to common tasks and problems in analyzing data." * `R for Data Science`_ - Free, online version of the book, **R for Data Science** by Hadley Wickham and Garrett Grolemund. * `The Official R Manuals`_ - These are accessible from the main R Project page in the Documentation section. * `Contributed Documentation`_ - Many people have written tutorials, books, and other free documentation for various aspects of R. This is part of the magic of R community. * `Introducing R to a non-programmer in one hour`_ - Just what it says. * `Webinars from R Studio`_- The creators of the hugely popular R Studio package have a ton of learning resources on their site. * `Teach yourself Shiny`_- A somewhat recent development by the folks at R Studio is something called a Shiny web app. Learn to create interactive, R driven, web apps! Packages ^^^^^^^^^ The R ecosystem relies on high quality packages and its community of package developers. Here are some collections of package descriptions and links. * `RStartHere`_- A very comprehensive and well organized list of packages for doing data science in R. * `Awesome R`_- Curated list of R packages by category (IDE, data manipulation, etc.) Learning Python ---------------- Online tutorials, books and examples for getting started ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * `Software Carpentry - Lessons`_ - Software Carpentry is one of my all time favorite resources for teaching and learning practical programming skills. This link takes you to their list of "Lessons" (really entire mini-courses). In addition to a lesson on Python, you'll find lessons on tons of stuff that is useful for business analytics and data science. Highly, highly recommended. * `Whirlwind Tour of Python - Jake VanderPlas`_ - Free 100 page pdf and associated Jupyter notebooks for those who want to learn Python for data science use and have some prior knowledge of programming. * `Python for Everybody - Charles Severance`_ - This is a remixed, freely available, textbook on learning Python to do data analysis. Blogs and listservs ^^^^^^^^^^^^^^^^^^^^ * `Practical Business Python`_ - Super relevant blog for business students learning Python. * `Pycoders Weekly`_ - Weekly email newsletter. Always has interesting stuff and almost always something directly data science related. Libraries ^^^^^^^^^^ * `Awesome Python`_ - A curated list of awesome Python frameworks, libraries, software and resources Statistics ----------------- If you are rusty on statistics, there's a really good OpenIntro Stats book available as a free online book or you can pay what you want for a paperback copy. It includes R based material. * `OpenIntro Stats `_ You can also find high quality `free online statistics courses through the Open Learning Initiative `_ as well as places like Coursera and EdX. `Cross Validated `_ is a great Q&A forum for all things statistics. Lots of R related content. Publicly available data ----------------------- * `Kaggle Datasets `_ - need to create a free Kaggle account * `OpenML Datasets `_ - site with many ML resources * `UCI Machine Learning Repository `_ * `cs109 Resources (2014) `_ - Many links to datasets (as well as links to Python and misc data science stuff) * `https://github.com/rstudio/RStartHere#data `_ - From the RStartHere site * `Google Public Data `_ * `Climate Data Online `_ * `USGS `_ * `dataportals.org `_ * `Finding Data on the Internet (from Inside-R) `_ Workflow and reproducible analysis ---------------------------------- * `Reproducible and Trustworthy Workflows for Data Science `_ * `Data Science Workflow: Overview and Challenges `_ - Blog post by Philip Guo who did his dissertation on this topic. * `Cookiecutter Data Science `_ - "A logical, reasonably standardized, but flexible project structure for doing and sharing data science work." .. _Analytics Magazine: http://analytics-magazine.org/ .. _Competing on Analytics: https://hbr.org/2006/01/competing-on-analytics .. _Quick-R: http://www.statmethods.net/ .. _Cookbook for R: http://www.cookbook-r.com/ .. _R for Data Science: http://r4ds.had.co.nz/ .. _The Official R Manuals: https://cran.r-project.org/manuals.html .. _Contributed Documentation: https://cran.r-project.org/other-docs.html .. _Introducing R to a non-programmer in one hour: http://alyssafrazee.com/2014/01/02/introducing-R.html .. _R-bloggers: http://www.r-bloggers.com/ .. _Webinars from R Studio: https://www.rstudio.com/resources/webinars/ .. _RStartHere: https://github.com/rstudio/RStartHere .. _Awesome R: https://github.com/qinwf/awesome-R .. _10 R packages I wish I knew about sooner: http://blog.yhat.com/posts/10-R-packages-I-wish-I-knew-about-earlier.html .. _Teach yourself Shiny: https://shiny.rstudio.com/tutorial/ .. _Introducing R Shiny web apps | SNAP: https://blog.snap.uaf.edu/2013/05/20/introducing-r-shiny-web-apps/ .. _RStudio Add-ins: https://rstudio.github.io/rstudioaddins/#overview .. _Software Carpentry - Lessons: http://software-carpentry.org/lessons/ .. _Whirlwind Tour of Python - Jake VanderPlas: https://github.com/jakevdp/WhirlwindTourOfPython .. _Python for Everybody - Charles Severance: https://www.py4e.com/book.php .. _Python for Everybody course: https://www.py4e.com/ .. _Awesome Python: https://github.com/vinta/awesome-python .. _Learning Python - suggestions for business analytics students: http://hselab.org/learning-python-suggestions-and-resources-for-business-analytics-students-and-professionals.html .. _Intro to Data Science in Python: https://www.coursera.org/learn/python-data-analysis .. _Practical Business Python: http://pbpython.com/ .. _Pycoders Weekly: http://pycoders.com/