diff --git a/README.md b/README.md index 951dae5..8c9d194 100644 --- a/README.md +++ b/README.md @@ -45,7 +45,7 @@ This is a shortcut path to start studying **Data Science**. Just follow the step - [Miscellaneous Tools](#miscellaneous-tools) - [Literature and Media](#literature-and-media) - [Books](#books) - - [Book Deals (Affiliated)](#book-deals-affiliated-) + - [Book Deals (Affiliated)](#book-deals-affiliated) - [Journals, Publications, and Magazines](#journals-publications-and-magazines) - [Newsletters](#newsletters) - [Bloggers](#bloggers) @@ -121,9 +121,7 @@ How do you learn data science? By doing data science, of course! Okay, okay - th - [Data science your way](https://github.com/jadianes/data-science-your-way) - [PySpark Cheatsheet](https://github.com/kevinschaich/pyspark-cheatsheet) - [Machine Learning, Data Science and Deep Learning with Python ](https://www.manning.com/livevideo/machine-learning-data-science-and-deep-learning-with-python) -- [How To Label Data](https://www.lighttag.io/how-to-label-data/) - [Your Guide to Latent Dirichlet Allocation](https://medium.com/@lettier/how-does-lda-work-ill-explain-using-emoji-108abf40fa7d) -- [Over 1000 Data Science Online Courses at Classpert Online Search Engine](https://classpert.com/search/data-science) - [Tutorials of source code from the book Genetic Algorithms with Python by Clinton Sheppard](https://github.com/handcraftsman/GeneticAlgorithmsWithPython) - [Tutorials to get started on signal processing for machine learning](https://github.com/jinglescode/python-signal-processing) - [Realtime deployment](https://www.microprediction.com/python-1) Tutorial on Python time-series model deployment. @@ -144,12 +142,10 @@ How do you learn data science? By doing data science, of course! Okay, okay - th - [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/) - [AI Expert Roadmap](https://github.com/AMAI-GmbH/AI-Expert-Roadmap) - Roadmap to becoming an Artificial Intelligence Expert - [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...) -- [Skillcombo - Data Science](https://skillcombo.com/courses/development/data-science/free/) - 1000+ free online Data Science courses - [Learning from Data](https://home.work.caltech.edu/telecourse.html) - Introduction to machine learning covering basic theory, algorithms and applications - [Kaggle](https://www.kaggle.com/learn) - Learn about Data Science, Machine Learning, Python etc - [ML Observability Fundamentals](https://arize.com/ml-observability-fundamentals/) - Learn how to monitor and root-cause production ML issues. - [Weights & Biases Effective MLOps: Model Development](https://www.wandb.courses/courses/effective-mlops-model-development) - Free Course and Certification for building an end-to-end machine using W&B -- [Python for Machine Learning](https://globalaihub.com/courses/introduction-to-python-the-road-to-machine-learning/) - Start your journey to machine learning with Python, one of the most powerful programming languages. - [Python for Data Science by Scaler](https://www.scaler.com/topics/course/python-for-data-science/) - This course is designed to empower beginners with the essential skills to excel in today's data-driven world. The comprehensive curriculum will give you a solid foundation in statistics, programming, data visualization, and machine learning. - [MLSys-NYU-2022](https://github.com/jacopotagliabue/MLSys-NYU-2022/tree/main) - Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022. - [Hands-on Train and Deploy ML](https://github.com/Paulescu/hands-on-train-and-deploy-ml) - A hands-on course to train and deploy a serverless API that predicts crypto prices. @@ -189,7 +185,7 @@ How do you learn data science? By doing data science, of course! Okay, okay - th - [Coursera Natural Language Processing Specialization](https://www.coursera.org/specializations/natural-language-processing) - [Coursera GAN Specialization](https://www.coursera.org/specializations/generative-adversarial-networks-gans) - [Codecademy's Data Science](https://www.codecademy.com/learn/paths/data-science) -- [Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/) - Linear Algebra course by Gilbert Strang +- [Linear Algebra](https://ocw.mit.edu/courses/18-06sc-linear-algebra-fall-2011/) - Linear Algebra course by Gilbert Strang - [A 2020 Vision of Linear Algebra (G. Strang)](https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-linear-algebra-spring-2020/) - [Python for Data Science Foundation Course](https://intellipaat.com/academy/course/python-for-data-science-free-training/) - [Data Science: Statistics & Machine Learning](https://www.coursera.org/specializations/data-science-statistics-machine-learning) @@ -226,7 +222,7 @@ How do you learn data science? By doing data science, of course! Okay, okay - th - [Msc in Data Science @ The University of Edinburgh](https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=902) - [Master of Management Analytics @ Queen's University](https://smith.queensu.ca/grad_studies/mma/index.php) - [Master of Data Science @ Illinois Institute of Technology](https://www.iit.edu/academics/programs/data-science-mas) -- [Master of Applied Data Science @ The University of Michigan](https://www.si.umich.edu/programs/master-applied-data-science-online) +- [Master of Applied Data Science @ The University of Michigan](https://www.si.umich.edu/programs/master-applied-data-science) - [Master Data Science and Artificial Intelligence @ Eindhoven University of Technology](https://www.tue.nl/en/education/graduate-school/master-data-science-and-artificial-intelligence/) - [Master's Degree in Data Science and Computer Engineering @ University of Granada](https://masteres.ugr.es/datcom/) @@ -427,7 +423,6 @@ These are some Machine Learning and Data Mining algorithms and models help you t **[`^ back to top ^`](#awesome-data-science)** - [altair](https://altair-viz.github.io/) -- [addepar](https://opensource.addepar.com/ember-charts/#/overview) - [amcharts](https://www.amcharts.com/) - [anychart](https://www.anychart.com/) - [bokeh](https://bokeh.org/) @@ -438,7 +433,6 @@ These are some Machine Learning and Data Mining algorithms and models help you t - [d3plus](https://d3plus.org/) - [Data-Driven Documents(D3js)](https://d3js.org/) - [dygraphs](https://dygraphs.com/) -- [ECharts](https://echarts.baidu.com/index-en.html) - [exhibit](https://www.simile-widgets.org/exhibit/) - [gephi](https://gephi.org/) - [ggplot2](https://ggplot2.tidyverse.org/) @@ -446,7 +440,6 @@ These are some Machine Learning and Data Mining algorithms and models help you t - [Google Chart Gallery](https://developers.google.com/chart/interactive/docs/gallery) - [highcarts](https://www.highcharts.com/) - [import.io](https://www.import.io/) -- [jqplot](https://www.jqplot.com/) - [Matplotlib](https://matplotlib.org/) - [nvd3](https://nvd3.org/) - [Netron](https://github.com/lutzroeder/netron) @@ -461,7 +454,7 @@ These are some Machine Learning and Data Mining algorithms and models help you t - [vida](https://vida.io/) - [vizzu](https://github.com/vizzuhq/vizzu-lib) - [Wrangler](http://vis.stanford.edu/wrangler/) -- [r2d3](https://www.r2d3.us/visual-intro-to-machine-learning-part-1/) +- [r2d3](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) - [NetworkX](https://networkx.org/) - [Redash](https://redash.io/) - [C3](https://c3js.org/) @@ -501,17 +494,17 @@ These are some Machine Learning and Data Mining algorithms and models help you t | [Wolfram Data Science Platform](https://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 generate rich interactive reports—all powered by the revolutionary knowledge-based Wolfram Language. | | [Datadog](https://www.datadoghq.com/) | Solutions, code, and devops for high-scale data science. | | [Variance](https://variancecharts.com/) | Build powerful data visualizations for the web without writing JavaScript | -| [Kite Development Kit](https://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. | +| [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. | | [Domino Data Labs](https://www.dominodatalab.com) | Run, scale, share, and deploy your models — without any infrastructure or setup. | | [Apache Flink](https://flink.apache.org/) | A platform for efficient, distributed, general-purpose data processing. | | [Apache Hama](https://hama.apache.org/) | Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce. | -| [Weka](https://www.cs.waikato.ac.nz/ml/weka/) | Weka is a collection of machine learning algorithms for data mining tasks. | +| [Weka](https://ml.cms.waikato.ac.nz/weka/index.html) | Weka is a collection of machine learning algorithms for data mining tasks. | | [Octave](https://www.gnu.org/software/octave/) | GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab) | | [Apache