├── img ├── data-science.webp └── datascience-intro.png ├── .gitignore ├── LICENSE ├── code-of-conduct.md └── README.md /img/data-science.webp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/exajobs/datascience-collection/HEAD/img/data-science.webp -------------------------------------------------------------------------------- /img/datascience-intro.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/exajobs/datascience-collection/HEAD/img/datascience-intro.png -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Prerequisites 2 | *.d 3 | 4 | # Compiled Object files 5 | *.slo 6 | *.lo 7 | *.o 8 | *.obj 9 | 10 | # Precompiled Headers 11 | *.gch 12 | *.pch 13 | 14 | # Compiled Dynamic libraries 15 | *.so 16 | *.dylib 17 | *.dll 18 | 19 | # Fortran module files 20 | *.mod 21 | *.smod 22 | 23 | # Compiled Static libraries 24 | *.lai 25 | *.la 26 | *.a 27 | *.lib 28 | 29 | # Executables 30 | *.exe 31 | *.out 32 | *.app 33 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Exajobs, Inc 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /code-of-conduct.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as 6 | contributors and maintainers pledge to making participation in our project and 7 | our community a harassment-free experience for everyone, regardless of age, body 8 | size, disability, ethnicity, sex characteristics, gender identity and expression, 9 | level of experience, education, socio-economic status, nationality, personal 10 | appearance, race, religion, or sexual identity and orientation. 11 | 12 | ## Our Standards 13 | 14 | Examples of behavior that contributes to creating a positive environment 15 | include: 16 | 17 | * Using welcoming and inclusive language 18 | * Being respectful of differing viewpoints and experiences 19 | * Gracefully accepting constructive criticism 20 | * Focusing on what is best for the community 21 | * Showing empathy towards other community members 22 | 23 | Examples of unacceptable behavior by participants include: 24 | 25 | * The use of sexualized language or imagery and unwelcome sexual attention or 26 | advances 27 | * Trolling, insulting/derogatory comments, and personal or political attacks 28 | * Public or private harassment 29 | * Publishing others' private information, such as a physical or electronic 30 | address, without explicit permission 31 | * Other conduct which could reasonably be considered inappropriate in a 32 | professional setting 33 | 34 | ## Our Responsibilities 35 | 36 | Project maintainers are responsible for clarifying the standards of acceptable 37 | behavior and are expected to take appropriate and fair corrective action in 38 | response to any instances of unacceptable behavior. 39 | 40 | Project maintainers have the right and responsibility to remove, edit, or 41 | reject comments, commits, code, wiki edits, issues, and other contributions 42 | that are not aligned to this Code of Conduct, or to ban temporarily or 43 | permanently any contributor for other behaviors that they deem inappropriate, 44 | threatening, offensive, or harmful. 45 | 46 | ## Scope 47 | 48 | This Code of Conduct applies both within project spaces and in public spaces 49 | when an individual is representing the project or its community. Examples of 50 | representing a project or community include using an official project e-mail 51 | address, posting via an official social media account, or acting as an appointed 52 | representative at an online or offline event. Representation of a project may be 53 | further defined and clarified by project maintainers. 54 | 55 | ## Enforcement 56 | 57 | Instances of abusive, harassing, or otherwise unacceptable behavior may be 58 | reported by contacting the project team at hi@academic.io. All 59 | complaints will be reviewed and investigated and will result in a response that 60 | is deemed necessary and appropriate to the circumstances. The project team is 61 | obligated to maintain confidentiality with regard to the reporter of an incident. 62 | Further details of specific enforcement policies may be posted separately. 63 | 64 | Project maintainers who do not follow or enforce the Code of Conduct in good 65 | faith may face temporary or permanent repercussions as determined by other 66 | members of the project's leadership. 67 | 68 | ## Attribution 69 | 70 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, 71 | available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html 72 | 73 | [homepage]: https://www.contributor-covenant.org 74 | 75 | For answers to common questions about this code of conduct, see 76 | https://www.contributor-covenant.org/faq 77 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Data Science Collection 2 | > A collection of awesome software, libraries, documents, books, resources and cool stuff about Data Science. 3 | > Thanks to our daily readers and contributoprs. The goal is to build a categorized community-driven collection of very well-known resources. Sharing, suggestions and contributions are always welcome! 4 | 5 | - Data science uses complex machine learning algorithms to build predictive models. 6 | 7 | ![datascience](https://github.com/exajobs/datascience-collection/blob/main/img/datascience-intro.png) 8 | 9 |
10 | 11 | ## What is Data Science? 12 | 13 | Data science is an essential part of many industries today, given the massive amounts of data that are produced, and is one of the most debated topics in IT circles. 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](https://www.quora.com/Data-Science/What-is-data-science) you can find the biggest question for **Data Science** and hundreds of answers from experts. 14 | 15 | 16 | | Link | Preview | 17 | | --- | --- | 18 | | [What is Data Science @ O'reilly](https://www.oreilly.com/ideas/what-is-data-science) | _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?”_ | 19 | | [What is Data Science @ Quora](https://www.quora.com/Data-Science/What-is-data-science) | 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. | 20 | | [The sexiest job of 21st century](https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-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._ | 21 | | [Wikipedia](https://en.wikipedia.org/wiki/Data_science) | _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._ | 22 | | [How to Become a Data Scientist](https://www.mastersindatascience.org/careers/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._ | 23 | | [a very short history of #datascience](http://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/) | _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._ | 24 | 25 | 26 | ## Learn Data Science 27 | 28 | Our favorite programming language is _Python_ nowadays for #DataScience. Python's - [Pandas](http://pandas.pydata.org/) library has full functionalities for collecting and analyzing data. We use [Anaconda](https://www.anaconda.com) to play with data and to create applications. 29 | 30 | - [Algorithms](#algorithms) 31 | - [Colleges](#colleges) 32 | - [MOOC's](#moocs) 33 | - [Podcasts](#podcasts) 34 | - [Books](#books) 35 | - [YouTube Videos & Channels](#youtube-videos--channels) 36 | - [Toolboxes - Environment](#toolboxes---environment) 37 | - [Journals, Publications and Magazines](#journals-publications-and-magazines) 38 | - [Presentations](#presentations) 39 | - [Tutorials](#tutorials) 40 | 41 | ## Algorithms 42 | [top](#data-science-collection) 43 | 44 | These are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it. 45 | 46 | ### Supervised Learning 47 | 48 | - Regression 49 | - Linear Regression 50 | - Ordinary Least Squares 51 | - Logistic Regression 52 | - Stepwise Regression 53 | - Multivariate Adaptive Regression Splines 54 | - Locally Estimated Scatterplot Smoothing 55 | - Classification 56 | - k-nearest neighbor 57 | - Support Vector Machines 58 | - Decision Trees 59 | - ID3 algorithm 60 | - C4.5 algorithm 61 | - Ensemble Learning 62 | - Boosting 63 | - Bagging 64 | - Random Forest 65 | - AdaBoost 66 | 67 | ### Unsupervised Learning 68 | - Clustering 69 | - Hierchical clustering 70 | - k-means 71 | - Fuzzy clustering 72 | - Mixture models 73 | - Dimension Reduction 74 | - Principal Component Analysis (PCA) 75 | - t-SNE 76 | - Neural Networks 77 | - Self-organizing map 78 | - Adaptive resonance theory 79 | - Hidden Markov Models (HMM) 80 | 81 | ### Semi-Supervised Learning 82 | 83 | - S3VM 84 | - Clustering 85 | - Generative models 86 | - Low-density separation 87 | - Laplacian regularization 88 | - Heuristic approaches 89 | 90 | ### Reinforcement Learning 91 | 92 | - Q Learning 93 | - SARSA (State-Action-Reward-State-Action) algorithm 94 | - Temporal difference learning 95 | 96 | ### Data Mining Algorithms 97 | 98 | - C4.5 99 | - k-Means 100 | - SVM 101 | - Apriori 102 | - EM 103 | - PageRank 104 | - AdaBoost 105 | - kNN 106 | - Naive Bayes 107 | - CART 108 | 109 | ### Deep Learning architectures 110 | 111 | - Multilayer Perceptron 112 | - Convolutional Neural Network (CNN) 113 | - Recurrent Neural Network (RNN) 114 | - Boltzmann Machines 115 | - Autoencoder 116 | - Generative Adversarial Network (GAN) 117 | - Self-Organized Maps 118 | 119 | ## COLLEGES 120 | [top](#data-science-collectione) 121 | 122 | - [A list of colleges and universities offering degrees in data science.](https://github.com/ryanswanstrom/awesome-datascience-colleges) 123 | - [Data Science Degree @ Berkeley](https://datascience.berkeley.edu/) 124 | - [Data Science Degree @ UVA](https://dsi.virginia.edu/) 125 | - [Data Science Degree @ Wisconsin](http://datasciencedegree.wisconsin.edu/) 126 | - [MS in Computer Information Systems @ Boston University](http://cisonline.bu.edu/) 127 | - [MS in Business Analytics @ ASU Online](http://asuonline.asu.edu/online-degree-programs/graduate/master-science-business-analytics/) 