Suggested readings from coursera courses

Statistics

• OpenIntro Statistics (book to download, video, lab, etc.) https://www.openintro.org/

• A tutorial on Principal Components Analysis (https://ourarchive.otago.ac.nz/bitstream/handle/10523/7534/OUCS-2002-12.pdf?sequence=1&isAllowed=y)

• The Art of Data Science A Guide for Anyone Who Works with Data by Peng and Matsui (https://leanpub.com/artofdatascience/ - not free)

• An Introduction to Bayesian Thinking - A Companion to the Statistics with R Course (online book) https://statswithr.github.io/book/index.html

• Spatial Data Science (online book) by Pebesma and Bivand https://keen-swartz-3146c4.netlify.app/

• Statistical inference for data science A companion to the Coursera Statistical Inference Course by Caffo https://github.com/bcaffo/LittleInferenceBook https://leanpub.com/LittleInferenceBook

• Regression Models for Data Science in R by Caffo https://leanpub.com/regmods

• Advanced Linear Models for Data Science by Caffo https://leanpub.com/lm

• Data Science Specialization (online companion) http://datasciencespecialization.github.io/

• Explanation of PCA on StackExchange https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues

Programming

R

• Cookbook for R (online ressource) http://www.cookbook-r.com/

• website for the book “R for Data Science” https://r4ds.had.co.nz/ by Wickham and Grolemund

• R programming for data science by Peng (book to download) https://leanpub.com/rprogramming?utm_source=coursera&utm_medium=CourseraEmail&utm_campaign=Coursera

• Mastering Software Development in R (online book) by Peng et al. (https://bookdown.org/rdpeng/RProgDA/) https://leanpub.com/msdr/

• R Markdown cheat sheet https://rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf

• Package development with devtools cheat sheet https://rstudio.com/wp-content/uploads/2015/06/devtools-cheatsheet.pdf

• data import in R cheat sheet https://github.com/rstudio/cheatsheets/blob/master/data-import.pdf

• R Markdown: The Definitive Guide online book https://bookdown.org/yihui/rmarkdown/

• Dynamic Documents with R and knitr by Xie

• Reproducible Research with R and RStudio by Gandrud https://englianhu.files.wordpress.com/2016/01/reproducible-research-with-r-and-studio-2nd-edition.pdf

• FasteR! HigheR! StrongeR! - A Guide to Speeding Up R Code for Busy People http://www.noamross.net/archives/2013-04-25-faster-talk/

• Developing Data Products in R by Caffo and Kross (online book) https://leanpub.com/ddp

• Methods in Biostatistics with R - A Rigorous and Practical Treatment of Biostatistics Foundations using R (online book) by Crainiceanu et al. https://leanpub.com/biostatmethods

Python

• Python Crash Course: A Hands-On, Project-Based Introduction to Programming by Matthes (http://bedford-computing.co.uk/learning/wp-content/uploads/2015/10/No.Starch.Python.Oct_.2015.ISBN_.1593276036.pdf) https://ehmatthes.github.io/pcc/

• Python for Data Analysis Book - Wes McKinney https://wesmckinney.com/pages/book.html notebooks: https://github.com/wesm/pydata-book

• online book Foundations of Python Programming https://runestone.academy/runestone/books/published/fopp/index.html

• machine learning python’s tutorial https://pythonspot.com/machine-learning/ and other Python tutorials https://pythonspot.com/

• The Hitchhiker’s Guide to Python! https://docs.python-guide.org/

• Reading and Writing CSV Files in Python https://realpython.com/python-csv/ from Real Python Tutorials (https://realpython.com/)

• Automate the Boring Stuff with Python By Sweigart https://automatetheboringstuff.com/

• Cleaning Text for Natural Language Processing Tasks in Machine Learning in Python http://ieva.rocks/2016/08/07/cleaning-text-for-nlp/

Bash

• Bash Scripting Tutorial https://ryanstutorials.net/bash-scripting-tutorial/ from Ryan’s tutorials (https://ryanstutorials.net/)