Spark](https://spark.apache.org/) | Lightning-fast cluster computing | | [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. | | [Data Mechanics](https://www.datamechanics.co) | A data science and engineering platform making Apache Spark more developer-friendly and cost-effective. | | [Caffe](https://caffe.berkeleyvision.org/) | Deep Learning Framework | -| [Torch](https://torch.ch/) | A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT | +| [Torch](http://torch.ch/) | A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT | | [Nervana's python based Deep Learning Framework](https://github.com/NervanaSystems/neon) | Intel® Nervana™ reference deep learning framework committed to best performance on all hardware. | | [Skale](https://github.com/skale-me/skale) | High performance distributed data processing in NodeJS | | [Aerosolve](https://airbnb.io/aerosolve/) | A machine learning package built for humans. | @@ -531,7 +524,6 @@ These are some Machine Learning and Data Mining algorithms and models help you t | [Lambdo](https://github.com/asavinov/lambdo) | is a workflow engine that significantly simplifies data analysis by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation. | | [Feast](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. | | [Polyaxon](https://github.com/polyaxon/polyaxon) | A platform for reproducible and scalable machine learning and deep learning. | -| [LightTag](https://www.lighttag.io/) | Text Annotation Tool for teams | | [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 | | [Trains](https://github.com/allegroai/clearml) | Auto-Magical Experiment Manager, Version Control & DevOps for AI | | [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. | @@ -604,7 +596,6 @@ This section includes some additional reading material, channels to watch, and t - [Artificial Intelligence with Python - Tutorialspoint](https://www.tutorialspoint.com/artificial_intelligence_with_python/artificial_intelligence_with_python_tutorial.pdf) - [Machine Learning from Scratch](https://dafriedman97.github.io/mlbook/content/introduction.html) - [Probabilistic Machine Learning: An Introduction](https://probml.github.io/pml-book/book1.html) -- [A Comprehensive Guide to Machine Learning](https://www.eecs189.org/static/resources/comprehensive-guide.pdf) - [How to Lead in Data Science](https://www.manning.com/books/how-to-lead-in-data-science) - Early Access - [Fighting Churn With Data](https://www.manning.com/books/fighting-churn-with-data) - [Data Science at Scale with Python and Dask](https://www.manning.com/books/data-science-with-python-and-dask) @@ -624,14 +615,13 @@ This section includes some additional reading material, channels to watch, and t - [Applied Data Science: Lessons Learned for the Data-Driven Business](https://www.springer.com/gp/book/9783030118204) - [The Data Science Handbook](https://www.amazon.com/Data-Science-Handbook-Field-Cady/dp/1119092949) - [Essential Natural Language Processing](https://www.manning.com/books/getting-started-with-natural-language-processing) - Early access -- [Mining Massive Datasets](https://www.mmds.org/) - free e-book comprehended by an online course +- [Mining Massive Datasets](http://www.mmds.org/) - free e-book comprehended by an online course - [Pandas in Action](https://www.manning.com/books/pandas-in-action) - Early access - [Genetic Algorithms and Genetic Programming](https://www.taylorfrancis.com/books/9780429141973) - [Advances in Evolutionary Algorithms](https://www.intechopen.com/books/advances_in_evolutionary_algorithms) - Free Download - [Genetic Programming: New Approaches and Successful Applications](https://www.intechopen.com/books/genetic-programming-new-approaches-and-successful-applications) - Free Download - [Evolutionary Algorithms](https://www.intechopen.com/books/evolutionary-algorithms) - Free Download -- [Advances in Genetic Programming, Vol. 3](https://www.cs.bham.ac.uk/~wbl/aigp3/) - Free Download -- [Global Optimization Algorithms: Theory and Application](https://www.it-weise.de/projects/book.pdf) - Free Download +- [Advances in Genetic Programming, Vol. 3](http://www0.cs.ucl.ac.uk/staff/W.Langdon/aigp3/) - Free Download - [Genetic Algorithms and Evolutionary Computation](https://www.talkorigins.org/faqs/genalg/genalg.html) - Free Download - [Convex Optimization](https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf) - Convex