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?”
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.
|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.
|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.
Our favorite programming language is Python nowadays for #DataScience. Python’s - Pandas library has full functionalities for collecting and analyzing data. We use Anaconda to play with data and to create applications.
These are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it.
|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
|PyTorch Geometric Temporal
|Representation learning on dynamic graphs.
|Little Ball of Fur
|A graph sampling library for NetworkX with a Scikit-Learn like API.
|An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.
|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.
|Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces very simple interface that enables clean machine learning pipeline design.
|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.
|is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials.
|is a free software environment for statistical computing and graphics.
|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
|Machine Learning in Python
|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.
|SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization.
|Data Science Toolbox
|Data Science Toolbox
|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.
|Solutions, code, and devops for high-scale data science.
|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.
|A platform for efficient, distributed, general-purpose data processing.
|Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce.
|Weka is a collection of machine learning algorithms for data mining tasks.
|GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab)
|Lightning-fast cluster computing
|a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services.
|A data science and engineering platform making Apache Spark more developer-friendly and cost-effective.
|Deep Learning Framework
|A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT
|Nervana’s python based Deep Learning Framework
|High performance distributed data processing in NodeJS
|A machine learning package built for humans.
|Intel® Deep Learning Framework
|An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at github.com
|TensorFlow is an Open Source Software Library for Machine Intelligence
|Natural Language Toolkit
|An introductory yet powerful toolkit for natural language processing and classification
|nlp-toolkit for node.js
|high-level, high-performance dynamic programming language for technical computing
|a Julia-language backend combined with the Jupyter interactive environment
|Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more
|An open source framework for automated feature engineering written in python
|Cleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend.
|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.
|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.
|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.
|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.
|A platform for reproducible and scalable machine learning and deep learning.
|Text Annotation Tool for teams
|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
|Auto-Magical Experiment Manager, Version Control & DevOps for AI
|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 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.
|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).
|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.
|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.
|Automate code reviews and optimize application performance with ML-powered recommendations.
|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.
|An open source Python library to painlessly transition your analytics code to distributed computing systems (Big Data)
|A Python-based inferential statistics, hypothesis testing and regression framework
|An open-source library for topic modeling of natural language text
|A performant natural language processing toolkit
|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
|A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
|A platform built on open source tools for data, model and pipeline management.
|A new kind of data science notebook. Jupyter-compatible, with real-time collaboration and running in the cloud.
|An MLOps platform that handles machine orchestration, automatic reproducibility and deployment.
|A Python Library for Probabalistic Programming (Bayesian Inference and Machine Learning)
|Python interface to Stan (Bayesian inference and modeling)
|Unsupervised learning and inference of Hidden Markov Models
|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.
|Director of Data Science at @ExploreAltamira
|Data scientist at Twitter
|Dev, Design, Data Science @mattermark #hackerei
|#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
|DataViz, Security, Military
|White House Data Chief, VP @ RelateIQ.
|Domino Data Lab
|Data nerd, hacker, student of conflict.
|#Networks, #MachineLearning and #DataScience. I work on #Social Media. Postdoc at @IndianaUniv
|Running with #BigData–enjoying a love/hate relationship with its hype. @iSchoolSU #DataScience Program Mgr.
|Working @ GrubHub about data and pandas
|KDnuggets President, Analytics/Big Data/Data Mining/Data Science expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, part-time philosopher.
|Chief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University.
|Data Scientist in Residence at @accel.
|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
|Hacker - Pandas - Data Analyze
|The Economist’s Data Editor and co-author of Big Data (http://big-data-book.com ).
|Organizer of https://meetup.com/San-Diego-R-Users-Group/
|Data science instructor, and founder of Data School
|Interactive data visualization and tools. Data flaneur.
|DataScientist, PhD Astrophysicist, Top #BigData Influencer.
|Data story teller, visualizations.
|PhD Student. Programming, Mobile, Web. Artificial Intelligence, Intelligent Robotics Machine Learning, Data Mining, Natural Language Processing, Data Science.
|Data Analytics Recruitment Specialist at Salt (@SaltJobs) Analytics - Insight - Big Data - Datascience
|Opinions of full-stack Python guy, author, instructor, currently playing Data Scientist. Occasional fathering, husbanding, organic gardening.
|Mining the Social Web.
|Data Scientist at BizQualify, Developer
|Data @ Jawbone. Turned data into stories & products at LinkedIn. Text mining, applied machine learning, recommender systems. Ex-gamer, ex-machine coder; namer.
|Visualization & interaction designer. Practical cyclist. Author of vis books: http://www.oreilly.com/pub/au/4419
|Cloud Computing/ Big Data/ Open Data Analyst & Consultant. Writer, Speaker & Moderator. Gigaom Research Analyst.
|Creating intelligent systems to automate tasks & improve decisions. Entrepreneur, ex Principal Data Scientist @LinkedIn. Machine Learning, ProductRei, Networks
|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
|Tweet blog posts from the R blogosphere, data science conferences and (!) open jobs for data scientists.
|Computer scientist researching artificial intelligence. Data tinkerer. Community leader for @DataIsBeautiful. #OpenScience advocate.
|Data Science geek @ UALR
|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
|Enjoys ABM, SNA, DM, ML, NLP, HI, Python, Java. Top percentile kaggler/data scientist
|Complex Event Processing, Big Data, Artificial Intelligence and Machine Learning. Passionate about programming and open-source.
|InfoGov; Bigdata; Data as a Service; Data Science; Open, Social & Business Data Convergence
|IT analyst with Ovum covering Big Data & data management with some systems engineering thrown in.
|Data Scientist , Author , Entrepreneur. Co-founder @DataCommunityDC. Founder @DistrictDataLab. #DataScience #BigData #DataDC
|Data Science @ PayPal. #NLP, #machinelearning; PhD, Carnegie Mellon alumni (Blog: https://allthingsds.wordpress.com )
|Pandas (Python Data Analysis library).
|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.
|Data science author
Some data mining competition platforms
|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
|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.