Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
https://github.com/academic/awesome-datascience
- Here
- What is Data Science @ O'reilly
- What is Data Science @ Quora
- The sexiest job of 21st century
- Wikipedia
- How to Become a Data Scientist
- a very short history of #datascience - -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 - ready code and tools._|
- Data Scientist Roadmap - 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._|
- Python - 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](https://www.anaconda.com) (invoked as `conda install`), a powerful package manager that can install packages for Python, R, and can download executables like Git.
- Scikit-Learn - 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](https://pandas.pydata.org/), one can collect and analyze their data into a convenient table format. [Numpy](https://numpy.org/) provides very fast tooling for mathematical operations, with a focus on vectors and matrices. [Seaborn](https://seaborn.pydata.org/), itself based on the [Matplotlib](https://matplotlib.org/) 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.
- deprem-ml - sourced [afet.org](https://afet.org).
- 1000 Data Science Projects
- #tidytuesday
- 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 - 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
- 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.
- Coursera Introduction to Data Science
- Data Science - 9 Steps Courses, A Specialization on Coursera
- Data Mining - 5 Steps Courses, A Specialization on Coursera
- Machine Learning – 5 Steps Courses, A Specialization on Coursera
- CS 109 Data Science
- OpenIntro
- CS 171 Visualization
- Process Mining: Data science in Action
- Oxford Deep Learning
- Oxford Deep Learning - video
- Oxford Machine Learning
- UBC Machine Learning - video
- Data Science Specialization
- Coursera Big Data Specialization
- Statistical Thinking for Data Science and Analytics by Edx
- Cognitive Class AI by IBM
- Udacity - Deep Learning
- Keras in Motion
- Microsoft Professional Program for Data Science
- COMP3222/COMP6246 - Machine Learning Technologies
- CS 231 - Convolutional Neural Networks for Visual Recognition
- Coursera Tensorflow in practice
- Coursera Deep Learning Specialization
- 365 Data Science Course
- Coursera Natural Language Processing Specialization
- Coursera GAN Specialization
- Codecademy's Data Science
- Linear Algebra - Linear Algebra course by Gilbert Strang
- A 2020 Vision of Linear Algebra (G. Strang)
- Python for Data Science Foundation Course
- Data Science: Statistics & Machine Learning
- Machine Learning Engineering for Production (MLOps)
- Recommender Systems Specialization from University of Minnesota
- Stanford Artificial Intelligence Professional Program
- Data Scientist with Python
- Programming with Julia
- Scaler Data Science & Machine Learning Program
- S2DS
- A list of colleges and universities offering degrees in data science.
- Data Science Degree @ Berkeley
- Data Science Degree @ UVA
- Data Science Degree @ Wisconsin
- BS in Data Science & Applications
- MS in Computer Information Systems @ Boston University
- MS in Business Analytics @ ASU Online
- MS in Applied Data Science @ Syracuse
- M.S. Management & Data Science @ Leuphana
- Master of Data Science @ Melbourne University
- Msc in Data Science @ The University of Edinburgh
- Master of Management Analytics @ Queen's University
- Master of Data Science @ Illinois Institute of Technology
- Master of Applied Data Science @ The University of Michigan
- Master Data Science and Artificial Intelligence @ Eindhoven University of Technology
- Master's Degree in Data Science and Computer Engineering @ University of Granada
- datacompy - DataComPy is a package to compare two Pandas DataFrames.
