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

Overview

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?"


What is 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.

Link Preview
What is Data Science @ O'reilly Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: “here’s a lot of data, what can you make from it?”
What is Data Science @ Quora Data Science is a combination of a number of aspects of Data such as Technology, Algorithm development, and data interference to study the data, analyse it, and find innovative solutions to difficult problems. Basically Data Science is all about Analysing data and driving for business growth by finding creative ways.
The sexiest job of 21st century Data scientists today are akin to Wall Street “quants” of the 1980s and 1990s. In those days people with backgrounds in physics and math streamed to investment banks and hedge funds, where they could devise entirely new algorithms and data strategies. Then a variety of universities developed master’s programs in financial engineering, which churned out a second generation of talent that was more accessible to mainstream firms. The pattern was repeated later in the 1990s with search engineers, whose rarefied skills soon came to be taught in computer science programs.
Wikipedia Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
How to Become a Data Scientist Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.
a very short history of #datascience The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science. The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms.

Learn Data Science

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.

Algorithms

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

Supervised Learning

  • Regression
  • Linear Regression
  • Ordinary Least Squares
  • Logistic Regression
  • Stepwise Regression
  • Multivariate Adaptive Regression Splines
  • Locally Estimated Scatterplot Smoothing
  • Classification
    • k-nearest neighbor
    • Support Vector Machines
    • Decision Trees
    • ID3 algorithm
    • C4.5 algorithm
  • Ensemble Learning
  • Boosting
  • Bagging
  • Random Forest
  • AdaBoost

Unsupervised Learning

  • Clustering
    • Hierchical clustering
    • k-means
    • Fuzzy clustering
    • Mixture models
  • Dimension Reduction
    • Principal Component Analysis (PCA)
    • t-SNE
  • Neural Networks
  • Self-organizing map
  • Adaptive resonance theory
  • Hidden Markov Models (HMM)

Semi-Supervised Learning

  • S3VM
  • Clustering
  • Generative models
  • Low-density separation
  • Laplacian regularization
  • Heuristic approaches

Reinforcement Learning

  • Q Learning
  • SARSA (State-Action-Reward-State-Action) algorithm
  • Temporal difference learning

Data Mining Algorithms

  • C4.5
  • k-Means
  • SVM
  • Apriori
  • EM
  • PageRank
  • AdaBoost
  • kNN
  • Naive Bayes
  • CART

Deep Learning architectures

  • Multilayer Perceptron
  • Convolutional Neural Network (CNN)
  • Recurrent Neural Network (RNN)
  • Boltzmann Machines
  • Autoencoder
  • Generative Adversarial Network (GAN)
  • Self-Organized Maps

COLLEGES

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Intensive Programs

MOOC's

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Tutorials

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Free Courses

Toolboxes - Environment

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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.
RStudio IDE – powerful user interface for R. It’s free and open source, works on Windows, Mac, and Linux.
Python - Pandas - Anaconda Completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing
Pandas GUI Pandas GUI
Scikit-Learn Machine Learning in Python
NumPy NumPy is fundamental for scientific computing with Python. It supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays.
Vaex Vaex is a Python library that allows you to visualize large datasets and calculate statistics at high speeds.
SciPy SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization.
Data Science Toolbox Coursera Course
Data Science Toolbox Blog
Wolfram Data Science Platform Take numerical, textual, image, GIS or other data and give it the Wolfram treatment, carrying out a full spectrum of data science analysis and visualization and automatically generating rich interactive reports—all powered by the revolutionary knowledge-based Wolfram Language.
Datadog Solutions, code, and devops for high-scale data science.
Variance Build powerful data visualizations for the web without writing JavaScript
Kite Development Kit The Kite Software Development Kit (Apache License, Version 2.0) , or Kite for short, is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem.
Domino Data Labs Run, scale, share, and deploy your models — without any infrastructure or setup.
Apache Flink A platform for efficient, distributed, general-purpose data processing.
Apache Hama Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce.
Weka Weka is a collection of machine learning algorithms for data mining tasks.
Octave GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab)
Apache Spark Lightning-fast cluster computing
Hydrosphere Mist a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services.
Data Mechanics A data science and engineering platform making Apache Spark more developer-friendly and cost-effective.
Caffe Deep Learning Framework
Torch A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT
Nervana's python based Deep Learning Framework .
Skale High performance distributed data processing in NodeJS
Aerosolve A machine learning package built for humans.
Intel framework Intel® Deep Learning Framework
Datawrapper An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at 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
nlp-toolkit for node.js .
Julia high-level, high-performance dynamic programming language for technical computing
IJulia a Julia-language backend combined with the Jupyter interactive environment
Apache Zeppelin Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more
Featuretools An open source framework for automated feature engineering written in python
Optimus Cleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend.
Albumentations А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops.
DVC An open-source data science version control system. It helps track, organize and make data science projects reproducible. In its very basic scenario it helps version control and share large data and model files.
Lambdo is a workflow engine which significantly simplifies data analysis by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation.
Feast A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving.
Polyaxon A platform for reproducible and scalable machine learning and deep learning.
LightTag Text Annotation Tool for teams
UBIAI Easy-to-use text annotation tool for teams with most comprehensive auto-annotation features. Supports NER, relations and document classification as well as OCR annotation for invoice labeling
Trains Auto-Magical Experiment Manager, Version Control & DevOps for AI
Hopsworks Open-source data-intensive machine learning platform with a feature store. Ingest and manage features for both online (MySQL Cluster) and offline (Apache Hive) access, train and serve models at scale.
MindsDB MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code.
Lightwood A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with an objective to build predictive models with one line of code.
AWS Data Wrangler An open-source Python package that extends the power of Pandas library to AWS connecting DataFrames and AWS data related services (Amazon Redshift, AWS Glue, Amazon Athena, Amazon EMR, etc).
Amazon Rekognition AWS Rekognition is a service that lets developers working with Amazon Web Services add image analysis to their applications. Catalog assets, automate workflows, and extract meaning from your media and applications.
Amazon Textract Automatically extract printed text, handwriting, and data from any document.
Amazon Lookout for Vision Spot product defects using computer vision to automate quality inspection.Identify missing product components, vehicle and structure damage, and irregularities for comprehensive quality control.
Amazon CodeGuru Automate code reviews and optimize application performance with ML-powered recommendations.
CML An open source toolkit for using continuous integration in data science projects. Automatically train and test models in production-like environments with GitHub Actions & GitLab CI, and autogenerate visual reports on pull/merge requests.
Dask An open source Python library to painlessly transition your analytics code to distributed computing systems (Big Data)
Statsmodels A Python-based inferential statistics, hypothesis testing and regression framework
Gensim An open-source library for topic modeling of natural language text
spaCy A performant natural language processing toolkit
Grid Studio Grid studio is a web-based spreadsheet application with full integration of the Python programming language.
Python Data Science Handbook Python Data Science Handbook: full text in Jupyter Notebooks
Shapley A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
DAGsHub A platform built on open source tools for data, model and pipeline management.
Deepnote A new kind of data science notebook. Jupyter-compatible, with real-time collaboration and running in the cloud.
Valohai An MLOps platform that handles machine orchestration, automatic reproducibility and deployment.
PyMC3 A Python Library for Probabalistic Programming (Bayesian Inference and Machine Learning)
PyStan Python interface to Stan (Bayesian inference and modeling)
hmmlearn Unsupervised learning and inference of Hidden Markov Models