128 | - [Data Science Engineer @ BTH](https://www.bth.se/nyheter/bth-startar-sveriges-forsta-civilingenjorsprogram-inom-data-science/) 129 | - [MS in Applied Data Science @ Syracuse](https://ischool.syr.edu/academics/graduate/masters-degrees/ms-in-applied-data-science/) 130 | - [M.S. Management & Data Science @ Leuphana](https://www.leuphana.de/en/graduate-school/master/course-offerings/management-data-science.html) 131 | - [Master of Data Science @ Melbourne University](https://science-courses.unimelb.edu.au/study/degrees/master-of-data-science/overview#overview) 132 | - [Msc in Data Science @ The University of Edinburgh](https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=902) 133 | - [Master of Management Analytics @ Queen's University](https://smith.queensu.ca/grad_studies/mma/index.php) 134 | - [Master of Data Science @ Illinois Institute of Technology](https://science.iit.edu/programs/graduate/master-data-science) 135 | - [Master of Applied Data Science @ The University of Michigan](https://www.si.umich.edu/programs/master-applied-data-science-online) 136 | - [Master Data Science and Artificial Intelligence @ Eindhoven University of Technology](https://www.tue.nl/en/education/graduate-school/master-data-science-and-artificial-intelligence/) 137 | 138 | ## Intensive Programs 139 | - [S2DS](https://s2ds.org/) 140 | 141 | ## MOOC's 142 | [top](#data-science-collection) 143 | 144 | - [Coursera Introduction to Data Science](https://www.coursera.org/specializations/data-science) 145 | - [Data Science - 9 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/jhu-data-science) 146 | - [Data Mining - 5 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specialization/datamining) 147 | - [Machine Learning – 5 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/machine-learning) 148 | - [CS 109 Data Science](http://cs109.github.io/2015/) 149 | - [OpenIntro](https://www.openintro.org/) 150 | - [CS 171 Visualization](http://www.cs171.org/#!index.md) 151 | - [Process Mining: Data science in Action](https://www.coursera.org/learn/process-mining) 152 | - [Oxford Deep Learning](http://www.cs.ox.ac.uk/projects/DeepLearn/) 153 | - [Oxford Deep Learning - video](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) 154 | - [Oxford Machine Learning](http://www.cs.ox.ac.uk/activities/machinelearning/) 155 | - [UBC Machine Learning - video](http://www.cs.ubc.ca/~nando/540-2013/lectures.html) 156 | - [Data Science Specialization](https://github.com/DataScienceSpecialization/courses) 157 | - [Coursera Big Data Specialization](https://www.coursera.org/specializations/big-data) 158 | - [Statistical Thinking for Data Science and Analytics by Edx](https://www.edx.org/course/statistical-thinking-for-data-science-and-analytic) 159 | - [Cognitive Class AI by IBM](https://cognitiveclass.ai/) 160 | - [Udacity - Deep Learning](https://www.udacity.com/course/deep-learning--ud730) 161 | - [Keras in Motion](https://www.manning.com/livevideo/keras-in-motion) 162 | - [Microsoft Professional Program for Data Science](https://academy.microsoft.com/en-us/professional-program/tracks/data-science/) 163 | - [COMP3222/COMP6246 - Machine Learning Technologies](https://tdgunes.com/COMP6246-2019Fall/) 164 | - [CS 231 - Convolutional Neural Networks for Visual Recognition](http://cs231n.github.io/) 165 | - [Coursera Tensorflow in practice](https://www.coursera.org/specializations/tensorflow-in-practice?) 166 | - [Coursera Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning) 167 | - [365 Data Science Course](https://365datascience.com/) 168 | - [Coursera Natural Language Processing Specialization](https://www.coursera.org/specializations/natural-language-processing) 169 | - [Coursera GAN Specialization](https://www.coursera.org/specializations/generative-adversarial-networks-gans) 170 | - [Codecademy's Data Science](https://www.codecademy.com/learn/paths/data-science) 171 | - [Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/) - Linear Algebra course by Gilbert Strang 172 | - [A 2020 Vision of Linear Algebra (G. Strang)](https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-linear-algebra-spring-2020/) 173 | - [Python for Data Science Foundation Course](https://intellipaat.com/academy/course/python-for-data-science-free-training/) 174 | - [Data Science: Statistics & Machine Learning](https://www.coursera.org/specializations/data-science-statistics-machine-learning) 175 | - [Machine Learning Engineering for Production (MLOps)](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops) 176 | - [NLP Specialization Coursera](https://www.coursera.org/specializations/natural-language-processing) 177 | - [Recommender Systems Specialization from University of Minnesota](https://www.coursera.org/specializations/recommender-systems) is an intermediate/advanced level specialization focused on Recommender System on Coursera Plaform. 178 | 179 | 180 | ## Tutorials 181 | [top](#data-science-collection) 182 | 183 | - [1000 Data Science Projects](https://cloud.blobcity.com/#/ps/explore) you can run on browser with ipyton. 184 | - [#tidytuesday](https://github.com/rfordatascience/tidytuesday) A weekly data project aimed at the R ecosystem. 185 | - [Data science your way](https://github.com/jadianes/data-science-your-way) 186 | - [PySpark Cheatsheet](https://github.com/kevinschaich/pyspark-cheatsheet) 187 | - [Machine Learning, Data Science and Deep Learning with Python ](https://www.manning.com/livevideo/machine-learning-data-science-and-deep-learning-with-python) 188 | - [How To Label Data](https://www.lighttag.io/how-to-label-data/) 189 | - [Your Guide to Latent Dirichlet Allocation](https://medium.com/@lettier/how-does-lda-work-ill-explain-using-emoji-108abf40fa7d) 190 | - [Over 1000 Data Science Online Courses at Classpert Online Search Engine](https://classpert.com/data-science) 191 | - [Tutorials of source code from the book Genetic Algorithms with Python by Clinton Sheppard](https://github.com/handcraftsman/GeneticAlgorithmsWithPython) 192 | - [Tutorials to get started on signal processings for machine learning](https://github.com/jinglescode/python-signal-processing) 193 | - [Realtime deployment](https://www.microprediction.com/python-1) Tutorial on Python time-series model deployment. 194 | - [Python for Data Science: A Beginner’s Guide](https://learntocodewith.me/posts/python-for-data-science/) 195 | 196 | ### Free Courses 197 | 198 | - [Data Scientist with R](https://www.datacamp.com/tracks/data-scientist-with-r) 199 | - [Data Scientist with Python](https://www.datacamp.com/tracks/data-scientist-with-python) 200 | - [Genetic Algorithms OCW Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-1-introduction-and-scope/) 201 | - [AI Expert Roadmap](https://github.com/AMAI-GmbH/AI-Expert-Roadmap) - Roadmap to becoming an Artificial Intelligence Expert 202 | - [Convex Optimization](https://www.edx.org/course/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...) 203 | 204 | 205 | ## Toolboxes - Environment 206 | [top](#data-science-collection) 207 | 208 | | Link | Description | 209 | | --- | --- | 210 | | [The Data Science Lifecycle Process](https://github.com/dslp/dslp) | 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 | 211 | | [Data Science Lifecycle Template Repo](https://github.com/dslp/dslp-repo-template) | Template repository for data science lifecycle project | 212 | | [RexMex](https://github.com/AstraZeneca/rexmex) | A general purpose recommender metrics library for fair evaluation. | 213 | | [ChemicalX](https://github.com/AstraZeneca/chemicalx) | A PyTorch based deep learning library for drug pair scoring. | 214 | | [PyTorch Geometric Temporal](https://github.com/benedekrozemberczki/pytorch_geometric_temporal) | Representation learning on dynamic graphs. | 215 | | [Little Ball of Fur](https://github.com/benedekrozemberczki/littleballoffur) | A graph sampling library for NetworkX with a Scikit-Learn like API. | 216 | | [Karate Club](https://github.com/benedekrozemberczki/karateclub) | An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API. | 217 | | [ML Workspace](https://github.com/ml-tooling/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) | 218 | | [Neptune.ai](https://neptune.ai) | Community-friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility. | 219 | | [steppy](https://github.com/neptune-ml/steppy) | Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces very simple interface that enables clean machine learning pipeline design. | 220 | | [steppy-toolkit](https://github.com/neptune-ml/steppy-toolkit) | Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective. | 221 | | [Datalab from Google](https://cloud.google.com/datalab/docs/) | easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively. | 222 | | [Hortonworks Sandbox](http://hortonworks.com/products/sandbox/) | is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials. | 223 | | [R](http://www.r-project.org/) | is a free software environment for statistical computing and graphics. | 224 | | [RStudio](https://www.rstudio.com) | IDE – powerful user interface for R. It’s free and open source, works on Windows, Mac, and Linux. | 225 | | [Python - Pandas - Anaconda](https://www.anaconda.com) | Completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing | 226 | | [Pandas GUI](https://github.com/adamerose/pandasgui) | Pandas GUI | 227 | | [Scikit-Learn](http://scikit-learn.org/stable/) | Machine Learning in Python | 228 | | [NumPy](http://www.numpy.org/) | 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. | 229 | | [Vaex](https://vaex.io/) | Vaex is a Python library that allows you to visualize large datasets and calculate statistics at high speeds. | 230 | | [SciPy](https://www.scipy.org/) | SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization. 