academic/awesome-datascience

Fork: 5918 Star: 25208 (更新于 2024-11-29 01:19:38)

license: MIT

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

最后发布版本： v2024.08.29.1 ( 2024-08-29 22:27:06)

GitHub网址

介绍
版本
相关

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`

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 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 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.
Data Scientist Roadmap	Data science is an excellent career choice in today’s 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.
Navigating Your Path to Becoming a Data Scientist	_Data science is one of the most in-demand careers today. With businesses increasingly relying on data to make decisions, the need for skilled data scientists has grown rapidly. Whether it’s tech companies, healthcare organizations, or even government institutions, data scientists play a crucial role in turning raw data into valuable insights. But how do you become a data scientist, especially if you’re just starting out? _

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 that 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.

Disaster

^ back to top ^

deprem-ml AYA: Açık Yazılım Ağı (+25k developers) is trying to help disaster response using artificial intelligence. Everything is open-sourced afet.org.

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 rough order from least to greatest commitment - Tutorials, Massively Open Online Courses (MOOCs), Intensive Programs, and Colleges.

Tutorials

^ back to top ^

1000 Data Science Projects you can run on the browser with IPython.
#tidytuesday A weekly data project aimed at the R ecosystem.
Data science your way
PySpark Cheatsheet
Machine Learning, Data Science and Deep Learning with Python
How To Label Data
Your Guide to Latent Dirichlet Allocation
Over 1000 Data Science Online Courses at Classpert Online Search Engine
Tutorials of source code from the book Genetic Algorithms with Python by Clinton Sheppard
Tutorials to get started on signal processing for machine learning
Realtime deployment Tutorial on Python time-series model deployment.
Python for Data Science: A Beginner’s Guide
Minimum Viable Study Plan for Machine Learning Interviews
Understand and Know Machine Learning Engineering by Building Solid Projects
12 free Data Science projects to practice Python and Pandas
Best CV/Resume for Data Science Freshers
Understand Data Science Course in Java
Data Analytics Interview Questions (Beginner to Advanced)
Top 100+ Data Science Interview Questions and Answers

Free Courses

^ back to top ^

Data Scientist with R
Data Scientist with Python
Genetic Algorithms OCW Course
AI Expert Roadmap - Roadmap to becoming an Artificial Intelligence Expert
Convex Optimization - Convex Optimization (basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory...)
Skillcombo - Data Science - 1000+ free online Data Science courses
Learning from Data - Introduction to machine learning covering basic theory, algorithms and applications
Kaggle - Learn about Data Science, Machine Learning, Python etc
ML Observability Fundamentals - Learn how to monitor and root-cause production ML issues.
Weights & Biases Effective MLOps: Model Development - Free Course and Certification for building an end-to-end machine using W&B
Python for Machine Learning - Start your journey to machine learning with Python, one of the most powerful programming languages.
Python for Data Science by Scaler - This course is designed to empower beginners with the essential skills to excel in today's data-driven world. The comprehensive curriculum will give you a solid foundation in statistics, programming, data visualization, and machine learning.
MLSys-NYU-2022 - Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022.
Hands-on Train and Deploy ML - A hands-on course to train and deploy a serverless API that predicts crypto prices.
LLMOps: Building Real-World Applications With Large Language Models - Learn to build modern software with LLMs using the newest tools and techniques in the field.
Prompt Engineering for Vision Models - Learn to prompt cutting-edge computer vision models with natural language, coordinate points, bounding boxes, segmentation masks, and even other images in this free course from DeepLearning.AI.
Data Science Course By IBM - Free resources and learn what data science is and how it’s used in different industries.

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.

Algorithms

^ 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.

Three kinds of Machine Learning Systems

Based on training with human supervision
Based on learning incrementally on fly
Based on data points comparison and pattern detection

Comparison

datacompy - DataComPy is a package to compare two Pandas DataFrames.

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)
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 – powerful user interface for R. It’s 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 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	Intel® Nervana™ 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	Intel® 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	А 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	🏕️ 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.
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 – 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.
Opik	Evaluate, test, and ship LLM applications across your dev and production lifecycles.
Synthical	AI-powered collaborative environment for research. Find relevant papers, create collections to manage bibliography, and summarize content — all in one place
teeplot	Workflow tool to automatically organize data visualization output
Streamlit	App framework for Machine Learning and Data Science projects
Gradio	Create customizable UI components around machine learning models
Weights & Biases	Experiment tracking, dataset versioning, and model management
DVC	Open-source version control system for machine learning projects
Optuna	Automatic hyperparameter optimization software framework
Ray Tune	Scalable hyperparameter tuning library
Apache Airflow	Platform to programmatically author, schedule, and monitor workflows
Prefect	Workflow management system for modern data stacks
Kedro	Open-source Python framework for creating reproducible, maintainable data science code
Hamilton	Lightweight library to author and manage reliable data transformations
SHAP	Game theoretic approach to explain the output of any machine learning model
LIME	Explaining the predictions of any machine learning classifier
flyte	Workflow automation platform for machine learning
dbt	Data build tool
SHAP	Game theoretic approach to explain the output of any machine learning model
LIME	Explaining the predictions of any machine learning classifier