Optimization book by Stephen Boyd - Free Download - [Data Analysis with Python and PySpark](https://www.manning.com/books/data-analysis-with-python-and-pyspark) - Early Access @@ -645,14 +635,13 @@ This section includes some additional reading material, channels to watch, and t - [Regression, a Friendly guide](https://www.manning.com/books/regression-a-friendly-guide) - Early Access - [Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing](https://www.oreilly.com/library/view/streaming-systems/9781491983867/) - [Data Science at the Command Line: Facing the Future with Time-Tested Tools](https://www.oreilly.com/library/view/data-science-at/9781491947845/) -- [Machine Learning - CIn UFPE](https://www.cin.ufpe.br/~cavmj/Machine%20-%20Learning%20-%20Tom%20Mitchell.pdf) - [Machine Learning with Python - Tutorialspoint](https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_tutorial.pdf) - [Deep Learning](https://www.deeplearningbook.org/) - [Designing Cloud Data Platforms](https://www.manning.com/books/designing-cloud-data-platforms) - Early Access - [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/) - [The Elements of Statistical Learning: Data Mining, Inference, and Prediction](https://hastie.su.domains/ElemStatLearn/) - [Deep Learning with PyTorch](https://www.simonandschuster.com/books/Deep-Learning-with-PyTorch/Eli-Stevens/9781617295263) -- [Neural Networks and Deep Learning](https://neuralnetworksanddeeplearning.com) +- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com) - [Deep Learning Cookbook](https://www.oreilly.com/library/view/deep-learning-cookbook/9781491995839/) - [Introduction to Machine Learning with Python](https://www.oreilly.com/library/view/introduction-to-machine/9781449369880/) - [Artificial Intelligence: Foundations of Computational Agents, 2nd Edition](https://artint.info/index.html) - Free HTML version @@ -673,7 +662,7 @@ This section includes some additional reading material, channels to watch, and t - [Machine Learning For Absolute Beginners](https://www.amazon.in/Machine-Learning-Absolute-Beginners-Introduction-ebook/dp/B07335JNW1) - [Unifying Business, Data, and Code: Designing Data Products with JSON Schema](https://learning.oreilly.com/library/view/unifying-business-data/9781098144999/) -#### Book Deals (Affiliated) 🛍 +#### Book Deals (Affiliated) - [eBook sale - Save up to 45% on eBooks!](https://www.manning.com/?utm_source=mikrobusiness&utm_medium=affiliate&utm_campaign=ebook_sale_8_8_22) @@ -698,12 +687,10 @@ This section includes some additional reading material, channels to watch, and t - [Data Science Trello Board](https://trello.com/b/rbpEfMld/data-science) - [Medium Data Science Topic](https://medium.com/tag/data-science) - Data Science related publications on medium - [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 towards Data Science -- [all AI news](https://allainews.com/) - The AI/ML/Big Data news aggregator platform ### Newsletters **[`^ back to top ^`](#awesome-data-science)** -- [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). - [DataTalks.Club](https://datatalks.club). A weekly newsletter about data-related things. [Archive](https://us19.campaign-archive.com/home/?u=0d7822ab98152f5afc118c176&id=97178021aa). - [The Analytics Engineering Roundup](https://roundup.getdbt.com/about). A newsletter about data science. [Archive](https://roundup.getdbt.com/archive). @@ -712,22 +699,18 @@ This section includes some additional reading material, channels to watch, and t - [Wes McKinney](https://wesmckinney.com/archives.html) - Wes McKinney Archives. - [Matthew Russell](https://miningthesocialweb.com/) - Mining The Social Web. -- [Greg Reda](https://www.gregreda.com/) - Greg Reda Personal Blog +- [Greg Reda](http://www.gregreda.com/) - Greg Reda Personal Blog - [Kevin Davenport](https://kldavenport.com/) - Kevin Davenport Personal Blog - [Julia Evans](https://jvns.ca/) - Recurse Center alumna - [Hakan Kardas](https://www.cse.unr.edu/~hkardes/) - Personal Web Page - [Sean J. Taylor](https://seanjtaylor.com/) - Personal Web Page -- [Drew Conway](https://drewconway.com/) - Personal Web Page +- [Drew Conway](http://drewconway.com/) - Personal Web Page - [Hilary Mason](https://hilarymason.com/) - Personal Web Page -- [Noah Iliinsky](https://complexdiagrams.com/) - Personal Blog +- [Noah Iliinsky](http://complexdiagrams.com/) - Personal