awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

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awesome-python

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awesome

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😎 Awesome lists about all kinds of interesting topics

awesome-go

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data-science-ipython-notebooks

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Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

awesome-datascience

24,632

:memo: An awesome Data Science repository to learn and apply for real world problems.

Quick Overview

The academic/awesome-datascience repository is a curated list of resources for data science enthusiasts and professionals. It provides a comprehensive collection of tools, libraries, courses, books, and other materials related to various aspects of data science, including machine learning, statistics, data visualization, and more.

Pros

Extensive collection of resources covering a wide range of data science topics
Regularly updated with new and relevant content
Well-organized structure, making it easy to find specific resources
Community-driven project with contributions from data science professionals

Cons

Can be overwhelming for beginners due to the sheer volume of resources
Some links may become outdated over time
Lacks detailed descriptions or reviews of individual resources
May not cover all niche or specialized areas of data science

Note: As this is not a code library, the code example and quick start sections have been omitted.

Competitor Comparisons

awesome-python

221,658

An opinionated list of awesome Python frameworks, libraries, software and resources.

Pros of awesome-python

More comprehensive coverage of Python ecosystem, including web development, testing, and other non-data science topics
Larger community with more frequent updates and contributions
Better organization with clear categorization of libraries and tools

Cons of awesome-python

Less focused on data science specifically, which may make it harder to find relevant resources
Lacks educational resources and learning paths for data science beginners
Doesn't include as many data-specific tools and platforms

Code comparison

While both repositories are curated lists and don't contain much code, here's a sample of how they might differ in presenting a Python library:

awesome-python:

- [NumPy](https://numpy.org/) - A fundamental package for scientific computing with Python.

awesome-datascience:

#### Python
- [NumPy](https://numpy.org/) - NumPy is the fundamental package for scientific computing with Python.
  - [NumPy Tutorial](https://numpy.org/doc/stable/user/quickstart.html)

The awesome-datascience repository tends to include more context and learning resources for each tool, while awesome-python focuses on a concise list of libraries and frameworks.

awesome-machine-learning

65,690

A curated list of awesome Machine Learning frameworks, libraries and software.

Pros of awesome-machine-learning

More comprehensive coverage of machine learning topics and resources
Better organization with clear categorization by programming language
Includes sections on specific ML tasks like computer vision and NLP

Cons of awesome-machine-learning

Less focus on general data science concepts and tools
Fewer resources for beginners or those new to the field
Limited coverage of data visualization and exploratory data analysis

Code comparison

While both repositories primarily consist of curated lists rather than code, awesome-machine-learning does include some code snippets in its descriptions. For example:

awesome-machine-learning:

from sklearn import svm
X = [[0, 0], [1, 1]]
y = [0, 1]
clf = svm.SVC()
clf.fit(X, y)

awesome-datascience: No direct code examples are provided in the main README.

Summary

awesome-machine-learning is more focused on machine learning-specific resources and tools, organized by programming language. It offers a deeper dive into ML topics but may be less accessible for beginners. awesome-datascience provides a broader overview of data science, including more general concepts and beginner-friendly resources, but with less depth in specific ML areas.

awesome

323,302

😎 Awesome lists about all kinds of interesting topics

Pros of awesome

Broader scope, covering various topics beyond data science
Larger community with more contributors and frequent updates
Well-organized structure with clear categories and subcategories

Cons of awesome

Less focused on data science specifically
May be overwhelming for users looking for targeted data science resources
Potentially harder to find specific data science tools or libraries

Code comparison

Not applicable for these repositories, as they are curated lists of resources rather than code projects.

Summary

awesome-datascience is a specialized repository focused on data science resources, while awesome is a more comprehensive list covering a wide range of topics. awesome-datascience provides a curated collection of data science tools, libraries, and learning materials, making it easier for data scientists to find relevant resources. On the other hand, awesome offers a broader perspective and may be more suitable for users interested in exploring various fields beyond data science.