Literature and Media

^ back to top ^

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

Books

^ back to top ^

Data Science From Scratch: First Principles with Python
Artificial Intelligence with Python - Tutorialspoint
Machine Learning from Scratch
Probabilistic Machine Learning: An Introduction
A Comprehensive Guide to Machine Learning
How to Lead in Data Science - Early Access
Fighting Churn With Data
Data Science at Scale with Python and Dask
Python Data Science Handbook
The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists
Think Like a Data Scientist
Introducing Data Science
Practical Data Science with R
Everyday Data Science & (cheaper PDF version)
Exploring Data Science - free eBook sampler
Exploring the Data Jungle - free eBook sampler
Classic Computer Science Problems in Python
Math for Programmers Early access
R in Action, Third Edition Early Access
Data Science Bookcamp Early access
Data Science Thinking: The Next Scientific, Technological and Economic Revolution
Applied Data Science: Lessons Learned for the Data-Driven Business
The Data Science Handbook
Essential Natural Language Processing - Early access
Mining Massive Datasets - free e-book comprehended by an online course
Pandas in Action - Early access
Genetic Algorithms and Genetic Programming
Advances in Evolutionary Algorithms - Free Download
Genetic Programming: New Approaches and Successful Applications - Free Download
Evolutionary Algorithms - Free Download
Advances in Genetic Programming, Vol. 3 - Free Download
Global Optimization Algorithms: Theory and Application - Free Download
Genetic Algorithms and Evolutionary Computation - Free Download
Convex Optimization - Convex Optimization book by Stephen Boyd - Free Download
Data Analysis with Python and PySpark - Early Access
R for Data Science
Build a Career in Data Science
Machine Learning Bookcamp - Early access
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Effective Data Science Infrastructure
Practical MLOps: How to Get Ready for Production Models
Data Analysis with Python and PySpark
Regression, a Friendly guide - Early Access
Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing
Data Science at the Command Line: Facing the Future with Time-Tested Tools
Machine Learning - CIn UFPE
Machine Learning with Python - Tutorialspoint
Deep Learning
Designing Cloud Data Platforms - Early Access
An Introduction to Statistical Learning with Applications in R
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Deep Learning with PyTorch
Neural Networks and Deep Learning
Deep Learning Cookbook
Introduction to Machine Learning with Python
Artificial Intelligence: Foundations of Computational Agents, 2nd Edition - Free HTML version
The Quest for Artificial Intelligence: A History of Ideas and Achievements - Free Download
Graph Algorithms for Data Science - Early Access
Data Mesh in Action - Early Access
Julia for Data Analysis - Early Access
Casual Inference for Data Science - Early Access
Regular Expression Puzzles and AI Coding Assistants by David Mertz
Dive into Deep Learning
Data for All
Interpretable Machine Learning: A Guide for Making Black Box Models Explainable - Free GitHub version
Foundations of Data Science Free Download
Comet for DataScience: Enhance your ability to manage and optimize the life cycle of your data science project
Software Engineering for Data Scientists - Early Access
Julia for Data Science - Early Access
An Introduction to Statistical Learning - Download Page
Machine Learning For Absolute Beginners
Unifying Business, Data, and Code: Designing Data Products with JSON Schema

Book Deals (Affiliated) 🛍

Journals, Publications and Magazines

^ back to top ^

ICML - International Conference on Machine Learning
GECCO - The Genetic and Evolutionary Computation Conference (GECCO)
epjdatascience
Journal of Data Science - an international journal devoted to applications of statistical methods at large
Big Data Research
Journal of Big Data
Big Data & Society
Data Science Journal
datatau.com/news - Like Hacker News, but for data
Data Science Trello Board
Medium Data Science Topic - Data Science related publications on medium
Towards Data Science Genetic Algorithm Topic -Genetic Algorithm related Publications towards Data Science
all AI news - The AI/ML/Big Data news aggregator platform

Newsletters

^ back to top ^

AI Digest. A weekly newsletter to keep up to date with AI, machine learning, and data science. Archive.
DataTalks.Club. A weekly newsletter about data-related things. Archive.
The Analytics Engineering Roundup. A newsletter about data science. Archive.