Machine Learning in General Purpose

Deep Learning

pytorch

tensorflow

keras

Visualization Tools - Environments

Journals, Publications and Magazines

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Presentations

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Podcasts

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Books

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Socialize

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Bloggers

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Facebook Accounts

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Twitter Accounts

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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://big-data-book.com ).
Kevin Davenport Organizer of https://meetup.com/San-Diego-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 story teller, visualizations.
Luis Rei PhD Student. Programming, Mobile, Web. Artificial Intelligence, Intelligent Robotics Machine Learning, Data Mining, Natural Language Processing, Data Science.
Mark Stevenson Data Analytics Recruitment Specialist at Salt (@SaltJobs) Analytics - Insight - Big Data - Datascience
Matt Harrison Opinions of full-stack Python guy, author, instructor, currently playing Data Scientist. Occasional fathering, husbanding, organic gardening.
Matthew Russell Mining the Social Web.
Mert Nuhoğlu Data Scientist at BizQualify, Developer
Monica Rogati Data @ Jawbone. Turned data into stories & products at LinkedIn. Text mining, applied machine learning, recommender systems. Ex-gamer, ex-machine coder; namer.
Noah Iliinsky Visualization & interaction designer. Practical cyclist. Author of vis books: http://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

Newsletters

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Youtube Videos & Channels

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Telegram Channels

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  • 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 vizualization.
  • Machinelearning – Daily ML news.

Slack Communities

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Github Groups

Competitions

Some data mining competition platforms

Fun

Infographic

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 | | Data Fallacies To Avoid | 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. |

Data Sets

Comics

Awesome Data Science

Awesome Subscribe to new links

Hobby

Other Lists

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Comments
  • Check the Repo of Data Science

    Check the Repo of Data Science

    Hey Guys I just made a great repo about Data Science and its tool, so that repo need some alternation it would be great if you guys check that out and drop your suggestion so I'll add your point on it. Thanks ❤️

    https://github.com/BIGG000/Data-Science-Learning-Track

    opened by BIGG000 3
  • Small improvements

    Small improvements

    I added a piece of this image and a link to the original one. IDK, maybe I should have added the whole visual guide. Anyway, + 1 link, + link to this visual guide, + some tiny fixes.

    opened by demidovakatya 3
  • Document Cleanup and Reformatting

    Document Cleanup and Reformatting

    Thanks for creating this list, it's a great resource! I went through and organized the document a bit and added a bit of text to explain how to get started.