- Regression
- Linear Regression
- Ordinary Least Squares
- Logistic Regression
- Stepwise Regression
- Multivariate Adaptive Regression Splines
- Softmax Regression
- Locally Estimated Scatterplot Smoothing
- k-nearest neighbor
- Support Vector Machines
- Decision Trees
- ID3 algorithm
- C4.5 algorithm
- Ensemble Learning
- Boosting
- Stacking
- Bagging
- Random Forest
- AdaBoost
- Clustering
- Hierchical clustering
- k-means
- Density-based clustering
- Fuzzy clustering
- Mixture models
- Dimension Reduction
- Principal Component Analysis (PCA)
- t-SNE; t-distributed Stochastic Neighbor Embedding
- Factor Analysis
- Latent Dirichlet Allocation (LDA)
- Neural Networks
- Self-organizing map
- Adaptive resonance theory
- Hidden Markov Models (HMM)
- Clustering
- Generative models
- Low-density separation
- Laplacian regularization
- Heuristic approaches
- Q Learning
- SARSA (State-Action-Reward-State-Action) algorithm
- Temporal difference learning
- C4.5
- k-Means
- SVM (Support Vector Machine)
- Apriori
- EM (Expectation-Maximization)
- PageRank
- AdaBoost
- KNN (K-Nearest Neighbors)
- Naive Bayes
- CART (Classification and Regression Trees)
- Multilayer Perceptron
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Boltzmann Machines
- Autoencoder
- Generative Adversarial Network (GAN)
- Self-Organized Maps
- Transformer
- Conditional Random Field (CRF)
- scikit-learn
- scikit-multilearn
- sklearn-expertsys
- scikit-feature
- scikit-rebate
- seqlearn
- sklearn-bayes
- sklearn-crfsuite
- sklearn-deap
- sigopt_sklearn
- sklearn-evaluation
- scikit-image
- scikit-opt
- scikit-posthocs
- pystruct
- Shogun
- xLearn
- cuML
- causalml
- mlpack
- MLxtend
- modAL
- Sparkit-learn
- hyperlearn
- dlib
- imodels
- RuleFit
- pyGAM
- Deepchecks
- scikit-survival
- interpretable
- PyTorch
- torchvision
- torchtext
- torchaudio
- ignite
- PyTorchNet
- PyToune
- skorch
- PyVarInf
- pytorch_geometric
- GPyTorch
- pyro
- Catalyst
- pytorch_tabular
- Yolov3
- Yolov5
- Yolov8
- TensorFlow
- TensorLayer
- TFLearn
- Sonnet
- tensorpack
- TRFL
- Polyaxon
- NeuPy
- tfdeploy
- tensorflow-upstream
- TensorFlow Fold
- tensorlm
- TensorLight
- Mesh TensorFlow
- Ludwig
- TF-Agents
- TensorForce
- Keras
- keras-contrib
- Hyperas
- Elephas
- Hera
- Spektral
- qkeras
- keras-rl
- Talos
- altair
- addepar
- amcharts
- anychart
- bokeh
- Comet
- slemma
- cartodb
- Cube
- d3plus
- Data-Driven Documents(D3js)
- dygraphs
- ECharts
- exhibit
- gephi
- ggplot2
- Glue
- Google Chart Gallery
- highcarts
- import.io
- jqplot
- Matplotlib
- nvd3
- Netron
- Openrefine
- plot.ly
- raw
- Resseract Lite
- Seaborn
- techanjs
- Timeline
- variancecharts
- vida
- vizzu
- Wrangler
- r2d3
- NetworkX
- Redash
- C3
- TensorWatch
- geomap
- The Data Science Lifecycle Process
- Data Science Lifecycle Template Repo
- RexMex
- ChemicalX
- PyTorch Geometric Temporal
- Little Ball of Fur - Learn like API. |
- Karate Club - Learn like API. |
- ML Workspace - 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 - friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility. |
- steppy
- steppy-toolkit
- Datalab from Google
- Hortonworks Sandbox
- R
- Tidyverse
- RStudio
- Python - Pandas - Anaconda - ready Python distribution for large-scale data processing, predictive analytics, and scientific computing |
- Pandas GUI
- Scikit-Learn
- NumPy - dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays. |
- Vaex
- SciPy
- Data Science Toolbox
- Data Science Toolbox
- Wolfram Data Science Platform - based Wolfram Language. |
- Datadog - scale data science. |
- Variance
- Kite Development Kit
- Domino Data Labs
- Apache Flink - purpose data processing. |
- Apache Hama - Level open source project, allowing you to do advanced analytics beyond MapReduce. |
- Weka
- Octave - level interpreted language, primarily intended for numerical computations.(Free Matlab) |
- Apache Spark - fast cluster computing |
- Hydrosphere Mist
- Data Mechanics - friendly and cost-effective. |
- Caffe
- Torch
- Nervana's python based Deep Learning Framework
- Skale
- Aerosolve
- Intel framework
- Datawrapper
- Tensor Flow
- Natural Language Toolkit
