Data Science Days

The objective of Data Science Days is to make comprehensive and high quality instructional materials easily accesible for anyone entering into the broad data science domain to gain in-depth knowledge:octocat:

List of Ebooks:rocket:

Python Programming

1. Learning with Python 3 – Peter Wentworth, Jeffrey Elkner, Allen B. Downey, and Chris Meyers
2. Problem Solving with Algorithms & Data Structures Using Python – Brad Miller and David Ranum
3. Elements of Data Science – Allen B. Downey
4. Computational & Inferential Thinking – Ani Adhikari and John DeNero
5. Python Data Science Handbook – Jake VanderPlas
6. Mining of Massive Datasets – Jure Leskovec, Anand Rajaraman, Jeff Ullman

R Programming

1. Hands-On Programming with R – Garrett Grolemund
2. Advanced R – Hadley Wickham
3. R for Data Science – Hadley Wickham, Garrett Grolemund
4. Introduction to Data Science: Data Analysis and Prediction Algorithms with R – Rafael A. Irizarry

Statistical Learning

1. A Course in Machine Learning – Hal Daumé III
2. The Hundred-Page Machine Learning Book – Andriy Burkov
3. Statistical Learning – Trevor Hastie, Robert Tibshirani
4. Mathematics for Machine Learning – Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
5. Understanding Machine Learning: From Theory to Algorithms – Shai Shalev-Shwartz, Shai Ben David
6. Foundations of Machine Learning – Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar

Probabilistic Learning

1. Pattern Recognition and Machine learning – Christopher Bishop
2. Causal Inference in Statistics: A Primer – Judea Pearl
3. Information Theory, Inference and Learning Algorithms – David J.C. Mackay
4. Gaussian Processes for Machine Learning – Carl Edward Rasmussen, Christopher K.I. Williams
5. Bayesian Reasoning and Machine Learning – David Barber
6. Bayesian Data Analysis – Andrew Gelman, Aki Vehtari

Machine Learning with Graphs

1. Network Science – Albert-Laszlo Barabasi
2. Networks, Crowds, and Markets – David Easley and Jon Kleinberg
3. Graph Representation Learning – William L Hamilton

Deep Learning and AI

1. Neural Networks and Deep Learning – Michael A. Nielsen
2. Deep Learning – Ian Goodfellow, Yoshua Bengio, Aaron Courville
3. Dive Into Deep Learning – Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola
4. Reinforcement Learning: An Introduction – Richard S Sutton
5. The Quest for Artificial Intelligence: A History of Ideas and Achievements – Nils J. Nilsson

Natural Language Processing

1. Natural Language Processing with Python – Steven Bird, Ewan Klein, and Edward Loper
2. Introduction to Natural Language Processing – Jacob Eisenstein
3. Speech and Language Processing – Dan Jurafsky and James H. Martin

Online Learning:rocket:

Free Courses

1. Fast.ai
2. freeCodeCamp
3. Seeing Theory
4. Elements of AI
5. Earth Data Science
6. Made With ML
7. Kaggle Tutorials
8. ML Crash Course
9. Advanced NLP with spaCy
10. Hugging Face course

YouTube Videos

• Statistics Fundamentals – StatQuest
• Machine Learning – StatQuest
• Machine Learning – Luis Serrano
• A 2020 Vision of Linear Algebra – Gilbert Strang
• MIT18.06 Linear Algebra – Gilbert Strang
• MIT18.065 Matrix Methods – Gilbert Strang
• Learn Differential Equations – Gilbert Strang
• Highlights of Calculus – Gilbert Strang
• Essence of Linear Algebra – 3Blue1Brown
• Essence of Calculus – 3Blue1Brown
• Differential Equations – 3Blue1Brown
• Neural Networks – 3Blue1Brown
• Data Structures and Algorithms – Gerry Jenkins
• Analysis of Algorithms – Steven Skiena
• Algorithms Illuminated – Tim Roughgarden Lectures
• MIT6.042J Mathematics for Computer Science – Albert R. Meyer
• Statistics 110: Probability – Joe Blitzstein

Open University Courses

• Caltech CS156: Learning From Data
• CMU Machine Learning – Tom Mitchell
• Stanford: NLP with Dan Jurafsky and Chris Manning
• Stanford CS229: Machine Learning
• Stanford CS230: Deep Learning
• Stanford CS221: AI Principles & Techniques
• Stanford CS224W: Machine Learning with Graphs
• Stanford CS231N: CNN for Visual Recognition
• Stanford CS224N: NLP with Deep Learning
• University of Tübingen: Statistical Machine Learning – Ulrike von Luxburg
• University of Tübingen: Probabilistic Machine Learning – Philipp Hennig
• CORNELL CS5780: Machine Learning for Intelligent Systems
• CORNELL CS5787: Applied Machine Learning
• MIT 6.S191: Introduction to Deep Learning
• UC Berkeley: Full Stack Deep Learning
• NYU DS-GA 1008: Deep Learning – Yann LeCun & Alfredo Canziani
• UCL: Introduction to Reinforcement Learning – David Silver
• GeorgiaTech MATH 1554: Linear Algebra – Greg Mayer
• GeorgiaTech ISYE 6739 - Probability and Statistics – Dave Goldsman
• Foundations of Machine Learning – David S. Rosenberg, Bloomberg ML EDU