Both repositories are valuable in their own right, with awesome-datascience being more targeted and awesome offering a wider range of options. The choice between the two depends on the user's specific needs and interests. Data science enthusiasts may find awesome-datascience more relevant, while those looking for a diverse set of resources across multiple domains might prefer awesome.

awesome-go

130,604

A curated list of awesome Go frameworks, libraries and software

Pros of awesome-go

More comprehensive and extensive list of resources
Better organized with clear categories and subcategories
Regularly updated with active community contributions

Cons of awesome-go

Focused solely on Go, lacking cross-disciplinary resources
May be overwhelming for beginners due to its extensive nature
Less emphasis on academic and theoretical aspects

Code comparison

awesome-datascience typically includes more data-oriented code snippets:

import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
X = df.drop('target', axis=1)
y = df['target']

awesome-go focuses on Go-specific code examples:

package main

import (
    "fmt"
    "net/http"
)

func main() {
    http.HandleFunc("/", func(w http.ResponseWriter, r *http.Request) {
        fmt.Fprintf(w, "Hello, World!")
    })
    http.ListenAndServe(":8080", nil)
}

Both repositories serve as valuable resources for their respective domains, with awesome-go being more language-specific and awesome-datascience covering a broader range of data science topics across multiple programming languages and tools.

data-science-ipython-notebooks

27,106

Pros of data-science-ipython-notebooks

Provides hands-on, executable code examples in Jupyter notebooks
Covers a wide range of data science topics with practical implementations
Allows users to run and experiment with code directly

Cons of data-science-ipython-notebooks

May become outdated if not regularly maintained
Focuses primarily on Python, limiting exposure to other languages and tools
Less comprehensive in terms of theoretical resources and learning materials

Code Comparison

data-science-ipython-notebooks:

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

awesome-datascience:

No direct code examples provided. This repository focuses on curating links to resources,
tutorials, and tools rather than providing executable code snippets.

Summary

data-science-ipython-notebooks offers practical, hands-on learning through executable Jupyter notebooks, making it ideal for those who prefer learning by doing. However, it may lack the breadth of resources and theoretical foundations found in awesome-datascience.

awesome-datascience provides a comprehensive collection of data science resources, covering a wider range of topics and tools. It's better suited for those seeking a broad overview of the field and various learning materials, but lacks the immediate practicality of executable code examples.

awesome-datascience

24,632

:memo: An awesome Data Science repository to learn and apply for real world problems.

Pros of awesome-datascience

More comprehensive and regularly updated resource list
Better organized with clear categories and subcategories
Includes a wider range of data science topics and tools

Cons of awesome-datascience

May be overwhelming for beginners due to the sheer volume of resources
Some links may become outdated over time
Less focus on academic-specific resources

Code comparison

Not applicable for these repositories as they are curated lists of resources rather than code-based projects.

Summary

awesome-datascience is a more extensive and well-organized repository for data science resources. It covers a broader range of topics and tools, making it suitable for both beginners and experienced practitioners. However, the sheer volume of information may be overwhelming for some users, and maintaining the accuracy of all links can be challenging.

Both repositories serve as valuable starting points for data science enthusiasts, with awesome-datascience offering a more comprehensive and diverse collection of resources. The choice between the two depends on the user's specific needs and level of expertise in the field.

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README

AWESOME DATA SCIENCE

An open-source Data Science repository to learn and apply towards solving real world problems.

This is a shortcut path to start studying Data Science. Just follow the steps to answer the questions, "What is Data Science and what should I study to learn Data Science?"

Sponsor	Pitch
---	Be the first to sponsor! `github@academic.io`

Link	Preview
What is Data Science @ O'reilly	Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: âhereâs a lot of data, what can you make from it?â
What is Data Science @ Quora	Data Science is a combination of a number of aspects of Data such as Technology, Algorithm development, and data interference to study the data, analyse it, and find innovative solutions to difficult problems. Basically Data Science is all about Analysing data and driving for business growth by finding creative ways.
The sexiest job of 21st century	Data scientists today are akin to Wall Street âquantsâ of the 1980s and 1990s. In those days people with backgrounds in physics and math streamed to investment banks and hedge funds, where they could devise entirely new algorithms and data strategies. Then a variety of universities developed masterâs programs in financial engineering, which churned out a second generation of talent that was more accessible to mainstream firms. The pattern was repeated later in the 1990s with search engineers, whose rarefied skills soon came to be taught in computer science programs.
Wikipedia	Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
How to Become a Data Scientist	Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientistâs role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.
a very short history of #datascience	The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science. The term âData Scienceâ has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term âData Scienceâ and its use, attempts to define it, and related terms.
Software Development Resources for Data Scientists	Data scientists concentrate on making sense of data through exploratory analysis, statistics, and models. Software developers apply a separate set of knowledge with different tools. Although their focus may seem unrelated, data science teams can benefit from adopting software development best practices. Version control, automated testing, and other dev skills help create reproducible, production-ready code and tools.
Data Scientist Roadmap	Data science is an excellent career choice in todayâs data-driven world where approx 328.77 million terabytes of data are generated daily. And this number is only increasing day by day, which in turn increases the demand for skilled data scientists who can utilize this data to drive business growth.