• Shell scripting tutorial by Steve Parker https://www.shellscript.sh/

• Bash Scripting Tutorial for Beginners https://linuxconfig.org/bash-scripting-tutorial-for-beginners

Machine learning

• Jay Alammar blog (http://jalammar.github.io/):
  • The Illustrated Transformer (jalammar.github.io/illustrated-transformer)
  • The Illustrated GPT-2 (Visualizing Transformer Language Models) (http://jalammar.github.io/illustrated-gpt2/)
  • How GPT3 Works - Visualizations and Animations (http://jalammar.github.io/how-gpt3-works-visualizations-animations/)

• Speech and Language Processing, by Jurafsky et al.

• Comprendre le fonctionnement d’un LSTM et d’un GRU en schémas du site Pensée artificielle https://penseeartificielle.fr/comprendre-lstm-gru-fonctionnement-schema/ https://penseeartificielle.fr/

• Intro to optimization in deep learning: Gradient Descent https://blog.paperspace.com/intro-to-optimization-in-deep-learning-gradient-descent/ from PaperspaceBlog (https://blog.paperspace.com/)

• Animated RNN, LSTM and GRU from towards data science https://towardsdatascience.com/

• Understanding LSTM Networks from colah’s blog https://colah.github.io/

• Mini Batch Gradient Descent video https://www.youtube.com/watch?v=4qJaSmvhxi8 by Deeplearning.ai (see also https://www.youtube.com/channel/UCcIXc5mJsHVYTZR1maL5l9w)

• Introduction to Neural Networks and Machine Learning http://www.cs.toronto.edu/~tijmen/csc321/ (slides !), course form the University of Toronto

• Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names (https://gombru.github.io/2018/05/23/cross_entropy_loss/) from Raúl Gómez’s blog (https://gombru.github.io/)

• Image filtering (https://lodev.org/cgtutor/filtering.html) from Lode’s Computer Graphics Tutorial (https://lodev.org/cgtutor/index.html)

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Géron

• Deep Learning with Python by Chollet

• Mathematics for Machine Learning by Deisenroth et al. https://mml-book.github.io/

• Natural Language Processing Lecture Slides from the 2012 Stanford Coursera course by Dan Jurafsky and Christopher Manning https://web.stanford.edu/~jurafsky/NLPCourseraSlides.html (!!!)

• Chris Albon’s blog on Data Science & Machine Learning https://chrisalbon.com/

Visualization

• Inkscape: Guide to a Vector Drawing Program http://tavmjong.free.fr/INKSCAPE/MANUAL/html/index.html

• ggplot2: Elegant Graphics for Data Analysis by Wickham (online book) https://ggplot2-book.org/

• R Graphics Cookbook by Chang (online book) https://r-graphics.org/

• Data Visualization with R by Kabacoff (online book) https://rkabacoff.github.io/datavis/

Git and github accounts

• git book: https://git-scm.com/book/en/v2/

• https://github.com/wadefagen/ (coursera course: https://github.com/wadefagen/coursera)

• https://github.com/rdpeng

• https://github.com/lmoroney/dlaicourse (coursera course notebooks (Tensorflow): https://github.com/lmoroney/dlaicourse)

• https://github.com/seankross and slides from coursera courses: https://github.com/seankross/slides

• https://github.com/bcaffo/ and course materials for the Johns Hopkins Data Science Specialization on Coursera https://github.com/bcaffo/courses

• https://github.com/DataScienceSpecialization/Developing_Data_Products Developing Data Products Course from the Johns Hopkins Data Science Lab (see also https://github.com/DataScienceSpecialization/)

Tools

ATOM text editor https://atom.io/ Geany text editor https://www.geany.org/

• RStudio cheat sheet https://raw.githubusercontent.com/rstudio/cheatsheets/master/rstudio-ide.pdf

• Teaching and Learning with Jupyter (online book) by Barba et al. https://jupyter4edu.github.io/jupyter-edu-book/

• online regex tester and debugger https://regex101.com/

• swirl for teaching R https://swirlstats.com/