    Here's an overview of what changed:

    • Moved the Table of Contents to the start of the document
    • Moved the Awesome-List badge to under the title
    • Created a "Where do I Start?" section with some basic setup for a Python environment
    • Created a "Training Resources" section and moved the tutorials, free courses, MOOCs, intensive programs, and colleges into it
    • Created a "Data Science Toolbox" section and moved all the software and package links into it as different subsections
    • Created a "Literature and Media" section and moved the "books," "book deals," "journals,", "newsletters," "bloggers," "presentations,", "podcasts," and "youtube" sections in there
    • Rename "Other Lists" to "Other Awesome Lists" and moved the "Hobby" section into there
    • Updated some section header titles to be more descriptive
    • Removed a dead "subscribe for more links" URL (was in the old "Awesome Data Science" section)
    • Added descriptions to some of the new sections.
    • Miscellaneous formatting and typo fixes
    opened by AcylSilane 2
  • Update redirected links and

    Update redirected links and "Infographic" table error

    hi, in this list there is a lot of redirected links and lot of http://.

    i fixed those up like i did in https://github.com/sindresorhus/awesome/pull/2180 , https://github.com/troxler/awesome-css-frameworks/pull/66 and https://github.com/44bits/awesome-opensource-documents/pull/28. and i created a https://github.com/academic/awesome-datascience/issues/319 where i listed dead links and redirects that i dont know how to fix. please fix those up.

    please check if my edit is ok or not. if you have any complain please let me know.

    thanks for maintaining this list.

    opened by z00rat 2
  • unrelevant link

    unrelevant link

    I found wolframalpha on the visualization tools section, while wolframalpha is not viz tool, it's a computational intelligence more likely knowledge base. may i remove it?

    opened by andreaschandra 2
  • Validate pull requests with Travis

    Validate pull requests with Travis

    Hello, I wrote a tool that can validate README links (valid URLs, not duplicate). It can be run when someone submits a pull request.

    It is currently being used by

    • https://github.com/vsouza/awesome-ios
    • https://github.com/matteocrippa/awesome-swift
    • https://github.com/dkhamsing/open-source-ios-apps

    Examples

    • https://travis-ci.org/matteocrippa/awesome-swift/builds/96526196 ok ✅
    • https://travis-ci.org/matteocrippa/awesome-swift/builds/96722421 link redirected / rename 🔴
    • https://travis-ci.org/dkhamsing/open-source-ios-apps/builds/96763135 bad link / project deleted 🔴
    • https://travis-ci.org/dkhamsing/open-source-ios-apps/builds/95754715 dupe 🔴

    If you are interested, connect this repo to https://travis-ci.org/ and add a .travis.yml file to the project.

    See https://github.com/dkhamsing/awesome_bot for options, more information Feel free to leave a comment :smile:

    opened by awesome-bot 2
  • Data Science vs. Machine Learning

    Data Science vs. Machine Learning

    Both are awesome but there is a large overlap between these two:

    • https://github.com/okulbilisim/awesome-datascience
    • https://github.com/josephmisiti/awesome-machine-learning

    What would be the key differentiators? Any plan to merge the two awesome repos?

    question 
    opened by 0asa 2
  • Update README.md

    Update README.md

    Added "Transformer" and "CRF" to Deep Learning architectures and "Stanford Artificial Intelligence Professional Program" to MOOCS.

    opened by matthiasdroth 1
Releases(v2022.12.01)
  • v2022.12.01(Dec 1, 2022)

    What's Changed

    • Update README.md by @iamkunalpitale in https://github.com/academic/awesome-datascience/pull/356
    • Updated the Readme by @sharmaanj200 in https://github.com/academic/awesome-datascience/pull/357
    • Add Elements of Statistical Learning book by @jrinder42 in https://github.com/academic/awesome-datascience/pull/358
    • Add Survival Analysis ML Package by @jrinder42 in https://github.com/academic/awesome-datascience/pull/359
    • Remove duplicate MOOC by @osinkolu in https://github.com/academic/awesome-datascience/pull/361
    • Added ADS-B Exchange by @ajaj895 in https://github.com/academic/awesome-datascience/pull/362
    • added data science book by @dawoodwasif in https://github.com/academic/awesome-datascience/pull/363
    • Update README.md by @fakturk in https://github.com/academic/awesome-datascience/pull/364
    • Update README.md by @fakturk in https://github.com/academic/awesome-datascience/pull/365
    • Update README.md by @fakturk in https://github.com/academic/awesome-datascience/pull/366
    • Update README.md by @ilketaha in https://github.com/academic/awesome-datascience/pull/367
    • Update README.md by @ilketaha in https://github.com/academic/awesome-datascience/pull/368
    • Update README.md by @ilketaha in https://github.com/academic/awesome-datascience/pull/369
    • Update README.md by @ilketaha in https://github.com/academic/awesome-datascience/pull/370
    • Added links to dimensionality reduction algorithms by @RaInta in https://github.com/academic/awesome-datascience/pull/374
    • Added links to clustering algorithms by @RaInta in https://github.com/academic/awesome-datascience/pull/373
    • Added link to MLflow by @RaInta in https://github.com/academic/awesome-datascience/pull/372
    • Added link to ensemble methods by @RaInta in https://github.com/academic/awesome-datascience/pull/371

    New Contributors

    • @iamkunalpitale made their first contribution in https://github.com/academic/awesome-datascience/pull/356
    • @sharmaanj200 made their first contribution in https://github.com/academic/awesome-datascience/pull/357
    • @jrinder42 made their first contribution in https://github.com/academic/awesome-datascience/pull/358
    • @osinkolu made their first contribution in https://github.com/academic/awesome-datascience/pull/361
    • @ajaj895 made their first contribution in https://github.com/academic/awesome-datascience/pull/362
    • @dawoodwasif made their first contribution in https://github.com/academic/awesome-datascience/pull/363
    • @ilketaha made their first contribution in https://github.com/academic/awesome-datascience/pull/367

    Full Changelog: https://github.com/academic/awesome-datascience/compare/2022.10.14...v2022.12.01

    Source code(tar.gz)
    Source code(zip)
  • 2022.10.14(Oct 15, 2022)

    Your participation in this project brought huge value to the community. Thank you for your contribution.