231 | | [Data Science Toolbox](https://www.coursera.org/learn/data-scientists-tools) | Coursera Course | 232 | | [Data Science Toolbox](http://datasciencetoolbox.org/) | Blog | 233 | | [Wolfram Data Science Platform](http://www.wolfram.com/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. | 234 | | [Datadog](https://www.datadoghq.com/) | Solutions, code, and devops for high-scale data science. | 235 | | [Variance](http://variancecharts.com/) | Build powerful data visualizations for the web without writing JavaScript | 236 | | [Kite Development Kit](http://kitesdk.org/docs/current/index.html) | 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. | 237 | | [Domino Data Labs](http://www.dominodatalab.com) | Run, scale, share, and deploy your models — without any infrastructure or setup. | 238 | | [Apache Flink](http://flink.apache.org/) | A platform for efficient, distributed, general-purpose data processing. | 239 | | [Apache Hama](http://hama.apache.org/) | Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce. | 240 | | [Weka](http://www.cs.waikato.ac.nz/ml/weka/) | Weka is a collection of machine learning algorithms for data mining tasks. | 241 | | [Octave](https://www.gnu.org/software/octave/) | GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab) | 242 | | [Apache Spark](https://spark.apache.org/) | Lightning-fast cluster computing | 243 | | [Hydrosphere Mist](https://github.com/Hydrospheredata/mist) | a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services. | 244 | | [Data Mechanics](https://www.datamechanics.co) | A data science and engineering platform making Apache Spark more developer-friendly and cost-effective. | 245 | | [Caffe](http://caffe.berkeleyvision.org/) | Deep Learning Framework | 246 | | [Torch](http://torch.ch/) | A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT | 247 | | [Nervana's python based Deep Learning Framework](https://github.com/NervanaSystems/neon) | . | 248 | | [Skale](https://github.com/skale-me/skale-engine) | High performance distributed data processing in NodeJS | 249 | | [Aerosolve](http://airbnb.io/aerosolve/) | A machine learning package built for humans. | 250 | | [Intel framework](https://github.com/01org/idlf) | Intel® Deep Learning Framework | 251 | | [Datawrapper](https://www.datawrapper.de/) | An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at [github.com](https://github.com/datawrapper/datawrapper) | 252 | | [Tensor Flow](https://www.tensorflow.org/) | TensorFlow is an Open Source Software Library for Machine Intelligence | 253 | | [Natural Language Toolkit](http://www.nltk.org/) | An introductory yet powerful toolkit for natural language processing and classification | 254 | | [nlp-toolkit for node.js](https://www.npmjs.com/package/nlp-toolkit) | . | 255 | | [Julia](http://julialang.org) | high-level, high-performance dynamic programming language for technical computing | 256 | | [IJulia](https://github.com/JuliaLang/IJulia.jl) | a Julia-language backend combined with the Jupyter interactive environment | 257 | | [Apache Zeppelin](http://zeppelin.apache.org/) | Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more | 258 | | [Featuretools](https://github.com/featuretools/featuretools/) | An open source framework for automated feature engineering written in python | 259 | | [Optimus](https://github.com/ironmussa/Optimus) | Cleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend. | 260 | | [Albumentations](https://github.com/albu/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. | 261 | | [DVC](https://github.com/iterative/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. | 262 | | [Lambdo](https://github.com/asavinov/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. | 263 | | [Feast](https://github.com/feast-dev/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. | 264 | | [Polyaxon](https://github.com/polyaxon/polyaxon) | A platform for reproducible and scalable machine learning and deep learning. | 265 | | [LightTag](https://lighttag.io) | Text Annotation Tool for teams | 266 | | [UBIAI](https://ubiai.tools) | 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 | 267 | | [Trains](https://github.com/allegroai/trains) | Auto-Magical Experiment Manager, Version Control & DevOps for AI | 268 | | [Hopsworks](https://github.com/logicalclocks/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. | 269 | | [MindsDB](https://github.com/mindsdb/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. | 270 | | [Lightwood](https://github.com/mindsdb/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. | 271 | | [AWS Data Wrangler](https://github.com/awslabs/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). | 272 | | [Amazon Rekognition](https://aws.amazon.com/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.| 273 | | [Amazon Textract](https://aws.amazon.com/textract) | Automatically extract printed text, handwriting, and data from any document. | 274 | | [Amazon Lookout for Vision](https://aws.amazon.com/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.| 275 | | [Amazon CodeGuru](https://aws.amazon.com/codeguru) | Automate code reviews and optimize application performance with ML-powered recommendations.| 276 | | [CML](https://github.com/iterative/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. | 277 | | [Dask](https://dask.org/) | An open source Python library to painlessly transition your analytics code to distributed computing systems (Big Data) | 278 | | [Statsmodels](https://www.statsmodels.org/stable/index.html) | A Python-based inferential statistics, hypothesis testing and regression framework | 279 | | [Gensim](https://radimrehurek.com/gensim/) | An open-source library for topic modeling of natural language text | 280 | | [spaCy](https://spacy.io/) | A performant natural language processing toolkit | 281 | | [Grid Studio](https://github.com/ricklamers/gridstudio) | Grid studio is a web-based spreadsheet application with full integration of the Python programming language. | 282 | |[Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook)|Python Data Science Handbook: full text in Jupyter Notebooks| 283 | | [Shapley](https://github.com/benedekrozemberczki/shapley) | A data-driven framework to quantify the value of classifiers in a machine learning ensemble. | 284 | | [DAGsHub](https://dagshub.com) | A platform built on open source tools for data, model and pipeline management. | 285 | | [Deepnote](https://deepnote.com) | A new kind of data science notebook. Jupyter-compatible, with real-time collaboration and running in the cloud. | 286 | | [Valohai](https://valohai.com) | An MLOps platform that handles machine orchestration, automatic reproducibility and deployment. | 287 | | [PyMC3](https://docs.pymc.io/) | A Python Library for Probabalistic Programming (Bayesian Inference and Machine Learning) | 288 | | [PyStan](https://pypi.org/project/pystan/) | Python interface to Stan (Bayesian inference and modeling) | 289 | | [hmmlearn](https://pypi.org/project/hmmlearn/) | Unsupervised learning and inference of Hidden Markov Models | 290 | 291 | ## Machine Learning in General Purpose 292 | 293 | * [scikit-learn](http://scikit-learn.org/) 294 | * [scikit-multilearn](https://github.com/scikit-multilearn/scikit-multilearn) 295 | * [sklearn-expertsys](https://github.com/tmadl/sklearn-expertsys) 296 | * [scikit-feature](https://github.com/jundongl/scikit-feature) 297 | * [scikit-rebate](https://github.com/EpistasisLab/scikit-rebate) 298 | * [seqlearn](https://github.com/larsmans/seqlearn) 299 | * [sklearn-bayes](https://github.com/AmazaspShumik/sklearn-bayes) 300 | * [sklearn-crfsuite](https://github.com/TeamHG-Memex/sklearn-crfsuite) 301 | * [sklearn-deap](https://github.com/rsteca/sklearn-deap) 302 | * [sigopt_sklearn](https://github.com/sigopt/sigopt_sklearn) 303 | * [sklearn-evaluation](https://github.com/edublancas/sklearn-evaluation) 304 | * [scikit-image](https://github.com/scikit-image/scikit-image) 305 | * [scikit-opt](https://github.com/guofei9987/scikit-opt) 306 | * [scikit-posthocs](https://github.com/maximtrp/scikit-posthocs) 307 | * [pystruct](https://github.com/pystruct/pystruct) 308 | * [Shogun](http://www.shogun-toolbox.org/) 309 | * [xLearn](https://github.com/aksnzhy/xlearn) 310 | * [cuML](https://github.com/rapidsai/cuml) 311 | * [causalml](https://github.com/uber/causalml) 312 | * [mlpack](https://github.com/mlpack/mlpack) 313 | * [MLxtend](https://github.com/rasbt/mlxtend) 314 | * [modAL](https://github.com/cosmic-cortex/modAL) 315 | * [Sparkit-learn](https://github.com/lensacom/sparkit-learn) 316 | * [hyperlearn](https://github.com/danielhanchen/hyperlearn) 317 | * [dlib](https://github.com/davisking/dlib) 318 | * [RuleFit](https://github.com/christophM/rulefit) 319 | * [pyGAM](https://github.com/dswah/pyGAM) 320 | * [Deepchecks](https://github.com/deepchecks/deepchecks) 321 | 322 | 323 | ## Deep Learning 324 | 325 | ### pytorch 326 | * [PyTorch](https://github.com/pytorch/pytorch) 327 | * [torchvision](https://github.com/pytorch/vision) 328 | * [torchtext](https://github.com/pytorch/text) 329 | * [torchaudio](https://github.com/pytorch/audio) 330 | * [ignite](https://github.com/pytorch/ignite) 331 | * [PyTorchNet](https://github.com/pytorch/tnt) 332 | * [PyToune](https://github.com/GRAAL-Research/pytoune) 333 | * [skorch](https://github.com/dnouri/skorch) 334 | * [PyVarInf](https://github.com/ctallec/pyvarinf) 335 | * [pytorch_geometric](https://github.com/rusty1s/pytorch_geometric) 336 | * [GPyTorch](https://github.com/cornellius-gp/gpytorch) 337 | * [pyro](https://github.com/uber/pyro) 338 | * [Catalyst](https://github.com/catalyst-team/catalyst) 339 | * [pytorch_tabular](https://github.com/manujosephv/pytorch_tabular) 340 | ### tensorflow 341 | * [TensorFlow](https://github.com/tensorflow/tensorflow) 342 | * [TensorLayer](https://github.com/zsdonghao/tensorlayer) 343 | * [TFLearn](https://github.com/tflearn/tflearn) 344 | * [Sonnet](https://github.com/deepmind/sonnet) 345 | * [tensorpack](https://github.com/ppwwyyxx/tensorpack) 346 | * [TRFL](https://github.com/deepmind/trfl) 347 | * [Polyaxon](https://github.com/polyaxon/polyaxon) 348 | * [NeuPy](https://github.com/itdxer/neupy) 349 | * [tfdeploy](https://github.com/riga/tfdeploy) 350 | * [tensorflow-upstream](https://github.com/ROCmSoftwarePlatform/tensorflow-upstream) 351 | * [TensorFlow Fold](https://github.com/tensorflow/fold) 352 | * [tensorlm](https://github.com/batzner/tensorlm) 353 | * [TensorLight](https://github.com/bsautermeister/tensorlight) 354 | * [Mesh TensorFlow](https://github.com/tensorflow/mesh) 355 | * [Ludwig](https://github.com/uber/ludwig) 356 | * [TF-Agents](https://github.com/tensorflow/agents) 357 | * [TensorForce](https://github.com/reinforceio/tensorforce) 358 | 359 | ### keras 360 | 361 | * [Keras](https://keras.io) 362 | * [keras-contrib](https://github.com/keras-team/keras-contrib) 363 | * [Hyperas](https://github.com/maxpumperla/hyperas) 364 | * [Elephas](https://github.com/maxpumperla/elephas) 365 | * [Hera](https://github.com/keplr-io/hera) 366 | * [Spektral](https://github.com/danielegrattarola/spektral) 367 | * [qkeras](https://github.com/google/qkeras) 368 | * [keras-rl](https://github.com/keras-rl/keras-rl) 369 | * [Talos](https://github.com/autonomio/talos) 370 | 371 | 372 | ## Visualization Tools - Environments 373 | 374 | - [altair](https://altair-viz.github.io/) 375 | - [addepar](http://opensource.addepar.com/ember-charts/#/overview) 376 | - [amcharts](https://www.amcharts.com/) 377 | - [anychart](http://www.anychart.com/) 378 | - [bokeh](https://bokeh.org/) 379 | - [slemma](https://slemma.com/) 380 | - [cartodb](http://cartodb.github.io/odyssey.js/) 381 | - [Cube](http://square.github.io/cube/) 382 | - [d3plus](http://d3plus.org/) 383 | - [Data-Driven Documents(D3js)](https://d3js.org/) 384 | - [datahero](https://datahero.com/) 385 | - [dygraphs](http://dygraphs.com/) 386 | - [ECharts](http://echarts.baidu.com/index-en.html) 387 | - [exhibit](http://www.simile-widgets.org/exhibit/) 388 | - [gephi](https://gephi.org/) 389 | - [ggplot2](https://ggplot2.tidyverse.org/) 390 | - [Glue](http://www.glueviz.org/en/latest/) 391 | - [Google Chart Gallery](https://developers.google.com/chart/interactive/docs/gallery) 392 | - [highcarts](http://www.highcharts.com/) 393 | - [import.io](https://www.import.io/) 394 | - [ipychart](https://nicohlr.gitlab.io/ipychart/) 395 | - [jqplot](http://www.jqplot.com/) 396 | - [Matplotlib](http://matplotlib.org/) 397 | - [nvd3](http://nvd3.org/) 398 | - [Netron](https://github.com/lutzroeder/netron) 399 | - [Opendata-tools](http://opendata-tools.org/en/visualization/) 400 | - [Openrefine](http://openrefine.org/) 401 | - [plot.ly](https://plot.ly/) 402 | - [raw](http://rawgraphs.io) 403 | - [Seaborn](https://seaborn.pydata.org/) 404 | - [techanjs](http://techanjs.org/) 405 | - [Timeline](http://timeline.knightlab.com/) 406 | - [variancecharts](http://variancecharts.com/index.html) 407 | - [vida](https://vida.io/) 408 | - [vizzu](https://github.com/vizzuhq/vizzu-lib) 409 | - [Wrangler](http://vis.stanford.edu/wrangler/) 410 | - [r2d3](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) 411 | - [NetworkX](https://networkx.github.io/) 412 | - [Redash](https://redash.io/) 413 | - [C3](https://c3js.org/) 414 | - [TensorWatch](https://github.com/microsoft/tensorwatch) 415 | 416 | 417 | ## Journals, Publications and Magazines 418 | [top](#awesome-data-science) 419 | 420 | - [ICML](http://icml.cc/2015/) - International Conference on Machine Learning 421 | - [GECCO](https://gecco-2019.sigevo.org/index.html/HomePage) - The Genetic and Evolutionary Computation Conference (GECCO) 422 | - [epjdatascience](http://epjdatascience.springeropen.com/) 423 | - [Journal of Data Science](http://www.jds-online.com/) - an international journal devoted to applications of statistical methods at large 424 | - [Big Data Research](https://www.journals.elsevier.com/big-data-research) 425 | - [Journal of Big Data](http://journalofbigdata.springeropen.com/) 426 | - [Big Data & Society](http://journals.sagepub.com/home/bds) 427 | - [Data Science Journal](https://www.jstage.jst.go.jp/browse/dsj) 428 | - [datatau.com/news](http://www.datatau.com/news) - Like Hacker News, but for data 429 | - [Data Science Trello Board](https://trello.com/b/rbpEfMld/data-science) 430 | - [Medium Data Science Topic](https://medium.com/topic/data-science) - Data Science related publications on medium 431 | - [Towards Data Science Genetic Algorithm Topic](https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3#:~:text=A%20genetic%20algorithm%20is%20a,offspring%20of%20the%20next%20generation.) -Genetic Algorithm related Publications onTowards Data Science 432 | 433 | ## Presentations 434 | [top](#awesome-data-science) 435 | 436 | - [How to Become a Data Scientist](http://www.slideshare.net/ryanorban/how-to-become-a-data-scientist) 437 | - [Introduction to Data Science](http://www.slideshare.net/NikoVuokko/introduction-to-data-science-25391618) 438 | - [Intro to Data Science for Enterprise Big Data](http://www.slideshare.net/pacoid/intro-to-data-science-for-enterprise-big-data) 439 | - [How to Interview a Data Scientist](http://www.slideshare.net/dtunkelang/how-to-interview-a-data-scientist) 440 | - [How to Share Data with a Statistician](https://github.com/jtleek/datasharing) 441 | - [The Science of a Great Career in Data Science](http://www.slideshare.net/katemats/the-science-of-a-great-career-in-data-science) 442 | - [What Does a Data Scientist Do?](http://www.slideshare.net/datasciencelondon/big-data-sorry-data-science-what-does-a-data-scientist-do) 443 | - [Building Data Start-Ups: Fast, Big, and Focused](http://www.slideshare.net/medriscoll/driscoll-strata-buildingdatastartups25may2011clean) 444 | - [How to win data science competitions with Deep Learning](http://www.slideshare.net/0xdata/how-to-win-data-science-competitions-with-deep-learning) 445 | - [Full-Stack Data Scientist](https://www.slideshare.net/AlexeyGrigorev/fullstack-data-scientist) 446 | 447 | 448 | ## Podcasts 449 | [top](#awesome-data-science) 450 | 451 | - [AI at Home](https://podcasts.apple.com/us/podcast/data-science-at-home/id1069871378) 452 | - [AI Today](https://www.cognilytica.com/aitoday/) 453 | - [Adversarial Learning](http://adversariallearning.com/) 454 | - [Becoming a Data Scientist](https://www.becomingadatascientist.com/category/podcast/) 455 | - [Chai time Data Science](https://www.youtube.com/playlist?list=PLLvvXm0q8zUbiNdoIazGzlENMXvZ9bd3x) 456 | - [Data Crunch](http://vaultanalytics.com/datacrunch/) 457 | - [Data Engineering Podcast](https://www.dataengineeringpodcast.com/) 458 | - [Data Science at Home](https://datascienceathome.com/) 459 | - [Data Science Mixer](https://community.alteryx.com/t5/Data-Science-Mixer-Podcast/bg-p/mixer) 460 | - [Data Skeptic](https://dataskeptic.com/) 461 | - [Data Stories](http://datastori.es/) 462 | - [Datacast](https://jameskle.com/writes/category/Datacast) 463 | - [DataFramed](https://www.datacamp.com/community/podcast) 464 | - [DataTalks.Club](https://anchor.fm/datatalksclub) 465 | - [Gradient Dissent](https://wandb.ai/fully-connected/gradient-dissent) 466 | - [Learning Machines 101](http://www.learningmachines101.com/) 467 | - [Let's Data (Brazil)](https://www.youtube.com/playlist?list=PLn_z5E4dh_Lj5eogejMxfOiNX3nOhmhmM) 468 | - [Linear Digressions](http://lineardigressions.com/) 469 | - [Not So Standard Deviations](https://nssdeviations.com/) 470 | - [O'Reilly Data Show Podcast](https://www.oreilly.com/radar/topics/oreilly-data-show-podcast/) 471 | - [Partially Derivative](http://partiallyderivative.com/) 472 | - [Superdatascience](https://www.superdatascience.com/podcast/) 473 | - [The Data Engineering Show](https://www.dataengineeringshow.com/) 474 | - [The Radical AI Podcast](https://www.radicalai.org/) 475 | - [The Robot Brains Podcast](https://www.therobotbrains.ai/) 476 | - [What's The Point](https://fivethirtyeight.com/tag/whats-the-point/) 477 | 478 | ## Books 479 | [top](#awesome-data-science) 480 | 481 | - [Artificial Intelligence with Python - Tutorialspoint](https://www.tutorialspoint.com/artificial_intelligence_with_python/artificial_intelligence_with_python_tutorial.pdf) 482 | - [Machine Learning from Scratch](https://dafriedman97.github.io/mlbook/content/introduction.html) 483 | - [Probabilistic Machine Learning: An Introduction](https://probml.github.io/pml-book/book1.html) 484 | - [A Comprehensive Guide to Machine Learning](https://www.eecs189.org/static/resources/comprehensive-guide.pdf) 485 | - [Become a Leader in Data Science](https://www.manning.com/books/become-a-leader-in-data-science) - Early access 486 | - [Fighting Churn With Data](https://www.manning.com/books/fighting-churn-with-data) 487 | - [Data Science at Scale with Python and Dask](https://www.manning.com/books/data-science-at-scale-with-python-and-dask) 488 | - [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) 489 | - [The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists](http://www.thedatasciencehandbook.com/) 490 | - [Think Like a Data Scientist](https://www.manning.com/books/think-like-a-data-scientist) 491 | - [Introducing Data Science](https://www.manning.com/books/introducing-data-science) 492 | - [Practical Data Science with R](https://www.manning.com/books/practical-data-science-with-r) 493 | - [Everyday Data Science](https://www.amazon.com/dp/B08TZ1MT3W/ref=cm_sw_r_cp_apa_fabc_a0ceGbWECF9A8) & [(cheaper