Link	Description
The Data Science Lifecycle Process	The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo
Data Science Lifecycle Template Repo	Template repository for data science lifecycle project
RexMex	A general purpose recommender metrics library for fair evaluation.
ChemicalX	A PyTorch based deep learning library for drug pair scoring.
PyTorch Geometric Temporal	Representation learning on dynamic graphs.
Little Ball of Fur	A graph sampling library for NetworkX with a Scikit-Learn like API.
Karate Club	An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.
ML Workspace	All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a Docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code)
Neptune.ai	Community-friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility.
steppy	Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces very simple interface that enables clean machine learning pipeline design.
steppy-toolkit	Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.
Datalab from Google	easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively.
Hortonworks Sandbox	is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials.
R	is a free software environment for statistical computing and graphics.
Tidyverse	is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.
RStudio	IDE â powerful user interface for R. Itâs free and open source, and works on Windows, Mac, and Linux.
Python - Pandas - Anaconda	Completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing
Pandas GUI	Pandas GUI
Scikit-Learn	Machine Learning in Python
NumPy	NumPy is fundamental for scientific computing with Python. It supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays.
Vaex	Vaex is a Python library that allows you to visualize large datasets and calculate statistics at high speeds.
SciPy	SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization.
Data Science Toolbox	Coursera Course
Data Science Toolbox	Blog
Wolfram Data Science Platform	Take numerical, textual, image, GIS or other data and give it the Wolfram treatment, carrying out a full spectrum of data science analysis and visualization and automatically generate rich interactive reportsâall powered by the revolutionary knowledge-based Wolfram Language.
Datadog	Solutions, code, and devops for high-scale data science.
Variance	Build powerful data visualizations for the web without writing JavaScript
Kite Development Kit	The Kite Software Development Kit (Apache License, Version 2.0), or Kite for short, is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem.
Domino Data Labs	Run, scale, share, and deploy your models â without any infrastructure or setup.
Apache Flink	A platform for efficient, distributed, general-purpose data processing.
Apache Hama	Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce.
Weka	Weka is a collection of machine learning algorithms for data mining tasks.
Octave	GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab)
Apache Spark	Lightning-fast cluster computing
Hydrosphere Mist	a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services.
Data Mechanics	A data science and engineering platform making Apache Spark more developer-friendly and cost-effective.
Caffe	Deep Learning Framework
Torch	A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT
Nervana's python based Deep Learning Framework	IntelÂ® Nervanaâ¢ reference deep learning framework committed to best performance on all hardware.
Skale	High performance distributed data processing in NodeJS
Aerosolve	A machine learning package built for humans.
Intel framework	IntelÂ® Deep Learning Framework
Datawrapper	An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at github.com
Tensor Flow	TensorFlow is an Open Source Software Library for Machine Intelligence
Natural Language Toolkit	An introductory yet powerful toolkit for natural language processing and classification
Annotation Lab	Free End-to-End No-Code platform for text annotation and DL model training/tuning. Out-of-the-box support for Named Entity Recognition, Classification, Relation extraction and Assertion Status Spark NLP models. Unlimited support for users, teams, projects, documents.
nlp-toolkit for node.js	This module covers some basic nlp principles and implementations. The main focus is performance. When we deal with sample or training data in nlp, we quickly run out of memory. Therefore every implementation in this module is written as stream to only hold that data in memory that is currently processed at any step.
Julia	high-level, high-performance dynamic programming language for technical computing
IJulia	a Julia-language backend combined with the Jupyter interactive environment
Apache Zeppelin	Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more
Featuretools	An open source framework for automated feature engineering written in python
Optimus	Cleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend.
Albumentations	Ð fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, and detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops.
DVC	An open-source data science version control system. It helps track, organize and make data science projects reproducible. In its very basic scenario it helps version control and share large data and model files.
Lambdo	is a workflow engine that significantly simplifies data analysis by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation.
Feast	A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving.