Blog - [Matt Harrison](https://hairysun.com/) - Personal Blog - [Vamshi Ambati](https://allthingsds.wordpress.com/) - AllThings Data Sciene - [Prash Chan](https://www.mdmgeek.com/) - Tech Blog on Master Data Management And Every Buzz Surrounding It -- [Clare Corthell](https://datasciencemasters.org/) - The Open Source Data Science Masters -- [Paul Miller](https://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. -- [Data Science London](https://datasciencelondon.org/) Data Science London is a non-profit organization dedicated to the free, open, dissemination of data science. - We are the largest data science community in Europe. - We are more than 3,190 data scientists and data geeks in our community. +- [Clare Corthell](http://datasciencemasters.org/) - The Open Source Data Science Masters - [Datawrangling](http://www.datawrangling.org) by Peter Skomoroch. MACHINE LEARNING, DATA MINING, AND MORE - [Quora Data Science](https://www.quora.com/topic/Data-Science) - Data Science Questions and Answers from experts - [Siah](https://openresearch.wordpress.com/) a PhD student at Berkeley @@ -742,7 +725,7 @@ This section includes some additional reading material, channels to watch, and t - [Spenczar](https://spenczar.com/) a data scientist at _Twitch_. I handle the whole data pipeline, from tracking to model-building to reporting. - [KD Nuggets](https://www.kdnuggets.com/) Data Mining, Analytics, Big Data, Data, Science not a blog a portal - [Meta Brown](https://www.metabrown.com/blog/) - Personal Blog -- [Data Scientist](https://datascientists.net/) is building the data scientist culture. +- [Data Scientist](https://datascientists.com/) is building the data scientist culture. - [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. - [Tevfik Kosar](https://magnus-notitia.blogspot.com/) - Magnus Notitia - [New Data Scientist](https://newdatascientist.blogspot.com/) How a Social Scientist Jumps into the World of Big Data @@ -751,16 +734,13 @@ This section includes some additional reading material, channels to watch, and t - [Kaggle Past Solutions](https://www.chioka.in/kaggle-competition-solutions/) - [DataScientistJourney](https://datascientistjourney.wordpress.com/category/data-science/) - [NYC Taxi Visualization Blog](https://chriswhong.github.io/nyctaxi/) -- [Learning Lover](https://learninglover.com/blog/) -- [Dataists](https://www.dataists.com/) - [Data-Mania](https://www.data-mania.com/) - [Data-Magnum](https://data-magnum.com/) -- [P-value](https://www.p-value.info/) - Musings on data science, machine learning, and stats. - [datascopeanalytics](https://datascopeanalytics.com/blog/) - [Digital transformation](https://tarrysingh.com/) - [datascientistjourney](https://datascientistjourney.wordpress.com/category/data-science/) - [Data Mania Blog](https://www.data-mania.com/blog/) - [The File Drawer](https://chris-said.io/) - Chris Said's science blog -- [Emilio Ferrara's web page](https://www.emilio.ferrara.name/) +- [Emilio Ferrara's web page](http://www.emilio.ferrara.name/) - [DataNews](https://datanews.tumblr.com/) - [Reddit TextMining](https://www.reddit.com/r/textdatamining/) - [Periscopic](https://periscopic.com/#!/news) @@ -769,7 +749,6 @@ This section includes some additional reading material, channels to watch, and t - [Data Science Lab](https://datasciencelab.wordpress.com/) - [Meaning of](https://www.kennybastani.com/) - [Adventures in Data Land](https://blog.smola.org) -- [DATA MINERS BLOG](https://blog.data-miners.com/) - [Dataclysm](https://theblog.okcupid.com/) - [FlowingData](https://flowingdata.com/) - Visualization and Statistics - [Calculated Risk](https://www.calculatedriskblog.com/) @@ -790,7 +769,6 @@ This section includes some additional reading material, channels to watch, and t - [floydhub](https://blog.floydhub.com/introduction-to-genetic-algorithms/) - Blog for Evolutionary Algorithms - [Jingles](https://jinglescode.github.io/) - Review and extract key concepts from academic papers - [nbshare](https://www.nbshare.io/notebooks/data-science/) - Data Science notebooks -- [Deep and Shallow](https://deep-and-shallow.com/) - All things Deep and Shallow in Data Science - [Loic Tetrel](https://ltetrel.github.io/) - Data science blog - [Chip Huyen's Blog](https://huyenchip.com/blog/) - ML Engineering, MLOps, and the use of ML in startups - [Maria Khalusova](https://www.mariakhalusova.com/) - Data