- Annotation Lab - 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 - level, high-performance dynamic programming language for technical computing |
- IJulia - language backend combined with the Jupyter interactive environment |
- Apache Zeppelin - based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more |
- Featuretools
- Optimus - processing, feature engineering, exploratory data analysis and easy ML with PySpark backend. |
- Albumentations
- DVC - 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
- Feast
- Polyaxon
- LightTag
- UBIAI - 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 - Magical Experiment Manager, Version Control & DevOps for AI |
- Hopsworks - 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
- Lightwood
- AWS Data Wrangler - 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
- Amazon Textract
- Amazon Lookout for Vision
- Amazon CodeGuru - powered recommendations.|
- CML - like environments with GitHub Actions & GitLab CI, and autogenerate visual reports on pull/merge requests. |
- Dask
- Statsmodels - based inferential statistics, hypothesis testing and regression framework |
- Gensim - source library for topic modeling of natural language text |
- spaCy
- Grid Studio - based spreadsheet application with full integration of the Python programming language. |
- Python Data Science Handbook
- Shapley - driven framework to quantify the value of classifiers in a machine learning ensemble. |
- DAGsHub
- Deepnote - compatible, with real-time collaboration and running in the cloud. |
- Valohai
- PyMC3
- PyStan
- hmmlearn
- Chaos Genius
- Nimblebox - 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
- LineaPy
- envd
- Explore Data Science Libraries
- MLEM
- MLflow
- cleanlab - centric AI and automatically detecting various issues in ML datasets |
- AutoGluon - series, and multi-modal data |
- Arize AI - causing issues such as data quality and performance drift. |
- Aureo.io - 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
- Arize-Phoenix - uncover insights, surface problems, monitor, and fine tune your models. |
- Comet
- CometLLM - to-use, 100% open-source tool. |
- Synthical - powered collaborative environment for research. Find relevant papers, create collections to manage bibliography, and summarize content — all in one place |
- teeplot
- 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
- Exploring Data Science - free eBook sampler
- Exploring the Data Jungle - free eBook sampler
- Classic Computer Science Problems in Python
- Math for Programmers
- R in Action, Third Edition
- Data Science Bookcamp
- 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
- 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
- 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
- eBook sale - Save up to 45% on eBooks!
- Causal Machine Learning
- Managing ML Projects
- Causal Inference for Data Science
- Data for All
- 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
- AI Digest
- DataTalks.Club - related things. [Archive](https://us19.campaign-archive.com/home/?u=0d7822ab98152f5afc118c176&id=97178021aa).
- The Analytics Engineering Roundup
- 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
- Data Science London - profit organization dedicated to the free, open, dissemination of data science.
- Datawrangling
- Quora Data Science - Data Science Questions and Answers from experts
- Siah
- Louis Dorard
- Machine Learning Mastery
- Daniel Forsyth - Personal Blog
- Data Science Weekly - Weekly News Blog
- Revolution Analytics - Data Science Blog
- R Bloggers - R Bloggers
- The Practical Quant
- Yet Another Data Blog
- Spenczar - building to reporting.
- KD Nuggets
- Meta Brown - Personal Blog
- Data Scientist
- WhatSTheBigData
- Tevfik Kosar - Magnus Notitia
- New Data Scientist
- 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](https://chris-said.io/) - 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
- How to Become a Data Scientist
- Introduction to Data Science
- Intro to Data Science for Enterprise Big Data
- How to Interview a Data Scientist
- How to Share Data with a Statistician
- The Science of a Great Career in Data Science
- What Does a Data Scientist Do?
