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:
- Learning with Python 3 – Peter Wentworth, Jeffrey Elkner, Allen B. Downey, and Chris Meyers
- Problem Solving with Algorithms & Data Structures Using Python – Brad Miller and David Ranum
- Elements of Data Science – Allen B. Downey
- Computational & Inferential Thinking – Ani Adhikari and John DeNero
- Python Data Science Handbook – Jake VanderPlas
- Mining of Massive Datasets – Jure Leskovec, Anand Rajaraman, Jeff Ullman
- Hands-On Programming with R – Garrett Grolemund
- Advanced R – Hadley Wickham
- R for Data Science – Hadley Wickham, Garrett Grolemund
- Introduction to Data Science: Data Analysis and Prediction Algorithms with R – Rafael A. Irizarry
- A Course in Machine Learning – Hal Daumé III
- The Hundred-Page Machine Learning Book – Andriy Burkov
- Statistical Learning – Trevor Hastie, Robert Tibshirani
- Mathematics for Machine Learning – Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
- Understanding Machine Learning: From Theory to Algorithms – Shai Shalev-Shwartz, Shai Ben David
- Foundations of Machine Learning – Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
- Pattern Recognition and Machine learning – Christopher Bishop
- Causal Inference in Statistics: A Primer – Judea Pearl
- Information Theory, Inference and Learning Algorithms – David J.C. Mackay
- Gaussian Processes for Machine Learning – Carl Edward Rasmussen, Christopher K.I. Williams
- Bayesian Reasoning and Machine Learning – David Barber
- Bayesian Data Analysis – Andrew Gelman, Aki Vehtari
- Network Science – Albert-Laszlo Barabasi
- Networks, Crowds, and Markets – David Easley and Jon Kleinberg
- Graph Representation Learning – William L Hamilton
- Neural Networks and Deep Learning – Michael A. Nielsen
- Deep Learning – Ian Goodfellow, Yoshua Bengio, Aaron Courville
- Dive Into Deep Learning – Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola
- Reinforcement Learning: An Introduction – Richard S Sutton
- The Quest for Artificial Intelligence: A History of Ideas and Achievements – Nils J. Nilsson
- Natural Language Processing with Python – Steven Bird, Ewan Klein, and Edward Loper
- Introduction to Natural Language Processing – Jacob Eisenstein
- Speech and Language Processing – Dan Jurafsky and James H. Martin
- Fast.ai
- freeCodeCamp
- Seeing Theory
- Elements of AI
- Earth Data Science
- Made With ML
- Kaggle Tutorials
- ML Crash Course
- Advanced NLP with spaCy
- Hugging Face course
- 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
- 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
- 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
- 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
- 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
- Statistics and Probability
- Precalculus
- Differential Calculus
- Integral Calculus
- Multivariable Calculus
- Differential Equations
- Linear Algebra
- RealPython Quizzes
- HackerRank Skills Certification
- DataCamp Signal
- Workera Data-AI Skills Assessments
- Data Science Readiness – NotreDameX
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