Resources for Data Science

Do you want to know about statistics, mathematics, data science and deep learning? Do you want to become a data scientist using R, Python or Julia? These are some of the resources that will help you.

Engines & IDEs

Engine	Recommended IDE	Alternative IDEs
R	RStudio	Dataiku
Python	Anaconda	Dataiku
Julia	Jupyter Notebook	Dataiku, Anaconda

Resources

Topic	Webs	Books
Computer Vision	---	Foundations of Computer Vision
Statistics		The Elements of Statistical Learning Statistical Thinking for the 21st Century
Mathematics		Mathematics For Machine Learning
Machine Learning		PATTERNS, PREDICTIONS, AND ACTIONS Interpretable Machine Learning
Data Science		Foundations of Data Science Data Science for Startups Handbook of Hidden Data Scientist
Deep Learning	Deep Learning Models	Goodfellow et al. 2016. Deep Learning. MIT Press
R	R-bloggers Togaware R and Datamining Awesome R R Cheatsheet	An Introduction to Statistical Learning R-Statistics Cookbook for R Advance R and solutions R Packages Reproducible Research Learning R in Practice Gitbook with R Markdown Efficient R Programming Open Forensic Science in R The tidyverse style guide Feature Engineering and Selection: A Practical Approach for Predictive Models Mastering Shiny R for data science and solutions Hands-On Programming with R Supervised Machine Learning for Text Analysis in R Modern Statistics with R R for applied epidemiology and public health An R Companion for Introduction to Data Mining Explanatory Model Analysis Scaling Up With R and Arrow Supervised Machine Learning for Science R for Data Engineers An Introduction to Bayesian Data Analysis for Cognitive Science
Python	W3schools Python Python Scientific Lecture Notes	Python Data Science Handbook Causal Inference for The Brave and True
Julia	Julia Documentation Getting Started with Julia	Data Science in Julia for Hackers Think Julia: How to Think Like a Computer Scientist Julia language: a concise tutorial

Learning R with 'swirl'

With the swirl R package you can learn R programming and data science. Open RStudio and type the following into the console:

install.packages("swirl")

Then load the package:

library(swirl)

For newbies we recommend the course "R Programming":

install_course("R Programming")
swirl()

Then you can continue wiht other courses for data cleasning, exploratory data analysis, statistics and data analysis. Podéis profundizar después con:

install_course("Getting_and_Cleaning_Data")
install_course("Exploratory_Data_Analysis")
install_course("Open_Intro/Overview_of_Statistics")
install_course("Data_Analysis")

Interesting packages

• Rdimtools: Dimension Reduction and Estimation Methods
• mlr: Machine Learning in R
• caret: Classification and Regression Training, a book for The caret Package and caretEnsemble: Ensembles of Caret Models
• lime: Local Interpretable Model-Agnostic Explanations
• ShapleyR
• breakDown: Model Agnostic Explainers for Individual Predictions and https://pbiecek.github.io/breakDown/
• DALEX: Descriptive mAchine Learning EXplanations and https://pbiecek.github.io/DALEX_docs/
• ceterisParibus and https://pbiecek.github.io/ceterisParibus/index.html

Reference Cards, Guides & Cheatsheets

• RStudio IDE Cheatsheet
• R Reference Card
• Rmarkdown Reference Guide
• Rmarkdown Cheatsheet Guide
• Data Wrangling Cheatsheet
• Shiny Cheatsheet
• Devtools Cheatsheet
• Data Visualization Cheatsheet with ggplot2

Libraries

• Pandas
• scikit-learn
• IPython
• matplotlib
• SciPy
• NumPy
• PyTorch
• Dask

General

Courses

• UCL Course on RL
• DeepMind/UCL course on RL
• Learn with Google AI
• https://www.yan-holtz.com/teaching
• https://learningstatisticswithr.com
• https://m-clark.github.io/mixed-models-with-R/
• https://noamross.github.io/gams-in-r-course/
• https://missing.csail.mit.edu

Books

• Stephen Boyd and Lieven Vandenberghe. 2014. Convex Optimization. Cambridge University Press
• Allen B. 2012. Downey. Think Bayes. O'Reilly
• Intuitive ML and Big Data in C++, Scala, Java and Python
• The Little Book of LDA
• Data Science Live Book

Machine Learning

• Tour of Machine Learning Algorithms
• Evaluation of Deep Learning Toolkits
• List of recommender systems
• Interactive Periodic Table of Machine Learning Libraries
• Google's Seedbank
• Google's Distill
• Reinforcement Learning: an Overview

Data Visualization

• Data Visualisation Tools

Data Sets

• Enigma
• Kaggle Datasets
• Yahoo Datasets
• Million Songs Dataset
• Open Football
• Global Earthquake Archive
• R Datasets
• Rstudio Babynames
• Rstudio Fueleconomy
• Rstudio NASA Weather
• Rstudio NYC Flights
• DataMartker Time Series Data Library
• Datasets from the book: A Handbook of Small Data Sets
• M&M Data
• Marvel Universe Social Graph
• UCI Network Data Repository
• UC Irvine Machine Learning Repository
• The Mondial Database
• Europa Open Data
• USA Data Gov
• Facebook Graph API
• Quandl
• Dbpedia
• Stanford Large Network Dataset Collection
• UCI ML Repository
• Reddit Datasets
• The Stanford Question Answering Dataset
• Trump Speeches
• LAMBADA Dataset
• University of Edinburugh - School of Informatics - Data Mining datsets
• Time Series Data Library - Data Market
• Data for Everyone - Crowdflower
• Datasets from MILA Lab - University of Montreal, Canada
• Open Data Stack Exchange
• dslabs package
• The Tate Collection
• Data Portals

Awesome Lists

• Pretty Awesome Lists
• Awesome Big Data
• Awesome Big Data Ecosystem
• Awesome Hadoop
• Awesome Data Science
• Awesome Analytics
• Awesome Machine Learning
• Awesome Dataviz
• Awesome python
• Awesome R
• Awesome Public Datasets
• Awesome Responsible AI

Blogs and podcast

• No So Standard deviations
• Data Skeptic
• The Analytics Engineering Podcast
• Apple Machine Learning Journal
• Data Science Leaders

Visual explanations

• Transformer explainer