- Building Data Start-Ups: Fast, Big, and Focused
- How to win data science competitions with Deep Learning
- Full-Stack Data Scientist
- AI at Home
- AI Today
- Adversarial Learning
- Becoming a Data Scientist
- Chai time Data Science
- Data Crunch
- Data Engineering Podcast
- Data Science at Home
- Data Science Mixer
- Data Skeptic
- Data Stories
- Datacast
- DataFramed
- DataTalks.Club
- Gradient Dissent
- Learning Machines 101
- Let's Data (Brazil)
- Linear Digressions
- Not So Standard Deviations
- O'Reilly Data Show Podcast
- Partially Derivative
- Superdatascience
- The Data Engineering Show
- The Radical AI Podcast
- The Robot Brains Podcast
- What's The Point
- How AI Built This
- What is machine learning?
- Andrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning
- Data36 - Data Science for Beginners by Tomi Mester
- Deep Learning: Intelligence from Big Data
- Interview with Google's AI and Deep Learning 'Godfather' Geoffrey Hinton
- Introduction to Deep Learning with Python
- What is machine learning, and how does it work?
- Data School - Data Science Education
- Neural Nets for Newbies by Melanie Warrick (May 2015)
- Neural Networks video series by Hugo Larochelle
- Google DeepMind co-founder Shane Legg - Machine Super Intelligence
- Data Science Primer
- Data Science with Genetic Algorithms
- Data Science for Beginners
- DataTalks.Club
- Mildlyoverfitted - Tutorials on intermediate ML/DL topics
- mlops.community - Interviews of industry experts about production ML
- ML Street Talk - Unabashedly technical and non-commercial, so you will hear no annoying pitches.
- Neural networks by 3Blue1Brown
- Neural networks from scratch by Sentdex
- Manning Publications YouTube channel
- Ask Dr Chong: How to Lead in Data Science - Part 1
- Ask Dr Chong: How to Lead in Data Science - Part 2
- Ask Dr Chong: How to Lead in Data Science - Part 3
- Ask Dr Chong: How to Lead in Data Science - Part 4
- Ask Dr Chong: How to Lead in Data Science - Part 5
- Ask Dr Chong: How to Lead in Data Science - Part 6
- Regression Models: Applying simple Poisson regression
- Deep Learning Architectures
- Time Series Modelling and Analysis
- Data
- Big Data Scientist
- Data Science Day
- Data Science Academy
- Facebook Data Science Page
- Data Science London
- Data Science Technology and Corporation
- Data Science - Closed Group
- Center for Data Science
- Big data hadoop NOSQL Hive Hbase
- Analytics, Data Mining, Predictive Modeling, Artificial Intelligence
- Big Data Analytics using R
- Big Data Analytics with R and Hadoop
- Big Data Learnings
- Big Data, Data Science, Data Mining & Statistics
- BigData/Hadoop Expert
- Data Mining / Machine Learning / AI
- Data Mining/Big Data - Social Network Ana
- Vademecum of Practical Data Science
- Veri Bilimi Istanbul
- The Data Science Blog
- Big Data Combine - fire, live tryouts for data scientists seeking to monetize their models as trading strategies |
- Big Data Science
- Chris Said
- Clare Corthell
- DADI Charles-Abner
- Data Science Central
- Data Science London
- Data Science Renee
- Data Science Report
- Data Science Tips
- Data Vizzard
- DataScienceX
- DJ Patil
- Domino Data Lab
- Drew Conway
- Erin Bartolo - -enjoying a love/hate relationship with its hype. @iSchoolSU #DataScience Program Mgr. |
- Greg Reda
- Gregory Piatetsky - founder, was Chief Scientist at 2 startups, part-time philosopher. |