Polyaxon	A platform for reproducible and scalable machine learning and deep learning.
LightTag	Text Annotation Tool for teams
UBIAI	Easy-to-use text annotation tool for teams with most comprehensive auto-annotation features. Supports NER, relations and document classification as well as OCR annotation for invoice labeling
Trains	Auto-Magical Experiment Manager, Version Control & DevOps for AI
Hopsworks	Open-source data-intensive machine learning platform with a feature store. Ingest and manage features for both online (MySQL Cluster) and offline (Apache Hive) access, train and serve models at scale.
MindsDB	MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code.
Lightwood	A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with an objective to build predictive models with one line of code.
AWS Data Wrangler	An open-source Python package that extends the power of Pandas library to AWS connecting DataFrames and AWS data related services (Amazon Redshift, AWS Glue, Amazon Athena, Amazon EMR, etc).
Amazon Rekognition	AWS Rekognition is a service that lets developers working with Amazon Web Services add image analysis to their applications. Catalog assets, automate workflows, and extract meaning from your media and applications.
Amazon Textract	Automatically extract printed text, handwriting, and data from any document.
Amazon Lookout for Vision	Spot product defects using computer vision to automate quality inspection. Identify missing product components, vehicle and structure damage, and irregularities for comprehensive quality control.
Amazon CodeGuru	Automate code reviews and optimize application performance with ML-powered recommendations.
CML	An open source toolkit for using continuous integration in data science projects. Automatically train and test models in production-like environments with GitHub Actions & GitLab CI, and autogenerate visual reports on pull/merge requests.
Dask	An open source Python library to painlessly transition your analytics code to distributed computing systems (Big Data)
Statsmodels	A Python-based inferential statistics, hypothesis testing and regression framework
Gensim	An open-source library for topic modeling of natural language text
spaCy	A performant natural language processing toolkit
Grid Studio	Grid studio is a web-based spreadsheet application with full integration of the Python programming language.
Python Data Science Handbook	Python Data Science Handbook: full text in Jupyter Notebooks
Shapley	A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
DAGsHub	A platform built on open source tools for data, model and pipeline management.
Deepnote	A new kind of data science notebook. Jupyter-compatible, with real-time collaboration and running in the cloud.
Valohai	An MLOps platform that handles machine orchestration, automatic reproducibility and deployment.
PyMC3	A Python Library for Probabalistic Programming (Bayesian Inference and Machine Learning)
PyStan	Python interface to Stan (Bayesian inference and modeling)
hmmlearn	Unsupervised learning and inference of Hidden Markov Models
Chaos Genius	ML powered analytics engine for outlier/anomaly detection and root cause analysis
Nimblebox	A full-stack MLOps platform designed to help data scientists and machine learning practitioners around the world discover, create, and launch multi-cloud apps from their web browser.
Towhee	A Python library that helps you encode your unstructured data into embeddings.
LineaPy	Ever been frustrated with cleaning up long, messy Jupyter notebooks? With LineaPy, an open source Python library, it takes as little as two lines of code to transform messy development code into production pipelines.
envd	ðï¸ machine learning development environment for data science and AI/ML engineering teams
Explore Data Science Libraries	A search engine ð tool to discover & find a curated list of popular & new libraries, top authors, trending project kits, discussions, tutorials & learning resources
MLEM	ð¶ Version and deploy your ML models following GitOps principles
MLflow	MLOps framework for managing ML models across their full lifecycle
cleanlab	Python library for data-centric AI and automatically detecting various issues in ML datasets
AutoGluon	AutoML to easily produce accurate predictions for image, text, tabular, time-series, and multi-modal data
Arize AI	Arize AI community tier observability tool for monitoring machine learning models in production and root-causing issues such as data quality and performance drift.
Aureo.io	Aureo.io is a low-code platform that focuses on building artificial intelligence. It provides users with the capability to create pipelines, automations and integrate them with artificial intelligence models â all with their basic data.
ERD Lab	Free cloud based entity relationship diagram (ERD) tool made for developers.
Arize-Phoenix	MLOps in a notebook - uncover insights, surface problems, monitor, and fine tune your models.
Comet	An MLOps platform with experiment tracking, model production management, a model registry, and full data lineage to support your ML workflow from training straight through to production.
CometLLM	Log, track, visualize, and search your LLM prompts and chains in one easy-to-use, 100% open-source tool.
Synthical	AI-powered collaborative environment for research. Find relevant papers, create collections to manage bibliography, and summarize content â all in one place
teeplot	Workflow tool to automatically organize data visualization output