Popular MOOCs:rocket:

Coursera

• Machine Learning – Andrew Ng, Stanford
• Probabilistic Graphical Models Specialization – Daphne Koller, Stanford
• Introduction to Mathematical Thinking – Keith Delvin, Stanford
• Algorithms Specialization – Tim Roughgarden, Stanford
• DeepLearning.AI Specializations
• Python for Everybody Specialization – Michigan
• Django for Everybody Specialization – Michigan
• Python 3 Programming Specialization – Michigan
• Applied Data Science with Python Specialization – Michigan
• Mathematics for Machine Learning Specialization – Imperial College
• Advanced Machine Learning Specialization – HSE University
• Learn SQL Basics for Data Science Specialization – UCDavis
• Building Cloud Computing Solutions at Scale Specialization – Duke
• Big Data Specialization – UC San Diego
• Functional Programming in Scala Specialization – Martin Odersky, EPFL
• IBM Data Science Professional Certificate
• Data Science with Databricks for Data Analysts Specialization
• AWS Cloud Practitioner Essentials
• Microsoft Azure Fundamentals AZ-900 Specialization
• Google Cloud Platform Fundamentals: Core Infrastructure

edX

• CS50's Introduction to Computer Science – HarvardX
• CS50's Web Programming with Python and JavaScript – HarvardX
• CS50's Introduction to Artificial Intelligence with Python – HarvardX
• Computational Thinking Using Python – MITx
• Statistics and Data Science MicroMasters Program – MITx
• Using Python for Research – HarvardX
• Reproducible Data Science – HarvardX
• Foundations of Data Science Professional Certificate – BerkeleyX
• Data Science MicroMasters Program – UCSanDiegoX
• Algorithms and Data Structures MicroMasters Program – UCSanDiegoX
• Cloud Computing MicroMasters Program – MarylandX
• Introduction to Databases MicroMasters Program – NYUx
• Fat Chance: Probability from the Ground Up – HarvardX
• Probability: Basic Concepts & Discrete Random Variables
• Probability: Distribution Models & Continuous Random Variables
• Introduction to Probability – HarvardX
• Computational Probability and Inference – MITx
• Single Variable Calculus – MIT18.01x
• Multivariable Calculus – MIT18.02x
• Differential Equations – MIT18.03x
• Calculus Applied – HarvardX
• Linear Algebra: Foundations to Frontiers (LAFF) – UTAustinX
• Advanced Linear Algebra: Foundations to Frontiers (ALAFF) – UTAustinX

Udacity

• Introduction to Python Programming – Juno Lee
• Intro to Statistics – Sebastian Thrun
• Intro to Data Science – Dave Holtz, Cheng-Han Lee
• Introduction to Machine Learning – Sebastian Thrun, Katie Malone
• Intro to Artificial Intelligence – Sebastian Thrun, Peter Norvig
• Intro to Deep Learning with Pytorch – Facebook AI
• Intro to Tensorflow for Deep Learning – Google
• Model Building and Validation – AT&T
• Design of Computer Programs – Peter Norvig
• Intro to Algorithms – Michael Littman
• Introduction to Graduate Algorithms – Georgia Tech
• Intro to Theoretical Computer Science – Sebastian Wernicke
• Intro to Descriptive Statistics – San Jose State University
• Intro to Inferential Statistics – San Jose State University
• Linear Algebra Refresher Course – Chris Pryby
• Differential Equations in Action – Jörn Loviscach, Miriam Swords Kalk
• SQL for Data Analysis – Derek Steer, Mode Analysis
• Data Wrangling with MongoDB – Shannon Bradshaw
• AWS Machine Learning Foundations
• Microsoft Azure AI Fundamentals
• Scholarships at Udacity

Khan Academy

• Statistics and Probability
• Precalculus
• Differential Calculus
• Integral Calculus
• Multivariable Calculus
• Differential Equations
• Linear Algebra

Open Resources:rocket:

Open Assessments

1. RealPython Quizzes
2. HackerRank Skills Certification
3. DataCamp Signal
4. Workera Data-AI Skills Assessments
5. Data Science Readiness – NotreDameX

Open Data Science Platforms

1. Kaggle Notebooks
2. Google Colab
3. Anaconda IE
4. Databricks CE

Open Data Science Projects

1. Kaggle Competitions
2. DrivenData Competitions
3. AIcrowd Challenges
4. Call for Code
5. OpenAI Projects
6. Google AI Experiments
7. Google Open Source
8. Microsoft AI Lab
9. Microsoft AI for Earth

Open Datasets

1. Kaggle Datasets
2. UCI Machine Learning Repository
3. Appen Curated List
4. AWS Open Data Registry