PDF version)](http://gum.co/everydaydata) 494 | - [Exploring Data Science](https://www.manning.com/books/exploring-data-science) - free eBook sampler 495 | - [Exploring the Data Jungle](https://www.manning.com/books/exploring-the-data-jungle) - free eBook sampler 496 | - [Classic Computer Science Problems in Python](https://www.manning.com/books/classic-computer-science-problems-in-python) 497 | - [Math for Programmers](https://www.manning.com/books/math-for-programmers) Early access 498 | - [R in Action, Third Edition](https://www.manning.com/books/r-in-action-third-edition) Early access 499 | - [Data Science Bookcamp](https://www.manning.com/books/data-science-bookcamp) Early access 500 | - [Data Science Thinking: The Next Scientific, Technological and Economic Revolution](https://www.springer.com/gp/book/9783319950914) 501 | - [Applied Data Science: Lessons Learned for the Data-Driven Business](https://www.springer.com/gp/book/9783030118204) 502 | - [The Data Science Handbook](https://www.amazon.com/Data-Science-Handbook-Field-Cady/dp/1119092949) 503 | - [Essential Natural Language Processing](https://www.manning.com/books/essential-natural-language-processing) - Early access 504 | - [Mining Massive Datasets](http://www.mmds.org/) - free e-book comprehended by an online course 505 | - [Pandas in Action](https://www.manning.com/books/pandas-in-action) - Early access 506 | - [Genetic Algorithms and Genetic Programming](https://www.taylorfrancis.com/books/9780429141973) 507 | - [Genetic algorithms in search, optimization, and machine learning](http://www2.fiit.stuba.sk/~kvasnicka/Free%20books/Goldberg_Genetic_Algorithms_in_Search.pdf) - Free Download 508 | - [Advances in Evolutionary Algorithms](https://www.intechopen.com/books/advances_in_evolutionary_algorithms) - Free Download 509 | - [Genetic Programming: New Approaches and Successful Applications](https://www.intechopen.com/books/genetic-programming-new-approaches-and-successful-applications) - Free Download 510 | - [Evolutionary Algorithms](https://www.intechopen.com/books/evolutionary-algorithms) - Free Download 511 | - [Advances in Genetic Programming, Vol. 3](https://www.cs.bham.ac.uk/~wbl/aigp3/) - Free Download 512 | - [Global Optimization Algorithms: Theory and Application](http://www.it-weise.de/projects/book.pdf) - Free Download 513 | - [Genetic Algorithms and Evolutionary Computation](http://www.talkorigins.org/faqs/genalg/genalg.html) - Free Download 514 | - [Convex Optimization](https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf) - Convex Optimization book by Stephen Boyd - Free Download 515 | - [Data Analysis with Python and PySpark](https://www.manning.com/books/data-analysis-with-python-and-pyspark) - Early access 516 | - [R for Data Science](https://r4ds.had.co.nz/) 517 | - [Build a Career in Data Science](https://www.manning.com/books/build-a-career-in-data-science) 518 | - [Machine Learning Bookcamp](https://mlbookcamp.com/) - Early access 519 | - [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 520 | - [Effective Data Science Infrastructure](https://www.manning.com/books/effective-data-science-infrastructure) 521 | - [Practical MLOps: How to Get Ready for Production Models](https://valohai.com/mlops-ebook/) 522 | - [Data Analysis with Python and PySpark](https://www.manning.com/books/data-analysis-with-python-and-pyspark) 523 | - [Regression, a Friendly guide](https://www.manning.com/books/regression-a-friendly-guide) - Early access 524 | - [Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing](https://www.oreilly.com/library/view/streaming-systems/9781491983867/) 525 | - [Data Science at the Command Line: Facing the Future with Time-Tested Tools](https://www.oreilly.com/library/view/data-science-at/9781491947845/) 526 | - [Machine Learning - CIn UFPE](https://www.cin.ufpe.br/~cavmj/Machine%20-%20Learning%20-%20Tom%20Mitchell.pdf) 527 | - [Machine Learning with Python - Tutorialspoint](https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_tutorial.pdf) 528 | - [Deep Learning](https://www.deeplearningbook.org/) 529 | - [Designing Cloud Data Platforms](https://www.manning.com/books/designing-cloud-data-platforms) - Early access 530 | - [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/) 531 | - [Deep Learning with PyTorch](https://www.simonandschuster.com/books/Deep-Learning-with-PyTorch/Eli-Stevens/9781617295263) 532 | - [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com) 533 | - [Deep Learning Cookbook](https://www.oreilly.com/library/view/deep-learning-cookbook/9781491995839/) 534 | - [Introduction to Machine Learning with Python](https://www.oreilly.com/library/view/introduction-to-machine/9781449369880/) 535 | - [Artificial Intelligence: Foundations of Computational Agents, 2nd Edition](http://artint.info/index.html) - Free HTML version 536 | - [The Quest for Artificial Intelligence: A History of Ideas and Achievements](http://ai.stanford.edu/~nilsson/QAI/qai.pdf) - Free Download 537 | - [Graph Algorithms for Data Science](https://www.manning.com/books/graph-algorithms-for-data-science) - Early access 538 | 539 | 540 | 541 | 542 | # Socialize 543 | [top](#awesome-data-science) 544 | 545 | - [Bloggers](#bloggers) 546 | - [Facebook Accounts](#facebook-accounts) 547 | - [Twitter Accounts](#twitter-accounts) 548 | - [Newsletters](#newsletters) 549 | - [Telegram Channels](#telegram-channels) 550 | - [Slack Communities](#slack-communities) 551 | - [Data Science Competitions](#competitions) 552 | 553 | ## Bloggers 554 | [top](#awesome-data-science) 555 | 556 | - [Wes McKinney](http://wesmckinney.com/archives.html) - Wes McKinney Archives. 557 | - [Matthew Russell](https://miningthesocialweb.com/) - Mining The Social Web. 558 | - [Greg Reda](http://www.gregreda.com/) - Greg Reda Personal Blog 559 | - [Kevin Davenport](http://kldavenport.com/) - Kevin Davenport Personal Blog 560 | - [Julia Evans](http://jvns.ca/) - Recurse Center alumna 561 | - [Hakan Kardas](https://www.cse.unr.edu/~hkardes/) - Personal Web Page 562 | - [Sean J. Taylor](http://seanjtaylor.com/) - Personal Web Page 563 | - [Drew Conway](http://drewconway.com/) - Personal Web Page 564 | - [Hilary Mason](https://hilarymason.com/) - Personal Web Page 565 | - [Noah Iliinsky](http://complexdiagrams.com/) - Personal Blog 566 | - [Matt Harrison](http://hairysun.com/) - Personal Blog 567 | - [Vamshi Ambati](https://allthingsds.wordpress.com/) - AllThings Data Sciene 568 | - [Prash Chan](http://www.mdmgeek.com/) - Tech Blog on Master Data Management And Every Buzz Surrounding It 569 | - [Clare Corthell](http://datasciencemasters.org/) - The Open Source Data Science Masters 570 | - [Paul Miller](http://cloudofdata.com/) 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. 571 | - [Data Science London](http://datasciencelondon.org/) Data Science London is a non-profit organization dedicated to the free, open, dissemination of data science. 572 | We are the largest data science community in Europe. 573 | We are more than 3,190 data scientists and data geeks in our community. 574 | - [Datawrangling](http://datawrangling.com/) by Peter Skomoroch. MACHINE LEARNING, DATA MINING, AND MORE 575 | - [Quora Data Science](https://www.quora.com/Data-Science) - Data Science Questions and Answers from experts 576 | - [Siah](https://openresearch.wordpress.com/) a PhD student at Berkeley 577 | - [Data Science Report](http://datasciencereport.com/) MDS, Inc. Helps Build Careers in Data Science, Advanced Analytics, Big Data Architecture, and High Performance Software Engineering 578 | - [Louis Dorard](http://www.louisdorard.com/blog/) a technology guy with a penchant for the web and for data, big and small 579 | - [Machine Learning Mastery](http://machinelearningmastery.com/) about helping professional programmers to confidently apply machine learning algorithms to address complex problems. 580 | - [Daniel Forsyth](http://www.danielforsyth.me/) - Personal Blog 581 | - [Data Science Weekly](https://www.datascienceweekly.org/) - Weekly News Blog 582 | - [Revolution Analytics](http://blog.revolutionanalytics.com/) - Data Science Blog 583 | - [R Bloggers](https://www.r-bloggers.com/) - R Bloggers 584 | - [The Practical Quant](https://practicalquant.blogspot.com/) Big data 585 | - [Datascope Analytics](https://datascopeanalytics.com/) data-driven consulting and design 586 | - [Yet Another Data Blog](http://yet-another-data-blog.blogspot.com.tr/) Yet Another Data Blog 587 | - [Spenczar](http://spenczar.com/) a data scientist at _Twitch_. I handle the whole data pipeline, from tracking to model-building to reporting. 588 | - [KD Nuggets](http://www.kdnuggets.com/) Data Mining, Analytics, Big Data, Data, Science not a blog a portal 589 | - [Meta Brown](http://www.metabrown.com/blog/) - Personal Blog 590 | - [Data Scientist](https://www.datascientists.net/) is building the data scientist culture. 591 | - [WhatSTheBigData](https://whatsthebigdata.com/) 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. 592 | - [Tevfik Kosar](http://magnus-notitia.blogspot.com.tr/) - Magnus Notitia 593 | - [New Data Scientist](http://newdatascientist.blogspot.com/) How a Social Scientist Jumps into the World of Big Data 594 | - [Harvard Data Science](http://harvarddatascience.com/) - Thoughts on Statistical Computing and Visualization 595 | - [Data Science 101](http://101.datascience.community/) - Learning To Be A Data Scientist 596 | - [Kaggle Past Solutions](http://www.chioka.in/kaggle-competition-solutions/) 597 | - [DataScientistJourney](https://datascientistjourney.wordpress.com/category/data-science/) 598 | - [NYC Taxi Visualization Blog](http://chriswhong.github.io/nyctaxi/) 599 | - [Learning Lover](http://learninglover.com/blog/) 600 | - [Dataists](http://www.dataists.com/) 601 | - [Data-Mania](http://www.data-mania.com/) 602 | - [Data-Magnum](http://data-magnum.com/) 603 | - [Map Reduce Blog](https://www.mapr.com/blog) 604 | - [P-value](http://www.p-value.info/) - Musings on data science, machine learning and stats. 