Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
resources
A bunch of collated useful resources.
https://github.com/DataKind-BLR/resources
Last synced: 3 days ago
JSON representation
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Podcasts
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Data Science
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Data Visualization
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Machine Learning
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Python Programming
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R Programming
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Statistics
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Start From Here
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Tools
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Blogs
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Datasets
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APIs
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Data Lists
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Data Repositories
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Open Data Portals
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Courses
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Open Data Portals
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Competitions
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Open Data Portals
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Cheatsheets
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Databases
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Languages
- Basic Functions
- Statistics & Machine Learning
- Importing Data
- Python Basics
- Python Snippets
- keras
- matplotlib (Plotting)
- numpy (Data Analysis)
- pandas (Data Wrangling)
- pySpark (Spark DataFrames / SQL Basics)
- pySpark (RDD Basics)
- scikit-learn (Machine Learning)
- scipy (Linear Algebra)
- seaborn (statistical data visualization)
- Advanced R
- Basic R
- Data Management
- Data Mining
- Data Wrangling
- Functions for Regression Analysis
- Functions for Time Series Analysis
- Graph Sizing
- Importing Data
- Keras
- Regular Expressions and Pattern Matching Basics
- R Markdown
- RStudio IDE
- Syntax Comparison
- caret (Modeling and Machine Learning)
- cartograpy (Thematic Maps with Spatial Objects)
- data.table
- devtools (Package Development)
- dplyr (Data Transformation)
- eurostat (eurostat Database)
- ggplot2 (Data Visualization)
- h2o (Big Data and Parallel Processing)
- leaflet (Interactive Maps)
- mlr
- mosaic
- quanteda (Quantitative Analysis of Textual Data)
- randomizr (Random Assignment and Sampling)
- shiny
- sparlyr
- survminer (Survival Plots)
- testthat
- lubridate (Dates and Times)
- purrr (Apply Functions)
- stringr (String)
- Summary
- xplain (Statistical functions for XML data)
- xts (Time Series)
- bokeh (data visualization)
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Math
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Misc
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Books
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Concepts
- Big Data Now: Current Perspectives from O'Reilly Radar
- Database Explorations
- Database Fundamentals
- Databases, Types, and The Relational Model: The Third Manifesto
- Foundations of Databases
- Temporal Database Management by Christian S. Jensen
- The Theory of Relational Databases
- What is Database Design, Anyway?
- Data Mining Algorithms In R - Wikibooks
- Internet Advertising: An Interplay among Advertisers, Online Publishers, Ad Exchanges and Web Users
- Introduction to Data Science by Jeffrey Stanton
- School of Data Handbook
- Theory and Applications for Advanced Text Mining
- Information Retrieval: A Survey
- Creative Commons: a user guide by Simone Aliprandi
- Open Source Licensing Software Freedom and Intellectual Property Law by Lawrence Rosen
- The Public Domain: Enclosing the Commons of the Mind by James Boyle
- A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone
- A Brief Introduction to Neural Networks
- A Course in Machine Learning
- A First Encounter with Machine Learning
- Algorithms for Reinforcement Learning by Csaba Szepesvári
- Bayesian Reasoning and Machine Learning by David Barber
- Computer Vision by Dana Ballard, Chris Brown
- Computer Vision: Algorithms and Applications by Richard Szeliski
- Introduction to Machine Learning by Amnon Shashua
- Learning Deep Architectures for AI
- Machine Learning
- Machine Learning, Neural and Statistical Classification
- The LION Way: Machine Learning plus Intelligent Optimization
- The Bastards Book of Regular Expressions: Finding Patterns in Everyday Text by Dan Nguyen
- A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone
- RexEgg
- Information Retrieval: A Survey
- Computer Vision: Models, Learning, and Inference by Simon J.D. Prince
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Languages
- 20 Python Libraries You Aren't Using (But Should)
- A Beginner's Python Tutorial
- A Whirlwind Tour of Python by Jake VanderPlas
- Automate the Boring Stuff by Al Sweigart
- Building Machine Learning Systems with Python by Willi Richert & Luis Pedro Coelho, Packt
- Code Like a Pythonista: Idiomatic Python
- Data Structures and Algorithms in Python by B. R. Preiss
- Functional Programming in Python
- Google's Python Style Guide
- Hadoop with Python
- High Performance Python
- How to Make Mistakes in Python by Mike Pirnat
- Learn Pandas by Hernan Rojas
- Learn to Program Using Python by Cody Jackson
- Learn Tensorflow (IPython Notebooks)
- Learning Python by Fabrizio Romano, Packt
- Learning to Program
- Mining the Social Web - 2nd Edition (IPython Notebooks)
- Modeling Creativity: Case Studies in Python by Tom D. De Smedt
- Picking a Python Version: A Manifesto
- Practical Programming in Python by Jeffrey Elkner
- Python Practice Projects
- The Python Game Book
- Think Stats: Probability and Statistics for Programmers by Allen B. Downey
- Learning Statistics with R by Daniel Navarro
- Practical Regression and Anova using R by Julian J. Faraway
- Probabilistic Models in the Study of Language (with R Code)
- R for Spatial Analysis
- R Practicals
- R Programming
- R Programming for Data Science by Roger D. Peng
- The caret Package by Max Kuhn
- The R Manuals
- Developing Time-Oriented Database Applications in SQL
- SQL For Web Nerds
- SQL Notes for Professionals
- An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
- The Hitchhiker’s Guide to Python!
- Building Machine Learning Systems with Python by Willi Richert & Luis Pedro Coelho, Packt
- R Succinctly, Syncfusion
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Mathematics
- Advanced Algebra by Anthony W. Knapp
- Basic Algebra by Anthony W. Knapp
- Basics of Algebra, Topology, and Differential Calculus
- Lecture Notes of Linear Algebra by Dr. P. Shunmugaraj, IIT Kanpur
- Linear Algebra by Dr. Arbind K Lal, IIT Kanpur
- Linear Algebra
- Calculus Made Easy by Silvanus P. Thompson
- Differential Equations by Paul Dawkins
- Elementary Differential Equations by William F. Trench
- Ordinary Differential Equations
- An Introduction to the Theory of Numbers by Leo Moser
- Book of Proof by Richard Hammack
- Category Theory for the Sciences
- Computational Geometry by Sean Luke
- Graph Theory
- Mathematical Logic - an Introduction
- Mathematics, MTH101A by P. Shunmugaraj, IIT Kanpur
- Non-Uniform Random Variate Generation by Luc Devroye
- Power Programming with Mathematica by David B. Wagner
- CK-12 Probability and Statistics - Advanced
- Concepts & Applications of Inferential Statistics
- Hyperstat
- Introduction to Probability by Charles M. Grinstead and J. Laurie Snell
- Introduction to Probability and Statistics Spring 2014
- Introduction to Statistical Thought by Micheal Lavine
- Multivariate Statistics: Concepts, Models, and Applications - 3rd Web Edition by David W. Stockburger
- Probability and Statistics Cookbook
- Probability and Statistics EBook
- StatLect
- StatSoft
- The Little Handbook of Statistical Practice by Gerard E. Dallal, Ph.D
- Think Bayes: Bayesian Statistics Made Simple by Allen B. Downey
- Hyperstat
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Programming Languages
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