Twitter	Description
Big Data Combine	Rapid-fire, live tryouts for data scientists seeking to monetize their models as trading strategies
Big Data Mania	Data Viz Wiz, Data Journalist, Growth Hacker, Author of Data Science for Dummies (2015)
Big Data Science	Big Data, Data Science, Predictive Modeling, Business Analytics, Hadoop, Decision and Operations Research.
Charlie Greenbacker	Director of Data Science at @ExploreAltamira
Chris Said	Data scientist at Twitter
Clare Corthell	Dev, Design, Data Science @mattermark #hackerei
DADI Charles-Abner	#datascientist @Ekimetrics. , #machinelearning #dataviz #DynamicCharts #Hadoop #R #Python #NLP #Bitcoin #dataenthousiast
Data Science Central	Data Science Central is the industry's single resource for Big Data practitioners.
Data Science London	Data Science. Big Data. Data Hacks. Data Junkies. Data Startups. Open Data
Data Science Renee	Documenting my path from SQL Data Analyst pursuing an Engineering Master's Degree to Data Scientist
Data Science Report	Mission is to help guide & advance careers in Data Science & Analytics
Data Science Tips	Tips and Tricks for Data Scientists around the world! #datascience #bigdata
Data Vizzard	DataViz, Security, Military
DataScienceX
deeplearning4j
DJ Patil	White House Data Chief, VP @ RelateIQ.
Domino Data Lab
Drew Conway	Data nerd, hacker, student of conflict.
Emilio Ferrara	#Networks, #MachineLearning and #DataScience. I work on #Social Media. Postdoc at @IndianaUniv
Erin Bartolo	Running with #BigData--enjoying a love/hate relationship with its hype. @iSchoolSU #DataScience Program Mgr.
Greg Reda	Working @ GrubHub about data and pandas
Gregory Piatetsky	KDnuggets President, Analytics/Big Data/Data Mining/Data Science expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, part-time philosopher.
Hadley Wickham	Chief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University.
Hakan Kardas	Data Scientist
Hilary Mason	Data Scientist in Residence at @accel.
Jeff Hammerbacher	ReTweeting about data science
John Myles White	Scientist at Facebook and Julia developer. Author of Machine Learning for Hackers and Bandit Algorithms for Website Optimization. Tweets reflect my views only.
Juan Miguel Lavista	Principal Data Scientist @ Microsoft Data Science Team
Julia Evans	Hacker - Pandas - Data Analyze
Kenneth Cukier	The Economist's Data Editor and co-author of Big Data (http://www.big-data-book.com/).
Kevin Davenport	Organizer of https://www.meetup.com/San-Diego-Data-Science-R-Users-Group/
Kevin Markham	Data science instructor, and founder of Data School
Kim Rees	Interactive data visualization and tools. Data flaneur.
Kirk Borne	DataScientist, PhD Astrophysicist, Top #BigData Influencer.
Linda Regber	Data storyteller, visualizations.
Luis Rei	PhD Student. Programming, Mobile, Web. Artificial Intelligence, Intelligent Robotics Machine Learning, Data Mining, Natural Language Processing, Data Science.
Mark Stevenson	Data Analytics Recruitment Specialist at Salt (@SaltJobs) Analytics - Insight - Big Data - Data science
Matt Harrison	Opinions of full-stack Python guy, author, instructor, currently playing Data Scientist. Occasional fathering, husbanding, organic gardening.
Matthew Russell	Mining the Social Web.
Mert NuhoÄlu	Data Scientist at BizQualify, Developer
Monica Rogati	Data @ Jawbone. Turned data into stories & products at LinkedIn. Text mining, applied machine learning, recommender systems. Ex-gamer, ex-machine coder; namer.
Noah Iliinsky	Visualization & interaction designer. Practical cyclist. Author of vis books: https://www.oreilly.com/pub/au/4419
Paul Miller	Cloud Computing/ Big Data/ Open Data Analyst & Consultant. Writer, Speaker & Moderator. Gigaom Research Analyst.
Peter Skomoroch	Creating intelligent systems to automate tasks & improve decisions. Entrepreneur, ex-Principal Data Scientist @LinkedIn. Machine Learning, ProductRei, Networks
Prash Chan	Solution Architect @ IBM, Master Data Management, Data Quality & Data Governance Blogger. Data Science, Hadoop, Big Data & Cloud.
Quora Data Science	Quora's data science topic
R-Bloggers	Tweet blog posts from the R blogosphere, data science conferences, and (!) open jobs for data scientists.
Rand Hindi
Randy Olson	Computer scientist researching artificial intelligence. Data tinkerer. Community leader for @DataIsBeautiful. #OpenScience advocate.
Recep Erol	Data Science geek @ UALR
Ryan Orban	Data scientist, genetic origamist, hardware aficionado
Sean J. Taylor	Social Scientist. Hacker. Facebook Data Science Team. Keywords: Experiments, Causal Inference, Statistics, Machine Learning, Economics.
Silvia K. Spiva	#DataScience at Cisco
Harsh B. Gupta	Data Scientist at BBVA Compass
Spencer Nelson	Data nerd
Talha Oz	Enjoys ABM, SNA, DM, ML, NLP, HI, Python, Java. Top percentile Kaggler/data scientist
Tasos Skarlatidis	Complex Event Processing, Big Data, Artificial Intelligence and Machine Learning. Passionate about programming and open-source.
Terry Timko	InfoGov; Bigdata; Data as a Service; Data Science; Open, Social & Business Data Convergence
Tony Baer	IT analyst with Ovum covering Big Data & data management with some systems engineering thrown in.
Tony Ojeda	Data Scientist , Author , Entrepreneur. Co-founder @DataCommunityDC. Founder @DistrictDataLab. #DataScience #BigData #DataDC
Vamshi Ambati	Data Science @ PayPal. #NLP, #machinelearning; PhD, Carnegie Mellon alumni (Blog: https://allthingsds.wordpress.com )
Wes McKinney	Pandas (Python Data Analysis library).
WileyEd	Senior Manager - @Seagate Big Data Analytics @McKinsey Alum #BigData + #Analytics Evangelist #Hadoop, #Cloud, #Digital, & #R Enthusiast
WNYC Data News Team	The data news crew at @WNYC. Practicing data-driven journalism, making it visual, and showing our work.
Alexey Grigorev	Data science author
Ä°lker Arslan	Data science author. Shares mostly about Julia programming
INEVITABLE	AI & Data Science Start-up Company based in England, UK

