This repository contains a summary of some data science materials: papers, useful packages, MOOC, career development info, etc.

• Practical Data Science for Stats - a PeerJ Collection: Very interesting and insightful papers on data science practice

https://www.w3schools.com/sql/ SQL for Data Scientist Learning Notes: http://scientistcafe.com/notes/SQL

• kerasR
• Most Cited Deep Learning Papers: A curated list of the most cited deep learning papers (since 2012)
• Image Kernels: http://setosa.io/ev/image-kernels/
• http://www.fast.ai

• cleanNLP: cleanNLP calls one of two state of the art NLP libraries (CoreNLP or spaCy). The package currently supports input text in English, German, French, and Spanish.

• 'pwr' package: https://cran.r-project.org/web/packages/pwr/vignettes/pwr-vignette.html

• broom package: takes the messy output of built-in functions in R, such as lm, nls, or t.test, and turns them into tidy data frames

• CausalTree

• Excerpts on data science
• Probability interview questions

• Unix Learning Notes: http://scientistcafe.com/notes/Unix/
• Presentation about iOS and logs: https://github.com/mac4n6/Presentations
• scijava-jupyter-kernel aims to be a polyglot Jupyter kernel. It uses the Scijava scripting languages to execute the code in Jupyter client and it's possible to use different languages in the same notebook.

Some of the supported languages are Groovy (default), Python, Beanshell, Clojure, Java, Javascript, Ruby and Scala.

https://github.com/scijava/scijava-jupyter-kernel/tree/afd8c1c7be5b92a734e0fac9d78bcc0216162340