- Hadley Wickham
- Hakan Kardas
- Hilary Mason
- Jeff Hammerbacher
- John Myles White
- Juan Miguel Lavista
- Julia Evans - Pandas - Data Analyze |
- Kenneth Cukier - author of Big Data (http://www.big-data-book.com/). |
- Kevin Markham
- Kim Rees
- Kirk Borne
- Luis Rei
- Matt Harrison - stack Python guy, author, instructor, currently playing Data Scientist. Occasional fathering, husbanding, organic gardening. |
- Matthew Russell
- Mert Nuhoğlu
- Monica Rogati - gamer, ex-machine coder; namer. |
- Noah Iliinsky
- Paul Miller
- Peter Skomoroch - Principal Data Scientist @LinkedIn. Machine Learning, ProductRei, Networks |
- Prash Chan
- Quora Data Science
- R-Bloggers
- Rand Hindi
- Randy Olson
- Recep Erol
- Ryan Orban
- Sean J. Taylor
- Silvia K. Spiva
- Harsh B. Gupta
- Spencer Nelson
- Talha Oz
- Tasos Skarlatidis - source. |
- Terry Timko
- Tony Baer
- Tony Ojeda - founder @DataCommunityDC. Founder @DistrictDataLab. #DataScience #BigData #DataDC |
- Vamshi Ambati
- Wes McKinney
- WileyEd - @Seagate Big Data Analytics @McKinsey Alum #BigData + #Analytics Evangelist #Hadoop, #Cloud, #Digital, & #R Enthusiast |
- WNYC Data News Team - driven journalism, making it visual, and showing our work. |
- Alexey Grigorev
- İlker Arslan
- INEVITABLE - up Company based in England, UK |
- Open Data Science
- Loss function porn
- Machinelearning
- DataTalks.Club
- Women Who Code - Data Science
- Berkeley Institute for Data Science
- Kaggle
- DrivenData
- Analytics Vidhya
- InnoCentive
- Microprediction
- <img src="https://i.imgur.com/0OoLaa5.png" width="150" /> - differences-of-a-data-scientist-vs-data-engineer) |
- <img src="https://cloud.githubusercontent.com/assets/182906/19517857/604f88d8-960c-11e6-97d6-16c9738cb824.png" width="150" />
- <img src="https://i.imgur.com/W2t2Roz.png" width="150" />
- <img src="https://i.imgur.com/rb9ruaa.png" width="150" /> - a-data-scientist/). |
- <img src="https://i.imgur.com/XBgKF2l.png" width="150" />
- <img src="https://i.imgur.com/l9ZGtal.jpg" width="150" />
- <img src="https://i.imgur.com/TWkB4X6.png" width="150" />
- <img src="https://i.imgur.com/gtTlW5I.png" width="150" />
- <img src="https://scikit-learn.org/stable/_static/ml_map.png" width="150" />
- <img src="https://i.imgur.com/3JSyUq1.png" width="150" />
- <img src="https://i.imgur.com/DQqFwwy.png" width="150" />
- <img src="https://www.springboard.com/blog/wp-content/uploads/2016/03/20160324_springboard_vennDiagram.png" width="150" height="150" /> - science-career-paths-different-roles-industry/) by Springboard |
- <img src="https://data-literacy.geckoboard.com/assets/img/data-fallacies-to-avoid-preview.jpg" width="150" alt="Data Fallacies To Avoid" /> - data scientist/non-statistician colleagues [how to avoid mistakes with data](https://data-literacy.geckoboard.com/poster/). From Geckoboard's [Data Literacy Lessons](https://data-literacy.geckoboard.com/). |
- Academic Torrents
- ADS-B Exchange - Specific datasets for aircraft and Automatic Dependent Surveillance-Broadcast (ADS-B) sources.
- hadoopilluminated.com
- data.gov - The home of the U.S. Government's open data
- United States Census Bureau
- usgovxml.com
- enigma.com - Navigate the world of public data - Quickly search and analyze billions of public records published by governments, companies and organizations.