Preview	Description
	Key differences of a data scientist vs. data engineer
	A visual guide to Becoming a Data Scientist in 8 Steps by DataCamp (img)
	Mindmap on required skills (img)
	Swami Chandrasekaran made a Curriculum via Metro map.
	by @kzawadz via twitter
	By Data Science Central
	Data Science Wars: R vs Python
	How to select statistical or machine learning techniques
	Choosing the Right Estimator
	The Data Science Industry: Who Does What
	Data Science ~~Venn~~ Euler Diagram
	Different Data Science Skills and Roles from this article by Springboard
	A simple and friendly way of teaching your non-data scientist/non-statistician colleagues how to avoid mistakes with data. From Geckoboard's Data Literacy Lessons.

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README

AWESOME DATA SCIENCE

Sponsors

Table of Contents

What is Data Science?

Where do I Start?

Real World

Disaster

Training Resources

Tutorials

Free Courses

MOOC's

Intensive Programs

Colleges

The Data Science Toolbox

Algorithms

Three kinds of Machine Learning Systems

Comparison

Supervised Learning

Unsupervised Learning

Semi-Supervised Learning

Reinforcement Learning

Data Mining Algorithms

Deep Learning architectures

General Machine Learning Packages

Deep Learning Packages

PyTorch Ecosystem

TensorFlow Ecosystem

Keras Ecosystem

Visualization Tools

Miscellaneous Tools

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