    What's Changed

    • Update README.MD by @Rogerh91 in https://github.com/academic/awesome-datascience/pull/67
    • Update README.md by @jaytaylopub in https://github.com/academic/awesome-datascience/pull/70
    • Update README.md by @jaytaylopub in https://github.com/academic/awesome-datascience/pull/71
    • Added one more university in Sweden by @SunnyBingoMe in https://github.com/academic/awesome-datascience/pull/72
    • Added one Facebook account and one awesome list by @ujjwalkarn in https://github.com/academic/awesome-datascience/pull/75
    • Add new Data sets: New Zealand Institute of Economic Research Data1850 by @thibaudcolas in https://github.com/academic/awesome-datascience/pull/76
    • Updated README.md by @jaytaylopub in https://github.com/academic/awesome-datascience/pull/77
    • add Data School to Bloggers section by @justmarkham in https://github.com/academic/awesome-datascience/pull/78
    • add Kevin Markham to Twitter section by @justmarkham in https://github.com/academic/awesome-datascience/pull/79
    • remove duplicate Google data set by @fredkelly in https://github.com/academic/awesome-datascience/pull/80
    • Update link Data Science Degree @ Berkeley #81 by @Devinsuit in https://github.com/academic/awesome-datascience/pull/82
    • Add "Neural Networks video series" by @jbenjos in https://github.com/academic/awesome-datascience/pull/83
    • Add "Awesome Data Science Ideas" list by @JosPolfliet in https://github.com/academic/awesome-datascience/pull/85
    • Added "Machine Learning for Software Engineers" by @ZuzooVn in https://github.com/academic/awesome-datascience/pull/86
    • Adding competition section. by @sara-02 in https://github.com/academic/awesome-datascience/pull/73
    • Add skale, high performance distributed data processing in NodeJS by @mvertes in https://github.com/academic/awesome-datascience/pull/87
    • NumPy and SciPy python libraries by @gokhanm in https://github.com/academic/awesome-datascience/pull/88
    • Add Hydrosphere Mist by @spushkarev in https://github.com/academic/awesome-datascience/pull/89
    • Update links by @Devinsuit in https://github.com/academic/awesome-datascience/pull/90
    • Add of LITS Dataset by @PatrickChrist in https://github.com/academic/awesome-datascience/pull/91
    • Added blogs for understanding Neural Networks ! by @sarthusarth in https://github.com/academic/awesome-datascience/pull/93
    • small correction with the line break by @danklotz in https://github.com/academic/awesome-datascience/pull/92
    • Remove dot by @techmexdev in https://github.com/academic/awesome-datascience/pull/95
    • Minor grammar correction by @techmexdev in https://github.com/academic/awesome-datascience/pull/94
    • Add Trello Board and Datatau News by @murat in https://github.com/academic/awesome-datascience/pull/96
    • Added Podcasts Section by @kofoide in https://github.com/academic/awesome-datascience/pull/97
    • Added Data Science Medium Topic by @murat in https://github.com/academic/awesome-datascience/pull/98
    • Add zeppelin to tools and remove some dots by @sravan-s in https://github.com/academic/awesome-datascience/pull/99
    • Enigma.io to Enigma by @indiakerle in https://github.com/academic/awesome-datascience/pull/100
    • Add a books section by @jwood803 in https://github.com/academic/awesome-datascience/pull/101
    • Update README.md with one MOOC and a bunch of books by @Charismatron in https://github.com/academic/awesome-datascience/pull/102
    • One more book to add :) by @Charismatron in https://github.com/academic/awesome-datascience/pull/103
    • Fixed some small Markdown issues by @sheilnaik in https://github.com/academic/awesome-datascience/pull/104
    • adding skikit-learn choosing estimator infographic by @lhayhurst in https://github.com/academic/awesome-datascience/pull/105
    • fixed bad link by @lhayhurst in https://github.com/academic/awesome-datascience/pull/106
    • Add Featuretools to tools by @kmax12 in https://github.com/academic/awesome-datascience/pull/107
    • Add Data Fallacies poster to the infographic section by @BRMatt in https://github.com/academic/awesome-datascience/pull/109
    • Typo fixes in readme by @ferhatelmas in https://github.com/academic/awesome-datascience/pull/111
    • Add Optimus into Toolboxes section by @FavioVazquez in https://github.com/academic/awesome-datascience/pull/110
    • Fixed link to "Sweden, Statistics" by @gostaj in https://github.com/academic/awesome-datascience/pull/112
    • Add Syracuse MS in Applied Data Science to COLLEGES by @justinclarkhome in https://github.com/academic/awesome-datascience/pull/115
    • Added curated data science resources by @qualityjacks in https://github.com/academic/awesome-datascience/pull/116
    • Add tutorial section by @vivekimsit in https://github.com/academic/awesome-datascience/pull/117
    • General Data Science Channel on YouTube: Introduction, NLP, and Practice by @tomer-ben-david in https://github.com/academic/awesome-datascience/pull/118
    • add college & fix broken link by @cmasch in https://github.com/academic/awesome-datascience/pull/120
    • Add MLonCode datasets and list by @vmarkovtsev in https://github.com/academic/awesome-datascience/pull/121