science blog @@ -819,9 +797,7 @@ This section includes some additional reading material, channels to watch, and t - [AI at Home](https://podcasts.apple.com/us/podcast/data-science-at-home/id1069871378) - [AI Today](https://www.cognilytica.com/aitoday/) - [Adversarial Learning](https://adversariallearning.com/) -- [Becoming a Data Scientist](https://www.becomingadatascientist.com/category/podcast/) - [Chai time Data Science](https://www.youtube.com/playlist?list=PLLvvXm0q8zUbiNdoIazGzlENMXvZ9bd3x) -- [Data Crunch](https://datacrunchcorp.com/data-crunch-podcast/) - [Data Engineering Podcast](https://www.dataengineeringpodcast.com/) - [Data Science at Home](https://datascienceathome.com/) - [Data Science Mixer](https://community.alteryx.com/t5/Data-Science-Mixer/bg-p/mixer) @@ -836,13 +812,11 @@ This section includes some additional reading material, channels to watch, and t - [Linear Digressions](https://lineardigressions.com/) - [Not So Standard Deviations](https://nssdeviations.com/) - [O'Reilly Data Show Podcast](https://www.oreilly.com/radar/topics/oreilly-data-show-podcast/) -- [Partially Derivative](https://partiallyderivative.com/) +- [Partially Derivative](http://partiallyderivative.com/) - [Superdatascience](https://www.superdatascience.com/podcast/) - [The Data Engineering Show](https://www.dataengineeringshow.com/) - [The Radical AI Podcast](https://www.radicalai.org/) -- [The Robot Brains Podcast](https://www.therobotbrains.ai/) - [What's The Point](https://fivethirtyeight.com/tag/whats-the-point/) -- [How AI Built This](https://how-ai-built-this.captivate.fm/) - [The Analytics Engineering Podcast](https://roundup.getdbt.com/s/the-analytics-engineering-podcast) ### YouTube Videos & Channels @@ -1003,7 +977,6 @@ Below are some Social Media links. Connect with other data scientists! [top](#awesome-data-science) - [DataTalks.Club](https://datatalks.club) -- [Women Who Code - Data Science](https://www.womenwhocode.com/datascience) ### GitHub Groups - [Berkeley Institute for Data Science](https://github.com/BIDS) @@ -1038,10 +1011,10 @@ Some data mining competition platforms | [](https://i.imgur.com/xLY3XZn.jpg) | By [Data Science Central](https://www.datasciencecentral.com/) | | [](https://i.imgur.com/0TydZ4M.png) | Data Science Wars: R vs Python | | [](https://i.imgur.com/HnRwlce.png) | How to select statistical or machine learning techniques | -| [](https://scikit-learn.org/stable/_static/ml_map.png) | Choosing the Right Estimator | +| [](https://scikit-learn.org/1.5/_downloads/b82bf6cd7438a351f19fac60fbc0d927/ml_map.svg) | [Choosing the Right Estimator](https://scikit-learn.org/1.5/machine_learning_map.html#choosing-the-right-estimator) | | [](https://i.imgur.com/uEqMwZa.png) | The Data Science Industry: Who Does What | | [](https://i.imgur.com/RsHqY84.png) | Data Science ~~Venn~~ Euler Diagram | -| [](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 | +| [](https://www.springboard.com/blog/wp-content/uploads/2016/03/20160324_springboard_vennDiagram.png) | Different Data Science Skills and Roles from [Springboard](https://www.springboard.com) | | [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/). | ### Datasets @@ -1083,14 +1056,13 @@ Some data mining competition platforms - [Open Data Sources](https://github.com/datasciencemasters/data) - [UNICEF Data](https://data.unicef.org/) - [undata](https://data.un.org/) -- [NASA SocioEconomic Data and Applications Center - SEDAC](https://sedac.ciesin.columbia.edu/) +- [NASA SocioEconomic Data and Applications Center - SEDAC](https://earthdata.nasa.gov/centers/sedac-daac) - [The GDELT Project](https://www.gdeltproject.org/) - [Sweden, Statistics](https://www.scb.se/en/) - [StackExchange Data Explorer](https://data.stackexchange.com) - an open source tool for running arbitrary queries against public data from the Stack Exchange network. -- [SocialGrep](https://socialgrep.com/datasets) - a collection of open Reddit datasets. - [San Fransisco Government Open Data](https://datasf.org/opendata/) - [IBM Asset Dataset](https://developer.ibm.com/exchanges/data/) -- [Open data Index](https://index.okfn.org/) +- [Open data Index](http://index.okfn.org/) - [Public Git Archive](https://github.com/src-d/datasets/tree/master/PublicGitArchive) - [GHTorrent](https://ghtorrent.org/) - [Microsoft Research Open Data](https://msropendata.com/)