Bloggers

^ back to top ^

Wes McKinney - Wes McKinney Archives.
Matthew Russell - Mining The Social Web.
Greg Reda - Greg Reda Personal Blog
Kevin Davenport - Kevin Davenport Personal Blog
Julia Evans - Recurse Center alumna
Hakan Kardas - Personal Web Page
Sean J. Taylor - Personal Web Page
Drew Conway - Personal Web Page
Hilary Mason - Personal Web Page
Noah Iliinsky - Personal Blog
Matt Harrison - Personal Blog
Vamshi Ambati - AllThings Data Sciene
Prash Chan - Tech Blog on Master Data Management And Every Buzz Surrounding It
Clare Corthell - The Open Source Data Science Masters
Paul Miller Based in the UK and working globally, Cloud of Data's consultancy services help clients understand the implications of taking data and more to the Cloud.
Data Science London Data Science London is a non-profit organization dedicated to the free, open, dissemination of data science. We are the largest data science community in Europe. We are more than 3,190 data scientists and data geeks in our community.
Datawrangling by Peter Skomoroch. MACHINE LEARNING, DATA MINING, AND MORE
Quora Data Science - Data Science Questions and Answers from experts
Siah a PhD student at Berkeley
Louis Dorard a technology guy with a penchant for the web and for data, big and small
Machine Learning Mastery about helping professional programmers confidently apply machine learning algorithms to address complex problems.
Daniel Forsyth - Personal Blog
Data Science Weekly - Weekly News Blog
Revolution Analytics - Data Science Blog
R Bloggers - R Bloggers
The Practical Quant Big data
Yet Another Data Blog Yet Another Data Blog
Spenczar a data scientist at Twitch. I handle the whole data pipeline, from tracking to model-building to reporting.
KD Nuggets Data Mining, Analytics, Big Data, Data, Science not a blog a portal
Meta Brown - Personal Blog
Data Scientist is building the data scientist culture.
WhatSTheBigData is some of, all of, or much more than the above and this blog explores its impact on information technology, the business world, government agencies, and our lives.
Tevfik Kosar - Magnus Notitia
New Data Scientist How a Social Scientist Jumps into the World of Big Data
Harvard Data Science - Thoughts on Statistical Computing and Visualization
Data Science 101 - Learning To Be A Data Scientist
Kaggle Past Solutions
DataScientistJourney
NYC Taxi Visualization Blog
Learning Lover
Dataists
Data-Mania
Data-Magnum
P-value - Musings on data science, machine learning, and stats.
datascopeanalytics
Digital transformation
datascientistjourney
Data Mania Blog - The File Drawer - Chris Said's science blog
Emilio Ferrara's web page
DataNews
Reddit TextMining
Periscopic
Hilary Parker
Data Stories
Data Science Lab
Meaning of
Adventures in Data Land
DATA MINERS BLOG
Dataclysm
FlowingData - Visualization and Statistics
Calculated Risk
O'reilly Learning Blog
Dominodatalab
i am trask - A Machine Learning Craftsmanship Blog
Vademecum of Practical Data Science - Handbook and recipes for data-driven solutions of real-world problems
Dataconomy - A blog on the newly emerging data economy
Springboard - A blog with resources for data science learners
Analytics Vidhya - A full-fledged website about data science and analytics study material.
Occam's Razor - Focused on Web Analytics.
Data School - Data science tutorials for beginners!
Colah's Blog - Blog for understanding Neural Networks!
Sebastian's Blog - Blog for NLP and transfer learning!
Distill - Dedicated to clear explanations of machine learning!
Chris Albon's Website - Data Science and AI notes
Andrew Carr - Data Science with Esoteric programming languages
floydhub - Blog for Evolutionary Algorithms
Jingles - Review and extract key concepts from academic papers
nbshare - Data Science notebooks
Deep and Shallow - All things Deep and Shallow in Data Science
Loic Tetrel - Data science blog
Chip Huyen's Blog - ML Engineering, MLOps, and the use of ML in startups
Maria Khalusova - Data science blog
Aditi Rastogi - ML,DL,Data Science blog
Santiago Basulto - Data Science with Python
Akhil Soni - ML, DL and Data Science
Akhil Soni - ML, DL and Data Science

Presentations

^ back to top ^

Podcasts

^ back to top ^

YouTube Videos & Channels

^ back to top ^

Socialize

^ 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 (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 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: 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
İlker Arslan	Data science author. Shares mostly about Julia programming
INEVITABLE	AI & Data Science Start-up Company based in England, UK

Telegram Channels

^ back to top ^

Open Data Science – First Telegram Data Science channel. Covering all technical and popular staff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former.
Loss function porn — Beautiful posts on DS/ML theme with video or graphic visualization.
Machinelearning – Daily ML news.

Slack Communities

top

GitHub Groups

Berkeley Institute for Data Science

Data Science Competitions

Some data mining competition platforms

Fun

Infographics

^ 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.