Davidyakobovitch Data Science Resources Save

📊 Data Science Resources, Data Science Standards & Machine Learning Pipelines

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Data Science Resources

The following are python, statistics, and machine learning recommended resources, to assist you during your data science and machine learning studies. If you are searching for public workshops I have participated in, they are now organized under the dyakobovitch-talks organization.

Essentials LinksPre Data ScienceVersion ControlData EngineeringStatisticsData SciencePython DocsCode ChallengesInterview PrepResearchAI DemosWorkspace

  :small_orange_diamond: Data Science Standards - How to Operationalize Machine Learning Pipelines
  :small_orange_diamond: HumAIn Podcast - Human Centered Thought Leadership Podcast
  :small_orange_diamond: Open Source Data Sets - Data Lakes, APIs & Sets for Machine Learning
  :small_orange_diamond: Developer Roadmap - Tools to Excel in Tech

Pre Data Science

  :small_orange_diamond: How to Think Like a Computer Scientist - Open source for Scientific Computing
  :small_orange_diamond: Python Fundamentals - Rick Halterman PhD
  :small_orange_diamond: Tableau - Big Book of Dashboards
  :small_orange_diamond: Open Source CS - Free Textbooks

Version Control

  :small_orange_diamond: Software Carpentry - Open source for Scientific Computing
  :small_orange_diamond: Git Book - Official Git Documentation
  :small_orange_diamond: Github Lab - Learn Git on Github
  :small_orange_diamond: Git on Toptal - Best Practices for Git
  :small_orange_diamond: Toptal - Tips - Advanced Topics
  :small_orange_diamond: Atlassian - Version Control Topics

Data Engineering

  :small_orange_diamond: SQL Cookbook - Learn Essentials of SQL Syntax
  :small_orange_diamond: W3 SQL, Mode SQL, SQL Zoo
  :small_orange_diamond: Seven Databases in Seven Week - Databases for Big Data
  :small_orange_diamond: High Performance MySQL - Executing Fast Queries
  :small_orange_diamond: REST APIs with Django - Working with Applications
  :small_orange_diamond: Design Data-Intensive Applications - System Design for Data
  :small_orange_diamond: Advanced Analytics with Spark - Perform KPI Analysis


  :small_orange_diamond: Intro to Stats Learning (Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani)
  :small_orange_diamond: Fundamental Statistics (Bryan R. Burnham)
  :small_orange_diamond: Online Stats (Rice University, University of Houston, Tufts University)
  :small_orange_diamond: Practical Statistics (Peter Bruce, Andrew Bruce)
  :small_orange_diamond: Linear Algebra and Applications:An Inquiry-Based Approach (Feryal Alayont, Steven Schlicker)
  :small_orange_diamond: Statistics in a Nutshell (Sarah Boslaugh)
  :small_orange_diamond: All in One Math Cheat Sheet (Alex Spartalas)
  :small_orange_diamond: Math for Computer Science (Eric Lehman, F Thomson Leighton, Albert R Meyer)
  :small_orange_diamond: Math for Machine Learning (Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong)

Data Science

  :small_orange_diamond: Python for Data Analysis and Github (Wes McKinney)
  :small_orange_diamond: Feature Engineering and Github (Sinan Ozdemir, Divya Susarla)
  :small_orange_diamond: Hands on Machine Learning and Github (Aurélien Geron)
  :small_orange_diamond: Python Data Science Handbook and Github (Jake VanderPlas)
  :small_orange_diamond: Introduction to Machine Learning and Github (Andreas C. Müller and Sarah Guido)
  :small_orange_diamond: Introduction to ML/Deep Learning and Github (Sebastian Raschka, Vahid Mirjalili)
  :small_orange_diamond: Data Science 100 - Berkeley and Website (UCBerkeley)
  :small_orange_diamond: Interpretable Machine Learning (Christoph Molnar)
  :small_orange_diamond: Deep Learning with Python and Github (Francois Chollet)
  :small_orange_diamond: Tensorflow Deep Learning Cookbook and Github (Antonio Gulli, Amita Kapoor)
  :small_orange_diamond: Image Processing - Python and Github (Michael Beyeler)
  :small_orange_diamond: Deep Learning and Code (Ian Goodfellow)
  :small_orange_diamond: Data Science Yearning - Draft (Andrew Ng)

Python Docs

  :small_orange_diamond: Pandas - Stable Docs
  :small_orange_diamond: Matplotlib - 3.3.3 Docs
  :small_orange_diamond: Sci-kit Learn - Docs
  :small_orange_diamond: Scipy and Numpy - Docs