605 | - [datascopeanalytics](https://datascopeanalytics.com/blog/) 606 | - [Digital transformation](http://tarrysingh.com/) 607 | - [datascientistjourney](https://datascientistjourney.wordpress.com/category/data-science/) 608 | - [Data Mania Blog](http://www.data-mania.com/blog/) - [The File Drawer](http://chris-said.io/) - Chris Said's science blog 609 | - [Emilio Ferrara's web page](http://www.emilio.ferrara.name/) 610 | - [DataNews](http://datanews.tumblr.com/) 611 | - [Reddit TextMining](https://www.reddit.com/r/textdatamining/) 612 | - [Periscopic](http://www.periscopic.com/#/news) 613 | - [Hilary Parker](https://hilaryparker.com/) 614 | - [Data Stories](http://datastori.es/) 615 | - [Data Science Lab](https://datasciencelab.wordpress.com/) 616 | - [Meaning of](http://www.kennybastani.com/) 617 | - [Adventures in Data Land](http://blog.smola.org) 618 | - [DATA MINERS BLOG](http://blog.data-miners.com/) 619 | - [Dataclysm](https://theblog.okcupid.com/) 620 | - [FlowingData](http://flowingdata.com/) - Visualization and Statistics 621 | - [Calculated Risk](http://www.calculatedriskblog.com/) 622 | - [O'reilly Learning Blog](https://www.oreilly.com/learning) 623 | - [Dominodatalab](https://blog.dominodatalab.com/) 624 | - [i am trask](http://iamtrask.github.io/) - A Machine Learning Craftsmanship Blog 625 | - [Vademecum of Practical Data Science](https://datasciencevademecum.wordpress.com/) - Handbook and recipes for data-driven solutions of real-world problems 626 | - [Dataconomy](http://dataconomy.com/) - A blog on the new emerging data economy 627 | - [Springboard](https://springboard.com/blog) - A blog with resources for data science learners 628 | - [Analytics Vidhya](https://www.analyticsvidhya.com/) - A full-fledged website about data science and analytics study material. 629 | - [Occam's Razor](https://www.kaushik.net/avinash/) - Focused on Web Analytics. 630 | - [Data School](http://www.dataschool.io/) - Data science tutorials for beginners! 631 | - [Colah's Blog](http://colah.github.io) - Blog for understanding Neural Networks! 632 | - [Sebastian's Blog](http://sebastianruder.com/#open) - Blog for NLP and transfer learning! 633 | - [Distill](http://distill.pub) - Dedicated to clear explanations of machine learning! 634 | - [Chris Albon's Website](https://chrisalbon.com/) - Data Science and AI notes 635 | - [Andrew Carr](https://andrewnc.github.io/blog/blog.html) - Data Science with Esoteric programming languages 636 | - [floydhub](https://blog.floydhub.com/introduction-to-genetic-algorithms/) - Blog for Evolutionary Algorithms 637 | - [Jingles](https://jinglescode.github.io/) - Review and extract key concepts from academic papers 638 | - [nbshare](https://www.nbshare.io/notebooks/data-science/) - Data Science notebooks 639 | - [Deep and Shallow](https://deep-and-shallow.com/) - All things Deep and Shallow in Data Science 640 | - [Loic Tetrel](https://ltetrel.github.io/) - Data science blog 641 | - [Chip Huyen's Blog](https://huyenchip.com/blog/) - ML Engineering, MLOps, and the use of ML in startups 642 | - [Maria Khalusova](https://www.mariakhalusova.com/) - Data science blog 643 | 644 | 645 | ## Facebook Accounts 646 | [top](#awesome-data-science) 647 | 648 | - [Data](https://www.facebook.com/data) 649 | - [Big Data Scientist](https://www.facebook.com/Bigdatascientist) 650 | - [Data Science Day](https://www.facebook.com/DataScienceDay/) 651 | - [Data Science Academy](https://www.facebook.com/nycdatascience) 652 | - [Facebook Data Science Page](https://www.facebook.com/pages/Data-science/431299473579193?ref=br_rs) 653 | - [Data Science London](https://www.facebook.com/pages/Data-Science-London/226174337471513) 654 | - [Data Science Technology and Corporation](https://www.facebook.com/DataScienceTechnologyCorporation?ref=br_rs) 655 | - [Data Science - Closed Group](https://www.facebook.com/groups/1394010454157077/?ref=br_rs) 656 | - [Center for Data Science](https://www.facebook.com/centerdatasciences?ref=br_rs) 657 | - [Big data hadoop NOSQL Hive Hbase](https://www.facebook.com/groups/bigdatahadoop/) 658 | - [Analytics, Data Mining, Predictive Modeling, Artificial Intelligence](https://www.facebook.com/groups/data.analytics/) 659 | - [Big Data Analytics using R](https://www.facebook.com/groups/434352233255448/) 660 | - [Big Data Analytics with R and Hadoop](https://www.facebook.com/groups/rhadoop/) 661 | - [Big Data Learnings](https://www.facebook.com/groups/bigdatalearnings/) 662 | - [Big Data, Data Science, Data Mining & Statistics](https://www.facebook.com/groups/bigdatastatistics/) 663 | - [BigData/Hadoop Expert](https://www.facebook.com/groups/BigDataExpert/) 664 | - [Data Mining / Machine Learning / AI](https://www.facebook.com/groups/machinelearningforum/) 665 | - [Data Mining/Big Data - Social Network Ana](https://www.facebook.com/groups/dataminingsocialnetworks/) 666 | - [Vademecum of Practical Data Science](https://www.facebook.com/datasciencevademecum) 667 | - [Veri Bilimi Istanbul](https://www.facebook.com/groups/veribilimiistanbul/) 668 | - [The Data Science Blog](https://www.facebook.com/theDataScienceBlog/) 669 | 670 | 671 | ## Twitter Accounts 672 | [top](#awesome-data-science) 673 | 674 | | Twitter | Description | 675 | | --- | --- | 676 | | [Big Data Combine](https://twitter.com/BigDataCombine) | Rapid-fire, live tryouts for data scientists seeking to monetize their models as trading strategies | 677 | | Big Data Mania | Data Viz Wiz , Data Journalist , Growth Hacker , Author of Data Science for Dummies (2015) | 678 | | [Big Data Science](https://twitter.com/analyticbridge) | Big Data, Data Science, Predictive Modeling, Business Analytics, Hadoop, Decision and Operations Research. | 679 | | Charlie Greenbacker | Director of Data Science at @ExploreAltamira | 680 | | [Chris Said](https://twitter.com/Chris_Said) | Data scientist at Twitter | 681 | | [Clare Corthell](https://twitter.com/clarecorthell) | Dev, Design, Data Science @mattermark #hackerei | 682 | | [DADI Charles-Abner](https://twitter.com/DadiCharles) | #datascientist @Ekimetrics. , #machinelearning #dataviz #DynamicCharts #Hadoop #R #Python #NLP #Bitcoin #dataenthousiast | 683 | | [Data Science Central](https://twitter.com/DataScienceCtrl) | Data Science Central is the industry's single resource for Big Data practitioners. | 684 | | [Data Science London](https://twitter.com/ds_ldn) | Data Science. Big Data. Data Hacks. Data Junkies. Data Startups. Open Data | 685 | | [Data Science Renee](https://twitter.com/BecomingDataSci) | Documenting my path from SQL Data Analyst pursuing an Engineering Master's Degree to Data Scientist | 686 | | [Data Science Report](https://twitter.com/TedOBrien93) | Mission is to help guide & advance careers in Data Science & Analytics | 687 | | [Data Science Tips](https://twitter.com/datasciencetips) | Tips and Tricks for Data Scientists around the world! #datascience #bigdata | 688 | | [Data Vizzard](https://twitter.com/DataVisualizati) | DataViz, Security, Military | 689 | | [DataScienceX](https://twitter.com/DataScienceX) | | 690 | | deeplearning4j | | 691 | | [DJ Patil](https://twitter.com/dpatil) | White House Data Chief, VP @ RelateIQ. | 692 | | [Domino Data Lab](https://twitter.com/DominoDataLab) | | 693 | | [Drew Conway](https://twitter.com/drewconway) | Data nerd, hacker, student of conflict. | 694 | | Emilio Ferrara | #Networks, #MachineLearning and #DataScience. I work on #Social Media. Postdoc at @IndianaUniv | 695 | | [Erin Bartolo](https://twitter.com/erinbartolo) | Running with #BigData--enjoying a love/hate relationship with its hype. @iSchoolSU #DataScience Program Mgr. | 696 | | [Greg Reda](https://twitter.com/gjreda) | Working @ _GrubHub_ about data and pandas | 697 | | [Gregory Piatetsky](https://twitter.com/kdnuggets) | KDnuggets President, Analytics/Big Data/Data Mining/Data Science expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, part-time philosopher. | 698 | | [Hadley Wickham](https://twitter.com/hadleywickham) | Chief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University. | 699 | | [Hakan Kardas](https://twitter.com/hakan_kardes) | Data Scientist | 700 | | [Hilary Mason](https://twitter.com/hmason) | Data Scientist in Residence at @accel. | 701 | | [Jeff Hammerbacher](https://twitter.com/hackingdata) | ReTweeting about data science | 702 | | [John Myles White](https://twitter.com/johnmyleswhite) | Scientist at Facebook and Julia developer. Author of Machine Learning for Hackers and Bandit Algorithms for Website Optimization. Tweets reflect my views only. | 703 | | [Juan Miguel Lavista](https://twitter.com/BDataScientist) | Principal Data Scientist @ Microsoft Data Science Team | 704 | | [Julia Evans](https://twitter.com/b0rk) | Hacker - Pandas - Data Analyze | 705 | | [Kenneth Cukier](https://twitter.com/kncukier) | The Economist's Data Editor and co-author of Big Data (http://big-data-book.com ). | 706 | | Kevin Davenport | Organizer of https://meetup.com/San-Diego-R-Users-Group/ | 707 | | [Kevin Markham](https://twitter.com/justmarkham) | Data science instructor, and founder of [Data School](http://www.dataschool.io/) | 708 | | [Kim Rees](https://twitter.com/krees) | Interactive data visualization and tools. Data flaneur. | 709 | | [Kirk Borne](https://twitter.com/KirkDBorne) | DataScientist, PhD Astrophysicist, Top #BigData Influencer. | 710 | | Linda Regber | Data story teller, visualizations. | 711 | | [Luis Rei](https://twitter.com/lmrei) | PhD