    • Fix broken link to Adversarial Learning by @Teoretic6 in https://github.com/academic/awesome-datascience/pull/122
    • Removed duplicate and suspended twitter accounts by @lethalbrains in https://github.com/academic/awesome-datascience/pull/123
    • Add Albumentations into Toolboxes section by @creafz in https://github.com/academic/awesome-datascience/pull/124
    • Added "Microsoft Research Open Data" datasets link by @Teoretic6 in https://github.com/academic/awesome-datascience/pull/125
    • Added Open Government Datasets Platform India link by @knightcube in https://github.com/academic/awesome-datascience/pull/126
    • Adding a book by @MaaniBeigy in https://github.com/academic/awesome-datascience/pull/127
    • Add DVC to the list of tools by @shcheklein in https://github.com/academic/awesome-datascience/pull/128
    • add collage by @andreaschandra in https://github.com/academic/awesome-datascience/pull/129
    • add visualization tools by @andreaschandra in https://github.com/academic/awesome-datascience/pull/130
    • remove wolframalpha from visualization by @andreaschandra in https://github.com/academic/awesome-datascience/pull/132
    • adding to your book list by @raer6 in https://github.com/academic/awesome-datascience/pull/133
    • added three tools to the "Toolboxes - Environment" by @kamil-kaczmarek in https://github.com/academic/awesome-datascience/pull/134
    • Correcting link to DataHack Platform by @faizankshaikh in https://github.com/academic/awesome-datascience/pull/135
    • Added two specialised ML awesome aggregators. by @benedekrozemberczki in https://github.com/academic/awesome-datascience/pull/136
    • Added Chris Albon's website as a Data Science Resource by @ethanchewy in https://github.com/academic/awesome-datascience/pull/138
    • Added good Microsoft Data Science program by @Polovinkin in https://github.com/academic/awesome-datascience/pull/139
    • Added Data Science at Scale with Python and Dask by @roziunicorn in https://github.com/academic/awesome-datascience/pull/137
    • Add Lambdo to Toolboxes by @asavinov in https://github.com/academic/awesome-datascience/pull/141
    • Added Math for Programmers by @roziunicorn in https://github.com/academic/awesome-datascience/pull/142
    • Added Feast (Feature Store) by @woop in https://github.com/academic/awesome-datascience/pull/143
    • Update Toolboxes - Environment section by @mmourafiq in https://github.com/academic/awesome-datascience/pull/144
    • Add Google Dataset Search (beta) by @Teoretic6 in https://github.com/academic/awesome-datascience/pull/145
    • Addition of popular Telegram channels by @Hiyorimi in https://github.com/academic/awesome-datascience/pull/147
    • Add PySpark Cheatsheet by @kevinschaich in https://github.com/academic/awesome-datascience/pull/146
    • Add newsletter section with AI Digest by @jakubgarfield in https://github.com/academic/awesome-datascience/pull/148
    • Gradient boosting, decision trees, fraud detection by @benedekrozemberczki in https://github.com/academic/awesome-datascience/pull/149
    • Add computer vision models list. by @gmalivenko in https://github.com/academic/awesome-datascience/pull/150
    • Added TensorWatch by @sytelus in https://github.com/academic/awesome-datascience/pull/151
    • Added MMA degree from Queen's by @stepthom in https://github.com/academic/awesome-datascience/pull/152
    • Fixed broken link and broken duplicate links. by @benedekrozemberczki in https://github.com/academic/awesome-datascience/pull/153
    • Update README.md by @harshbg in https://github.com/academic/awesome-datascience/pull/154
    • chore(readme): update colleges with Illinois Tech program by @vlandeiro in https://github.com/academic/awesome-datascience/pull/155
    • Added R in Action, Third Edition to Books by @von-latinski in https://github.com/academic/awesome-datascience/pull/156
    • Added "How to Become a Data Scientist" by @jaytaylopub in https://github.com/academic/awesome-datascience/pull/157
    • Add ML Workspace to Toolboxes by @LukasMasuch in https://github.com/academic/awesome-datascience/pull/158
    • Added Machine Learning, Data Science and Deep Learning with Python by @von-latinski in https://github.com/academic/awesome-datascience/pull/159
    • Fix Venn to Euler by @stereobooster in https://github.com/academic/awesome-datascience/pull/160
    • Add MOOC by @rasulkireev in https://github.com/academic/awesome-datascience/pull/162
    • Add MOOC by @rasulkireev in https://github.com/academic/awesome-datascience/pull/161
    • Added Data Science Bookcamp to Books by @von-latinski in https://github.com/academic/awesome-datascience/pull/163
    • add section free course by @lucassmacedo in https://github.com/academic/awesome-datascience/pull/164
    • Add subscribe to new links badge by @scopsy in https://github.com/academic/awesome-datascience/pull/166
    • Statistics and ML glossary by @tiagowutzke in https://github.com/academic/awesome-datascience/pull/165
    • Added links to LightTag and Annotation guide by @talolard in https://github.com/academic/awesome-datascience/pull/167