Code Challenges

  :small_orange_diamond: Code Wars - Programming challenges that are progressively harder.
  :small_orange_diamond: Leetcode - Progressively more challenging questions
  :small_orange_diamond: Hacker Rank - Ranked challenges
  :small_orange_diamond: Project Euler - Weekly released challenges
  :small_orange_diamond: Kaggle - Data Science competitions
  :small_orange_diamond: CoderByte - Code challenges from FAANG companies

Interview Prep

  :small_orange_diamond: Tech Interview Handbook - Best Practices for technical interviews
  :small_orange_diamond: Big O Notation
  :small_orange_diamond: Cracking the Data Science Interview - Recommendations for interviews
  :small_orange_diamond: Cracking the Code Interview - Frequent Code Interview Questions
  :small_orange_diamond: Springboard - Data Science Interview Lessons
  :small_orange_diamond: How to - Prepare for Interviews
  :small_orange_diamond: Top 100 - DS Practice Questions
  :small_orange_diamond: Numpy Q&A Part I and Numpy Q&A Part II
  :small_orange_diamond: Pandas Q&A
  :small_orange_diamond: ML Code 100 Days of Practice
  :small_orange_diamond: Data Analyst 100 Questions
  :small_orange_diamond: Interview Cake FAANG Practice Questions
  :small_orange_diamond: Markdown - Framework for Formatting Text


  :small_orange_diamond: State of the Art - ML Papers w/ Code
  :small_orange_diamond: Arxiv Reports ArXiv Sanity
  :small_orange_diamond: Arxiv Adaptive Learning ArXiv Sanity
  :small_orange_diamond: ML Resources - For all Programming Languages
  :small_orange_diamond: Top Papers - ArxiV Curator Across Domains
  :small_orange_diamond: Binder and Gist - Sharing Code Resources
  :small_orange_diamond: Top Papers - ArxiV Curator Across Domains
  :small_orange_diamond: Awful AI - Ethically Questionable Use Cases
  :small_orange_diamond: Humane Principles - Design Elements for Applications
  :small_orange_diamond: DS Blogs - Curated Top Blogs on Data Science
  :small_orange_diamond: Ctrl Shift Face - Deep Fake Videos
  :small_orange_diamond: This Person Does Not Exist - Style GAN
  :small_orange_diamond: AI Portraits - Create Your own
  :small_orange_diamond: Tiobe - Most Popular Programming Languages
  :small_orange_diamond: PAIR - People + AI Guidebook (Google)
  :small_orange_diamond: LabML - Daily Research Papers
  :small_orange_diamond: Better Images of AI - Open Source Images

AI Demos

  :small_orange_diamond: TensorFlow Playground - Neural Networks Demo
  :small_orange_diamond: What if Tool - Google-backe research
  :small_orange_diamond: DBScan Demo and KMeans Demo
  :small_orange_diamond: NLP Text Transformer - OpenAI
  :small_orange_diamond: NLP Text Transformer - Demo
  :small_orange_diamond: Auto Text Detect - MIT/IBM/Harvard
  :small_orange_diamond: Grover - Neural Fake News Detector
  :small_orange_diamond: Thing Translator (Google)
  :small_orange_diamond: Sound Maker (Google)
  :small_orange_diamond: AI Duet Piano (Google)
  :small_orange_diamond: Giorgio Picture Cam (Google)
  :small_orange_diamond: Infinite Drum Machine (Google)
  :small_orange_diamond: Magenta Demos (Google)
  :small_orange_diamond: Self-Driving - MIT Demo
  :small_orange_diamond: GANs - Demo
  :small_orange_diamond: GAN Paint - Studio
  :small_orange_diamond: Deep Learning - Browser Experiences
  :small_orange_diamond: exBert - Interactive Bert Demo
  :small_orange_diamond: GANs Demo - Beautiful Fake Faces
  :small_orange_diamond: Deepfake Detector - MIT
  :small_orange_diamond: This Word Does Not Exist - Tranformer Demo


  :small_orange_diamond: Dual Arm Monitor
  :small_orange_diamond: LED Mountable Monitor
  :small_orange_diamond: Standing Desk


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To the extent possible under law, David Yakobovitch has licensed this work under Creative Commons, 4.0-NC-ND. This license is the most restrictive of Creative Commons six main licenses, only allowing others to download your works and share them with others as long as they credit the author, but they can’t change them in any way or use them commercially.

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