Student. Programming, Mobile, Web. Artificial Intelligence, Intelligent Robotics Machine Learning, Data Mining, Natural Language Processing, Data Science. | 712 | | Mark Stevenson | Data Analytics Recruitment Specialist at Salt (@SaltJobs) Analytics - Insight - Big Data - Datascience | 713 | | [Matt Harrison](https://twitter.com/__mharrison__) | Opinions of full-stack Python guy, author, instructor, currently playing Data Scientist. Occasional fathering, husbanding, organic gardening. | 714 | | [Matthew Russell](https://twitter.com/ptwobrussell) | Mining the Social Web. | 715 | | [Mert Nuhoğlu](https://twitter.com/mertnuhoglu) | Data Scientist at BizQualify, Developer | 716 | | [Monica Rogati](https://twitter.com/mrogati) | Data @ Jawbone. Turned data into stories & products at LinkedIn. Text mining, applied machine learning, recommender systems. Ex-gamer, ex-machine coder; namer. | 717 | | [Noah Iliinsky](https://twitter.com/noahi) | Visualization & interaction designer. Practical cyclist. Author of vis books: http://www.oreilly.com/pub/au/4419 | 718 | | [Paul Miller](https://twitter.com/PaulMiller) | Cloud Computing/ Big Data/ Open Data Analyst & Consultant. Writer, Speaker & Moderator. Gigaom Research Analyst. | 719 | | [Peter Skomoroch](https://twitter.com/peteskomoroch) | Creating intelligent systems to automate tasks & improve decisions. Entrepreneur, ex Principal Data Scientist @LinkedIn. Machine Learning, ProductRei, Networks | 720 | | [Prash Chan](https://twitter.com/MDMGeek) | Solution Architect @ IBM, Master Data Management, Data Quality & Data Governance Blogger. Data Science, Hadoop, Big Data & Cloud. | 721 | | [Quora Data Science](https://twitter.com/q_datascience) | Quora's data science topic | 722 | | [R-Bloggers](https://twitter.com/Rbloggers) | Tweet blog posts from the R blogosphere, data science conferences and (!) open jobs for data scientists. | 723 | | [Rand Hindi](https://twitter.com/randhindi) | | 724 | | [Randy Olson](https://twitter.com/randal_olson) | Computer scientist researching artificial intelligence. Data tinkerer. Community leader for @DataIsBeautiful. #OpenScience advocate. | 725 | | [Recep Erol](https://twitter.com/EROLRecep) | Data Science geek @ UALR | 726 | | [Ryan Orban](https://twitter.com/ryanorban) | Data scientist, genetic origamist, hardware aficionado | 727 | | [Sean J. Taylor](https://twitter.com/seanjtaylor) | Social Scientist. Hacker. Facebook Data Science Team. Keywords: Experiments, Causal Inference, Statistics, Machine Learning, Economics. | 728 | | [Silvia K. Spiva](https://twitter.com/silviakspiva) | #DataScience at Cisco | 729 | | [Harsh B. Gupta](https://twitter.com/harshbg) | Data Scientist at BBVA Compass | 730 | | [Spencer Nelson](https://twitter.com/spenczar_n) | Data nerd | 731 | | [Talha Oz](https://twitter.com/tozCSS) | Enjoys ABM, SNA, DM, ML, NLP, HI, Python, Java. Top percentile kaggler/data scientist | 732 | | [Tasos Skarlatidis](https://twitter.com/anskarl) | Complex Event Processing, Big Data, Artificial Intelligence and Machine Learning. Passionate about programming and open-source. | 733 | | [Terry Timko](https://twitter.com/Terry_Timko) | InfoGov; Bigdata; Data as a Service; Data Science; Open, Social & Business Data Convergence | 734 | | [Tony Baer](https://twitter.com/TonyBaer) | IT analyst with Ovum covering Big Data & data management with some systems engineering thrown in. | 735 | | [Tony Ojeda](https://twitter.com/tonyojeda3) | Data Scientist , Author , Entrepreneur. Co-founder @DataCommunityDC. Founder @DistrictDataLab. #DataScience #BigData #DataDC | 736 | | [Vamshi Ambati](https://twitter.com/vambati) | Data Science @ PayPal. #NLP, #machinelearning; PhD, Carnegie Mellon alumni (Blog: https://allthingsds.wordpress.com ) | 737 | | [Wes McKinney](https://twitter.com/wesmckinn) | Pandas (Python Data Analysis library). | 738 | | [WileyEd](https://twitter.com/WileyEd) | Senior Manager - @Seagate Big Data Analytics @McKinsey Alum #BigData + #Analytics Evangelist #Hadoop, #Cloud, #Digital, & #R Enthusiast | 739 | | [WNYC Data News Team](https://twitter.com/datanews) | The data news crew at @WNYC. Practicing data-driven journalism, making it visual and showing our work. | 740 | | [Alexey Grigorev](https://twitter.com/Al_Grigor) | Data science author | 741 | 742 | 743 | ## Newsletters 744 | [top](#awesome-data-science) 745 | 746 | - [AI Digest](https://aidigest.net/). A weekly newsletter to keep up to date with AI, machine learning, and data science. [Archive](https://aidigest.net/digests). 747 | - [DataTalks.Club](https://datatalks.club). A weekly newsletter about data-related things. [Archive](https://us19.campaign-archive.com/home/?u=0d7822ab98152f5afc118c176&id=97178021aa). 748 | - [The Analytics Engineering Roundup](https://roundup.getdbt.com/about). A newsletter about data science. [Archive](https://roundup.getdbt.com/archive). 749 | 750 | 751 | ## Youtube Videos & Channels 752 | [top](#awesome-data-science) 753 | 754 | - [What is machine learning?](https://www.youtube.com/watch?v=WXHM_i-fgGo) 755 | - [Andrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24) 756 | - [Data36 - Data Science for Beginners by Tomi Mester](https://www.youtube.com/c/TomiMesterData36comDataScienceForBeginners) 757 | - [Deep Learning: Intelligence from Big Data](https://www.youtube.com/watch?v=czLI3oLDe8M) 758 | - [Interview with Google's AI and Deep Learning 'Godfather' Geoffrey Hinton](https://www.youtube.com/watch?v=1Wp3IIpssEc) 759 | - [Introduction to Deep Learning with Python](https://www.youtube.com/watch?v=S75EdAcXHKk) 760 | - [What is machine learning, and how does it work?](https://www.youtube.com/watch?v=elojMnjn4kk) 761 | - [Data School](https://www.youtube.com/channel/UCnVzApLJE2ljPZSeQylSEyg) - Data Science Education 762 | - [Neural Nets for Newbies by Melanie Warrick (May 2015)](https://www.youtube.com/watch?v=Cu6A96TUy_o) 763 | - [Neural Networks video series by Hugo Larochelle](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) 764 | - [Google DeepMind co-founder Shane Legg - Machine Super Intelligence](https://www.youtube.com/watch?v=evNCyRL3DOU) 765 | - [Data Science Primer](https://www.youtube.com/watch?v=cHzvYxBN9Ls&list=PLPqVjP3T4RIRsjaW07zoGzH-Z4dBACpxY) 766 | - [Data Science with Genetic Algorithms](https://www.youtube.com/watch?v=lpD38NxTOnk) 767 | - [Data Science for Beginners](https://www.youtube.com/playlist?list=PL2zq7klxX5ATMsmyRazei7ZXkP1GHt-vs) 768 | - [DataTalks.Club](https://www.youtube.com/channel/UCDvErgK0j5ur3aLgn6U-LqQ) 769 | - [Mildlyoverfitted - Tutorials on intermediate ML/DL topics](https://www.youtube.com/channel/UCYBSjwkGTK06NnDnFsOcR7g) 770 | - [mlops.community - Interviews of industry experts about production ML](https://www.youtube.com/channel/UCYBSjwkGTK06NnDnFsOcR7g) 771 | - [ML Street Talk - Unabashedly technical and non-commercial, so you will hear no annoying pitches.](https://www.youtube.com/c/machinelearningstreettalk) 772 | - [Neural networks by 3Blue1Brown ](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) 773 | - [Neural networks from scratch by Sentdex](https://www.youtube.com/playlist?list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3) 774 | - [Manning Publications YouTube channel](https://www.youtube.com/c/ManningPublications/featured) 775 | - [Ask Dr Chong: How to Lead in Data Science - Part 1](https://youtu.be/JYuQZii5o58) 776 | - [Ask Dr Chong: How to Lead in Data Science - Part 2](https://youtu.be/SzqIXV-O-ko) 777 | - [Ask Dr Chong: How to Lead in Data Science - Part 3](https://youtu.be/Ogwm7k_smTA) 778 | - [Ask Dr Chong: How to Lead in Data Science - Part 4](https://youtu.be/a9usjdzTxTU) 779 | - [Ask Dr Chong: How to Lead in Data Science - Part 5](https://youtu.be/MYdQq-F3Ws0) 780 | - [Ask Dr Chong: How to Lead in Data Science - Part 6](https://youtu.be/LOOt4OVC3hY) 781 | 782 | 783 | ## Telegram Channels 784 | [top](#awesome-data-science) 785 | 786 | - [Open Data Science](https://t.me/opendatascience) – 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. 787 | - [Loss function porn](https://t.me/loss_function_porn) — Beautiful posts on DS/ML theme with video or graphic vizualization. 788 | - [Machinelearning](https://t.me/ai_machinelearning_big_data) – Daily ML news. 789 | 790 | 791 | ## Slack Communities 792 | [top](#awesome-data-science) 793 | 794 | - [DataTalks.Club](https://datatalks.club) 795 | 796 | 797 | # Github Groups 798 | - [Berkeley Institute for Data Science](https://github.com/BIDS) 799 | 800 | 801 | ## Competitions 802 | 803 | Some data mining competition platforms 804 | 805 | - [Kaggle](https://www.kaggle.com/) 806 | - [DrivenData](https://www.drivendata.org/) 807 | - [Analytics Vidhya](http://datahack.analyticsvidhya.com/) 808 | - [InnoCentive](https://www.innocentive.com/) 809 | - [TuneedIT](http://tunedit.org/challenges) 810 | - [Microprediction](https://www.microprediction.com/python-1) 811 | 812 | 813 | # Fun 814 | 815 | - [Infographic](#infographic) 816 | - [Data Sets](#data-sets) 817 | - [Comics](#comics) 818 | 819 | 820 | ## Infographic 821 | 822 | | Preview | Description | 823 | | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | 824 | | [](https://i.imgur.com/0OoLaa5.png) | [Key differences of a data scientist vs. data engineer](https://searchbusinessanalytics.techtarget.com/feature/Key-differences-of-a-data-scientist-vs-data-engineer) | 825 | | [](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/DataScienceEightSteps_Full.png) | A visual guide to Becoming a Data Scientist in 8 Steps by [DataCamp](https://www.datacamp.com) [(img)](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/DataScienceEightSteps_Full.png) | 826 | | [](https://i.imgur.com/FxsL3b8.png) | Mindmap on required skills ([img](https://i.imgur.com/FxsL3b8.png)) | 