    • new books and modified (The Datascience Handbook) book name by @mostafatouny in https://github.com/academic/awesome-datascience/pull/168
    • Added Exploring Data with R to Books by @ipcenas in https://github.com/academic/awesome-datascience/pull/169
    • added Essential Natural Language Processing by @ipcenas in https://github.com/academic/awesome-datascience/pull/170
    • added Mining Massive Datasets book by @vojtech-filipec in https://github.com/academic/awesome-datascience/pull/171
    • Update README.md by @andrewnc in https://github.com/academic/awesome-datascience/pull/172
    • Add book, fighting churn with data by @carl24k in https://github.com/academic/awesome-datascience/pull/173
    • Update README.md by @HyamsG in https://github.com/academic/awesome-datascience/pull/174
      • Added Awesome Monte Carlo Tree Search by @benedekrozemberczki in https://github.com/academic/awesome-datascience/pull/175
    • Added Karate Club an unsupervised machine learning extension library by @benedekrozemberczki in https://github.com/academic/awesome-datascience/pull/176
    • Adds Your Guide to Latent Dirichlet Allocation by @lettier in https://github.com/academic/awesome-datascience/pull/177
    • Add open-source Hopsworks platform by @jimdowling in https://github.com/academic/awesome-datascience/pull/178
    • Add Pandas in Action to book list by @paskhaver in https://github.com/academic/awesome-datascience/pull/179
    • Update README.md by @ZoranPandovski in https://github.com/academic/awesome-datascience/pull/180
    • Add Lightwood by @ZoranPandovski in https://github.com/academic/awesome-datascience/pull/181
    • add AWS Data Wrangler in toolboxes by @igorborgest in https://github.com/academic/awesome-datascience/pull/182
    • Fix Feast organization by @woop in https://github.com/academic/awesome-datascience/pull/183
    • Added some mooc's by @deadshotsb in https://github.com/academic/awesome-datascience/pull/184
    • Added Little Ball of Fur. by @benedekrozemberczki in https://github.com/academic/awesome-datascience/pull/185
    • Adds Classpert Data Science Page by @wyugue in https://github.com/academic/awesome-datascience/pull/187
    • Added Enron Email Dataset Link by @ruppysuppy in https://github.com/academic/awesome-datascience/pull/189
    • PyTorch Geometric Temporal by @benedekrozemberczki in https://github.com/academic/awesome-datascience/pull/194
    • Typo by @acharles7 in https://github.com/academic/awesome-datascience/pull/192
    • Add CML to Toolboxes- environment by @elleobrien in https://github.com/academic/awesome-datascience/pull/193
    • Update README.md by @0xpranjal in https://github.com/academic/awesome-datascience/pull/196
    • Addition of Evolutionary Algorithms #195 by @SourangshuGhosh in https://github.com/academic/awesome-datascience/pull/197
    • add Awesome Game Datasets by @leomaurodesenv in https://github.com/academic/awesome-datascience/pull/200
    • Adding info on Machine Learning from Scratch by @dafriedman97 in https://github.com/academic/awesome-datascience/pull/202
    • Update README.md by @deadshotsb in https://github.com/academic/awesome-datascience/pull/203
    • updated visualisation tools by @Hemshree in https://github.com/academic/awesome-datascience/pull/204
    • Added a new book: Become a Leader in DS by @jikechong in https://github.com/academic/awesome-datascience/pull/205
    • Create CODE_OF_CONDUCT.md by @hmert in https://github.com/academic/awesome-datascience/pull/206
    • Fixing some links by @spekulatius in https://github.com/academic/awesome-datascience/pull/208
    • [Exploring Data with R](https://www.manning.com/books/exploring-data-… by @fakturk in https://github.com/academic/awesome-datascience/pull/211
    • Update README.md by @gokulmanohar in https://github.com/academic/awesome-datascience/pull/212
    • Updated README.md by @amanraj20 in https://github.com/academic/awesome-datascience/pull/214
    • Update README.md by @bjungbogati in https://github.com/academic/awesome-datascience/pull/213
    • Update README.md by @Numericmind in https://github.com/academic/awesome-datascience/pull/215
    • Added AI Expert Roadmap by @JStumpp in https://github.com/academic/awesome-datascience/pull/216
    • Updated broken link for ggplot2 by @RaInta in https://github.com/academic/awesome-datascience/pull/220
    • Fix typo by @sonicdoe in https://github.com/academic/awesome-datascience/pull/219
    • Linear Algebra courses by G. Strang by @fakturk in https://github.com/academic/awesome-datascience/pull/218
    • Convex Optimization book and course added by @fakturk in https://github.com/academic/awesome-datascience/pull/217
    • WIP: V2 by @hmert in https://github.com/academic/awesome-datascience/pull/209
    • Added link to Dask by @RaInta in https://github.com/academic/awesome-datascience/pull/222
    • Added navigation link 'top' to major sections for easier navigation. by @aoot in https://github.com/academic/awesome-datascience/pull/223
    • added Build a Career in Data Science by @ipcenas in https://github.com/academic/awesome-datascience/pull/224
    • added Data Analysis with Python and PySpark by @ipcenas in https://github.com/academic/awesome-datascience/pull/225