- datahub.io
- aws.amazon.com/datasets
- datacite.org
- The official portal for European data
- NASDAQ:DATA - Nasdaq Data Link A premier source for financial, economic and alternative datasets.
- figshare.com
- GeoLite Legacy Downloadable Databases
- Quora's Big Datasets Answer
- Public Big Data Sets
- Kaggle Datasets
- A Deep Catalog of Human Genetic Variation
- A community-curated database of well-known people, places, and things
- Google Public Data
- World Bank Data
- NYC Taxi data
- Open Data Philly
- grouplens.org
- UC Irvine Machine Learning Repository - contains data sets good for machine learning
- research-quality data sets
- National Centers for Environmental Information
- ClimateData.us
- r/datasets
- MapLight - provides a variety of data free of charge for uses that are freely available to the general public. Click on a data set below to learn more
- GHDx - Institute for Health Metrics and Evaluation - a catalog of health and demographic datasets from around the world and including IHME results
- St. Louis Federal Reserve Economic Data - FRED
- New Zealand Institute of Economic Research – Data1850
- Open Data Sources
- UNICEF Data
- undata
- NASA SocioEconomic Data and Applications Center - SEDAC
- The GDELT Project
- Sweden, Statistics
- StackExchange Data Explorer - an open source tool for running arbitrary queries against public data from the Stack Exchange network.
- SocialGrep - a collection of open Reddit datasets.
- San Fransisco Government Open Data
- IBM Asset Dataset
- Open data Index
- Public Git Archive
- GHTorrent
- Microsoft Research Open Data
- Open Government Data Platform India
- Google Dataset Search (beta)
- NAYN.CO Turkish News with categories
- Covid-19
- Covid-19 Google
- Enron Email Dataset
- 5000 Images of Clothes
- IBB Open Portal
- The Humanitarian Data Exchange
- Comic compilation
- Cartoons
- awesome-awesomeness
- Awesome Machine Learning
- lists
- awesome-dataviz
- awesome-python
- Data Science IPython Notebooks.
- awesome-r
- awesome-datasets
- awesome-Machine Learning & Deep Learning Tutorials
- Awesome Data Science Ideas
- Machine Learning for Software Engineers
- Community Curated Data Science Resources
- Awesome Machine Learning On Source Code
- Awesome Community Detection
- Awesome Graph Classification
- Awesome Decision Tree Papers
- Awesome Fraud Detection Papers
- Awesome Gradient Boosting Papers
- Awesome Computer Vision Models
- Awesome Monte Carlo Tree Search
- Glossary of common statistics and ML terms
- 100 NLP Papers
- Awesome Game Datasets
- Data Science Interviews Questions
- Awesome Explainable Graph Reasoning
- Top Data Science Interview Questions
- Awesome Drug Synergy, Interaction and Polypharmacy Prediction
- Deep Learning Interview Questions
- Top Future Trends in Data Science in 2023
- How Generative AI Is Changing Creative Work
- What is generative AI?
- Awesome Music Production
Programming Languages
Keywords
machine-learning
71
deep-learning
52
python
41
data-science
36
pytorch
22
tensorflow
17
scikit-learn
11
ml
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neural-network
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keras
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reinforcement-learning
10
artificial-intelligence
9
data-analysis
8
mlops
7
numpy
6
computer-vision
6
neural-networks
6
object-detection
6
ai
6
spark
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jupyter-notebook
5
image-processing
5
awesome-list
5
nlp
5
awesome
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pandas
5
explainable-ai
4
classifier
4
dataset
4
pyspark
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random-forest
4
data-visualization
4
machine-learning-algorithms
4
reproducibility
4
explainable-ml
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jupyter
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node-embedding
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network-science
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network-embedding
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deeplearning
4
graph-embedding
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data-mining
4
developer-tools
4
data
3
pipeline
3
deepwalk
3
distributed-computing
3
feature-engineering
3
llm
3
unsupervised-learning
3