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! [email protected] |

Table of Contents

What is Data Science?

^ back to top ^

Data Science is one of the hottest topics on the Computer and Internet farmland nowadays. People have gathered data from applications and systems until today and now is the time to analyze them. The next steps are producing suggestions from the data and creating predictions about the future. Here you can find the biggest question for Data Science and hundreds of answers from experts.

| 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: “here’s a lot of data, what can you make from it?” | | 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 “quants” 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 master’s 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 inter-disciplinary 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 scientist’s 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 “Data Science” 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 “Data Science” 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.|

Where do I Start?

^ back to top ^

While not strictly necessary, having a programming language is a crucial skill to be effective as a data scientist. Currently, the most popular language is Python, closely followed by R. Python is a general-purpose scripting language which sees applications in a wide variety of fields. R is a domain-specific language for statistics, which contains a lot of common statistics tools out of the box.

Python is by far the most popular language in science, due in no small part to the ease at which it can be used and the vibrant ecosystem of user-generated packages. To install packages, there are two main methods: Pip (invoked as pip install), the package manager that comes bundled with Python, and Anaconda (invoked as conda install), a powerful package manager that can install packages for Python, R, and can download executables like Git.

Unlike R, Python was not built from the ground up with data science in mind, but there are plenty of third party libraries to make up for this. A much more exhaustive list of packages can be found later in this document, but these four packages are a good set of choices to start your data science journey with: Scikit-Learn is a general-purpose data science package which implements the most popular algorithms - it also includes rich documentation, tutorials, and examples of the models it implements. Even if you prefer to write your own implementations, Scikit-Learn is a valuable reference to the nuts-and-bolts behind many of the common algorithms you'll find. With Pandas, one can collect and analyze their data into a convenient table format. Numpy provides very fast tooling for mathematical operations, with a focus on vectors and matrices. Seaborn, itself based on the Matplotlib package, is a quick way to generate beautiful visualizations of your data, with many good defaults available out of the box, as well as a gallery showing how to produce many common visualizations of your data.

When embarking on your journey to becoming a data scientist, the choice of language isn't particularly important, and both Python and R have their pros and cons. Pick a language you like, and check out one of the Free courses we've listed below!

Real World

^ back to top ^

Data science is a powerful tool that is utilized in various fields to solve real-world problems by extracting insights and patterns from complex data.


^ back to top ^

Training Resources

^ back to top ^

How do you learn data science? By doing data science, of course! Okay, okay - that might not be particularly helpful when you're first starting out. In this section, we've listed some learning resources, in a rough order from least to greatest commitment - Tutorials, Massively Open Online Courses (MOOCs), Intensive Programs, and Colleges.


^ back to top ^

Free Courses

^ back to top ^


^ back to top ^

Intensive Programs

^ back to top ^


^ back to top ^

The Data Science Toolbox

^ back to top ^

This section is a collection of packages, tools, algorithms, and other useful items in the data science world.


^ back to top ^

These are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it.

Supervised Learning

Unsupervised Learning

Semi-Supervised Learning

Reinforcement Learning

Data Mining Algorithms

Deep Learning architectures

General Machine Learning Packages

^ back to top ^

Deep Learning Packages

PyTorch Ecosystem

TensorFlow Ecosystem

Keras Ecosystem

Visualization Tools

^ back to top ^

Miscellaneous Tools

^ back to top ^

| 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) | | | 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 – powerful user interface for R. It’s free and open source, 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 generating rich interactive reports—all 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 — 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 | . | | Skale | High performance distributed data processing in NodeJS | | Aerosolve | A machine learning package built for humans. | | Intel framework | Intel® Deep Learning Framework | | Datawrapper | An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at | | 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 | . | | 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 | А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, 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 which 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 | 🏕️ machine learning development environment for data science and AI/ML engineering teams | | Explore Data Science Libraries | A search engine 🔎 tool to discover & find a curated list of popular & new libraries, top authors, trending project kits, discussions, tutorials & learning resources | | MLEM | 🐶 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. | | | 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 – 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. |

Literature and Media

^ back to top ^

This section includes some additional reading material, channels to watch, and talks to listen to.


^ back to top ^

Book Deals (Affiliated) 🛍

Journals, Publications and Magazines

^ back to top ^


^ back to top ^


^ back to top ^


^ back to top ^


^ back to top ^

YouTube Videos & Channels

^ back to top ^


^ back to top ^

Below are some Social Media links. Connect with other data scientists!

Facebook Accounts

^ back to top ^

Twitter Accounts

^ back to top ^

| 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 ( | | Kevin Davenport | Organizer of | | 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 story teller, 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 - Datascience | | 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 Nuhoğlu | 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: | | 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: ) | | 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 | | İlker Arslan | Data science author. Shares mostly about Julia programming |

Telegram Channels

^ back to top ^

Slack Communities


GitHub Groups

Data Science Competitions

Some data mining competition platforms



^ back to top ^

| 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. |


^ back to top ^


^ back to top ^

Other Awesome Lists