    • Added tutorial and blogger link by @jinglescode in https://github.com/academic/awesome-datascience/pull/226
    • Added a free course by @cloudytechi in https://github.com/academic/awesome-datascience/pull/227
    • Add Data Mechanics to toolbox by @jystephan in https://github.com/academic/awesome-datascience/pull/229
    • Update README.md by @alexeygrigorev in https://github.com/academic/awesome-datascience/pull/230
    • Added Shapley by @benedekrozemberczki in https://github.com/academic/awesome-datascience/pull/231
    • Update README.md by @andrewnc in https://github.com/academic/awesome-datascience/pull/234
    • Add dagshub by @martintali in https://github.com/academic/awesome-datascience/pull/233
    • ibm asset dataset by @Dinesh101041 in https://github.com/academic/awesome-datascience/pull/235
    • Add entry in Youtube section by @jankrepl in https://github.com/academic/awesome-datascience/pull/236
    • Update README.md by @abhiphull in https://github.com/academic/awesome-datascience/pull/238
    • Add new podcasts by @traveldwindling in https://github.com/academic/awesome-datascience/pull/239
    • Update README.md by @manujosephv in https://github.com/academic/awesome-datascience/pull/241
    • Update README.md by @microprediction in https://github.com/academic/awesome-datascience/pull/242
    • Update Podcasts section by @traveldwindling in https://github.com/academic/awesome-datascience/pull/243
    • doc: fix typo mistake in README by @longqua69 in https://github.com/academic/awesome-datascience/pull/245
    • doc: add book by @longqua69 in https://github.com/academic/awesome-datascience/pull/247
    • Add Deepnote to toolboxes by @robertlacok in https://github.com/academic/awesome-datascience/pull/248
    • Adding an intensive program in data science by @mariabardon in https://github.com/academic/awesome-datascience/pull/249
    • Add Gradient Dissent and DataFramed podcasts by @traveldwindling in https://github.com/academic/awesome-datascience/pull/250
    • Updated Visualization Tools list by @sarthakkmishraa in https://github.com/academic/awesome-datascience/pull/252
    • add a book by @tuulos in https://github.com/academic/awesome-datascience/pull/253
    • Adding UBIAI Text Annotation Tool by @walidamamou in https://github.com/academic/awesome-datascience/pull/254
    • Added to COLLEGES U Mich MADS by @Apropos-Brandon in https://github.com/academic/awesome-datascience/pull/255
    • Added Awesome Explainable Graph Reasoning by @benedekrozemberczki in https://github.com/academic/awesome-datascience/pull/256
    • Adding new blogger by @ltetrel in https://github.com/academic/awesome-datascience/pull/257
    • Added new interview site link by @sukanya-pai in https://github.com/academic/awesome-datascience/pull/259
    • Added Data Analysis with Python and PySpark by @stjepanjurekovic in https://github.com/academic/awesome-datascience/pull/261
    • Add Valohai, MLOps ebook and MLOps community by @skogstrom in https://github.com/academic/awesome-datascience/pull/260
    • Change Neptune.ml to Neptune.ai by @PawNep in https://github.com/academic/awesome-datascience/pull/262
    • Added O'Reilly Streaming Systems book by @atg-abhishek in https://github.com/academic/awesome-datascience/pull/264
    • Update README.md by @AnirbanMukherjeeXD in https://github.com/academic/awesome-datascience/pull/263
    • Added O'Reilly Data Science at the Command Line book by @atg-abhishek in https://github.com/academic/awesome-datascience/pull/265
    • Added Chip Huyen's blog which covers ML Engineering by @atg-abhishek in https://github.com/academic/awesome-datascience/pull/266
    • Added the technical YouTube channel ML Street Talk by @atg-abhishek in https://github.com/academic/awesome-datascience/pull/267
    • Remove unused links for some twitter accounts by @Iqrar99 in https://github.com/academic/awesome-datascience/pull/268
    • Add 2 Free Machine Learning PDFs by @gusttavodev in https://github.com/academic/awesome-datascience/pull/269
    • Add youtube channel for Neural networks by @krithikha2001 in https://github.com/academic/awesome-datascience/pull/270
    • Add Book by @MuhamadAzizi in https://github.com/academic/awesome-datascience/pull/271
    • Added description for NLTK by @RaInta in https://github.com/academic/awesome-datascience/pull/272
    • Added MOOC Recomendation Systems by @ayoub-berdeddouch in https://github.com/academic/awesome-datascience/pull/274
    • Added MOOC NLP SPECIALIZATION by @ayoub-berdeddouch in https://github.com/academic/awesome-datascience/pull/275
    • added MOOC MLOPs by @ayoub-berdeddouch in https://github.com/academic/awesome-datascience/pull/276
    • added Mooc DS: Statistics & ML by @ayoub-berdeddouch in https://github.com/academic/awesome-datascience/pull/277
    • Added Comics, and a book by @AnirbanMukherjeeXD in https://github.com/academic/awesome-datascience/pull/278
    • Added regression, a Friendly Guide by @stjepanjurekovic in https://github.com/academic/awesome-datascience/pull/279
    • added self organized map by @jaira-encio in https://github.com/academic/awesome-datascience/pull/280