827 | | [](http://nirvacana.com/thoughts/wp-content/uploads/2013/07/RoadToDataScientist1.png) | Swami Chandrasekaran made a [Curriculum via Metro map](http://nirvacana.com/thoughts/becoming-a-data-scientist/). | 828 | | [](https://i.imgur.com/4ZBBvb0.png) | by [@kzawadz](https://twitter.com/kzawadz) via [twitter](https://twitter.com/MktngDistillery/status/538671811991715840) | 829 | | [](https://i.imgur.com/xLY3XZn.jpg) | By [Data Science Central](http://www.datasciencecentral.com/) | 830 | 831 | | [](https://i.imgur.com/0TydZ4M.png) | Data Science Wars: R vs Python | 832 | | [](https://i.imgur.com/HnRwlce.png) | How to select statistical or machine learning techniques | 833 | | [](http://scikit-learn.org/stable/_static/ml_map.png) | Choosing the Right Estimator | 834 | | [](https://i.imgur.com/uEqMwZa.png) | The Data Science Industry: Who Does What | 835 | | [](https://i.imgur.com/RsHqY84.png) | Data Science ~~Venn~~ Euler Diagram | 836 | | [](https://www.springboard.com/blog/wp-content/uploads/2016/03/20160324_springboard_vennDiagram.png) | Different Data Science Skills and Roles from [this article](https://www.springboard.com/blog/data-science-career-paths-different-roles-industry/) by Springboard | 837 | | [Data Fallacies To Avoid](https://data-literacy.geckoboard.com/poster/) | A simple and friendly way of teaching your non-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/). | 838 | 839 | ## Data Sets 840 | 841 | - [Academic Torrents](http://academictorrents.com/) 842 | - [hadoopilluminated.com](http://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html) 843 | - [data.gov](https://catalog.data.gov/dataset) - The home of the U.S. Government's open data 844 | - [United States Census Bureau](http://www.census.gov/) 845 | - [usgovxml.com](http://usgovxml.com/) 846 | - [enigma.com](http://enigma.com/) - Navigate the world of public data - Quickly search and analyze billions of public records published by governments, companies and organizations. 847 | - [datahub.io](https://datahub.io/) 848 | - [aws.amazon.com/datasets](https://aws.amazon.com/datasets/) 849 | - [datacite.org](https://www.datacite.org) 850 | - [The official portal for European data](https://data.europa.eu/en) 851 | - [quandl.com](https://www.quandl.com/) - Get the data you need in the form you want; instant download, API or direct to your app. 852 | - [figshare.com](https://figshare.com/) 853 | - [GeoLite Legacy Downloadable Databases](http://dev.maxmind.com/geoip/legacy/geolite/) 854 | - [Quora's Big Datasets Answer](https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public) 855 | - [Public Big Data Sets](http://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html) 856 | - [Kaggle Datasets](https://www.kaggle.com/datasets) 857 | - [A Deep Catalog of Human Genetic Variation](http://www.internationalgenome.org/data) 858 | - [A community-curated database of well-known people, places, and things](https://developers.google.com/freebase/) 859 | - [Google Public Data](http://www.google.com/publicdata/directory) 860 | - [World Bank Data](http://data.worldbank.org/) 861 | - [NYC Taxi data](http://chriswhong.github.io/nyctaxi/) 862 | - [Open Data Philly](https://www.opendataphilly.org/) Connecting people with data for Philadelphia 863 | - [grouplens.org](https://grouplens.org/datasets/) Sample movie (with ratings), book and wiki datasets 864 | - [UC Irvine Machine Learning Repository](http://archive.ics.uci.edu/ml/) - contains data sets good for machine learning 865 | - [research-quality data sets](http://web.archive.org/web/20150320022752/https://bitly.com/bundles/hmason/1) by [Hilary Mason](http://web.archive.org/web/20150501033715/https://bitly.com/u/hmason/bundles) 866 | - [National Climatic Data Center - NOAA](https://www.ncdc.noaa.gov/) 867 | - [ClimateData.us](http://www.climatedata.us/) (related: [U.S. Climate Resilience Toolkit](https://toolkit.climate.gov/)) 868 | - [r/datasets](https://www.reddit.com/r/datasets/) 869 | - [MapLight](https://www.maplight.org/data-series) - 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 870 | - [GHDx](http://ghdx.healthdata.org/) - Institute for Health Metrics and Evaluation - a catalog of health and demographic datasets from around the world and including IHME results 871 | - [St. Louis Federal Reserve Economic Data - FRED](https://fred.stlouisfed.org/) 872 | - [New Zealand Institute of Economic Research – Data1850](https://data1850.nz/) 873 | - [Open Data Sources](https://github.com/datasciencemasters/data) 874 | - [UNICEF Data](https://data.unicef.org/) 875 | - [undata](http://data.un.org/) 876 | - [NASA SocioEconomic Data and Applications Center - SEDAC](http://sedac.ciesin.columbia.edu/) 877 | - [The GDELT Project](http://gdeltproject.org/) 878 | - [Sweden, Statistics](http://www.scb.se/en/) 879 | - [Github free data source list](http://www.datasciencecentral.com/profiles/blogs/great-github-list-of-public-data-sets) 880 | - [StackExchange Data Explorer](http://data.stackexchange.com) - an open source tool for running arbitrary queries against public data from the Stack Exchange network. 881 | - [San Fransisco Government Open Data](https://data.sfgov.org/) 882 | - [IBM Blog about open data](http://www.datasciencecentral.com/profiles/blogs/the-free-big-data-sources-everyone-should-know) 883 | - [IBM Asset Dataset](https://developer.ibm.com/exchanges/data/) 884 | - [Open data Index](http://index.okfn.org/) 885 | - [Public Git Archive](https://github.com/src-d/datasets/tree/master/PublicGitArchive) 886 | - [GHTorrent](http://ghtorrent.org/) 887 | - [Microsoft Research Open Data](https://msropendata.com/) 888 | - [Open Government Data Platform India](https://data.gov.in/) 889 | - [Google Dataset Search (beta)](https://toolbox.google.com/datasetsearch) 890 | - [NAYN.CO Turkish News with categories](https://github.com/naynco/nayn.data) 891 | - [Covid-19](https://github.com/datasets/covid-19) 892 | - [Covid-19 Google](https://github.com/google-research/open-covid-19-data) 893 | - [Enron Email Dataset](https://www.cs.cmu.edu/~./enron/) 894 | - [5000 Images of Clothes](https://github.com/alexeygrigorev/clothing-dataset) 895 | 896 | ## Comics 897 | 898 | - [Comic compilation](https://medium.com/@nikhil_garg/a-compilation-of-comics-explaining-statistics-data-science-and-machine-learning-eeefbae91277) 899 | - [Cartoons](https://www.kdnuggets.com/websites/cartoons.html) 900 | 901 | 902 | ## Data Science Collection 903 | 904 | [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) [![Subscribe to new links](https://app.releasly.co/assets/badges/badge-awesome-blue.svg)](https://app.releasly.co/sites/academic/awesome-datascience?utm_source=github_badge) 905 | 906 | ## Hobby 907 | - [Music Production Collection](https://github.com/ad-si/awesome-music-production) 908 | 909 | ## Other Lists 910 | 911 | - Other amazingly awesome lists can be found in the [awesome-awesomeness](https://github.com/bayandin/awesome-awesomeness) 912 | - [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) 913 | - [lists](https://github.com/jnv/lists) 914 | - [awesome-dataviz](https://github.com/fasouto/awesome-dataviz) 915 | - [awesome-python](https://github.com/vinta/awesome-python) 916 | - [Data Science IPython Notebooks.](https://github.com/donnemartin/data-science-ipython-notebooks) 917 | - [awesome-r](https://github.com/qinwf/awesome-R) 918 | - [awesome-datasets](https://github.com/caesar0301/awesome-public-datasets) 919 | - [awesome-Machine Learning & Deep Learning Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md) 920 | - [Awesome Data Science Ideas](https://github.com/JosPolfliet/awesome-datascience-ideas) 921 | - [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) 922 | - [Community Curated Data Science Resources](https://hackr.io/tutorials/learn-data-science) 923 | - [Awesome Machine Learning On Source Code](https://github.com/src-d/awesome-machine-learning-on-source-code) 924 | - [Awesome Community Detection](https://github.com/benedekrozemberczki/awesome-community-detection) 925 | - [Awesome Graph Classification](https://github.com/benedekrozemberczki/awesome-graph-classification) 926 | - [Awesome Decision Tree Papers](https://github.com/benedekrozemberczki/awesome-decision-tree-papers) 927 | - [Awesome Fraud Detection Papers](https://github.com/benedekrozemberczki/awesome-fraud-detection-papers) 928 | - [Awesome Gradient Boosting Papers](https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers) 929 | - [Awesome Computer Vision Models](https://github.com/nerox8664/awesome-computer-vision-models) 930 | - [Awesome Monte Carlo Tree Search](https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers) 931 | - [Glossary of common statistics and ML terms](https://www.analyticsvidhya.com/glossary-of-common-statistics-and-machine-learning-terms/) 932 | - [100 NLP Papers](https://github.com/mhagiwara/100-nlp-papers) 933 | - [Awesome Game Datasets](https://github.com/leomaurodesenv/game-datasets#readme) 934 | - [Data Science Interviews Questions](https://github.com/alexeygrigorev/data-science-interviews) 935 | - [Awesome Explainable Graph Reasoning](https://github.com/AstraZeneca/awesome-explainable-graph-reasoning) 936 | - [Top Data Science Interview Questions](https://www.interviewbit.com/data-science-interview-questions/) 937 | - [Awesome Drug Synergy, Interaction and Polypharmacy Prediction](https://github.com/AstraZeneca/polypharmacy-ddi-synergy-survey) 938 | 939 | ## License 940 | MIT License & [cc](https://creativecommons.org/licenses/by/4.0/) license 941 | 942 | Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License. 943 | 944 | [Back to top](#data-science-collection) 945 | --------------------------------------------------------------------------------