    • added python for data science beginners guide by @jaira-encio in https://github.com/academic/awesome-datascience/pull/281
    • Added references to two books: statistical learning with R, PyTorch by @RaInta in https://github.com/academic/awesome-datascience/pull/282
    • Typo fix README.md by @fishmandev in https://github.com/academic/awesome-datascience/pull/284
    • Add AI Today and Data Engineering Show podcasts by @traveldwindling in https://github.com/academic/awesome-datascience/pull/285
    • Added Books -- Deep Learning Cookbook and Neural Networks and Deep Le… by @RaInta in https://github.com/academic/awesome-datascience/pull/283
    • Added hmmlearn, AI at Home and Intro to ML with Python by @RaInta in https://github.com/academic/awesome-datascience/pull/286
    • Added free book: Foundations of Computational Agents by @ManviGoel26 in https://github.com/academic/awesome-datascience/pull/287
    • Added Vizzu by @simzer in https://github.com/academic/awesome-datascience/pull/288
    • Adding a newsletter by @waniniraj in https://github.com/academic/awesome-datascience/pull/289
    • Update README.md by @99ansh in https://github.com/academic/awesome-datascience/pull/291
    • add o'reilly data show podcast by @afrizalhan in https://github.com/academic/awesome-datascience/pull/292
    • Update README.md by @aryan-mehta in https://github.com/academic/awesome-datascience/pull/293
    • AWS Rekognition by @fakturk in https://github.com/academic/awesome-datascience/pull/294
    • Update README.md by @fakturk in https://github.com/academic/awesome-datascience/pull/295
    • Amazon Lookout for Vision added to toolboxes by @fakturk in https://github.com/academic/awesome-datascience/pull/296
    • Amazon CodeGuru added to toolboxes by @fakturk in https://github.com/academic/awesome-datascience/pull/297
    • Add Let's Data Podcast on Podcasts pt_BR by @lucassmacedo in https://github.com/academic/awesome-datascience/pull/299
    • added free book: The Quest for Artificial Intelligence by @ManviGoel26 in https://github.com/academic/awesome-datascience/pull/300
    • Added Designing Cloud Data Platfroms by @stjepanjurekovic in https://github.com/academic/awesome-datascience/pull/301
    • Add Data36 Youtube Channel by @netwarex in https://github.com/academic/awesome-datascience/pull/302
    • Added Vaex to Toolboxes by @JadenLeake333 in https://github.com/academic/awesome-datascience/pull/304
    • Fixed Typo, change 'Pandas GU' to 'Pandas GUI' by @JadenLeake333 in https://github.com/academic/awesome-datascience/pull/305
    • Awesome Polypharmacy - DDI - Synergy Survey by @benedekrozemberczki in https://github.com/academic/awesome-datascience/pull/307
    • Added a channel and 6 new videos by @stjepanjurekovic in https://github.com/academic/awesome-datascience/pull/308
    • Remove https://www.datasciencegame.com from Competitions by @theodorosploumis in https://github.com/academic/awesome-datascience/pull/309
    • Added Graph Algorithms for Data Science by @stjepanjurekovic in https://github.com/academic/awesome-datascience/pull/310
    • Add Eindhoven University to the list of colleges by @mickeybeurskens in https://github.com/academic/awesome-datascience/pull/311
    • Add The Radical AI Podcast by @traveldwindling in https://github.com/academic/awesome-datascience/pull/312
    • Add ipychart by @nicohlr in https://github.com/academic/awesome-datascience/pull/313
    • Added RexMex and ChemicalX by @benedekrozemberczki in https://github.com/academic/awesome-datascience/pull/314
    • Add Netron by @lutzroeder in https://github.com/academic/awesome-datascience/pull/315
    • Add deepchecks to general purpose machine learning by @ItayGabbay in https://github.com/academic/awesome-datascience/pull/316
    • Added a video by Matthew Rudd by @stjepanjurekovic in https://github.com/academic/awesome-datascience/pull/317
    • Update redirected links and "Infographic" table error by @z00rat in https://github.com/academic/awesome-datascience/pull/320
    • Added Data Mesh in Action by @stjepanjurekovic in https://github.com/academic/awesome-datascience/pull/321
    • Add SocialGrep by @pavel-lexyr in https://github.com/academic/awesome-datascience/pull/322
    • Add Chaos Genius to Awesome Datascience by @suranah in https://github.com/academic/awesome-datascience/pull/323
    • Added Nimblebox to the Repository by @PranshulDobriyal in https://github.com/academic/awesome-datascience/pull/324
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    • @DeleMike made their first contribution in https://github.com/academic/awesome-datascience/pull/341
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    • @Sean12697 made their first contribution in https://github.com/academic/awesome-datascience/pull/342
    • @Juane99 made their first contribution in https://github.com/academic/awesome-datascience/pull/349
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    • @AcylSilane made their first contribution in https://github.com/academic/awesome-datascience/pull/354

    Full Changelog: https://github.com/academic/awesome-datascience/commits/2022.10.14

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