Useful learning resources

Here are some great learning resources that I’ve found helpful.

Bold means highly recommended.

• Data Science

  • Machine Learning

  • Causal Inference

  • Numerical Modelling

• Software Engineering

Data Science

• Foundations

  • Computational and Inferential Thinking: The Foundations of Data Science, Ani Adhikari and John DeNero, Data 8: Foundations of Data Science course, UC Berkeley.

    • Video lectures.

  • Python Data Science Handbook, Jake VanderPlas, 2016.

• Experiments:

  • Trustworthy Online Controlled Experiments : A Practical Guide to A/B Testing, Ron Kohavi, Diane Tang, and Ya Xu.

Machine Learning

If you’re interested in jobs in this area, I highly recommend Workera to help figure out what the roles are, what you’re suited to, what you need to improve on, and personalised plans to make this progress.

Machine Learning

• Machine learning, Coursera, Andrew Ng.

  • Video lectures, CS229, Standford University.

• Machine Learning for Intelligent Systems, Kilian Weinberger, 2018.

  • CS4780, Cornell: Video lectures.

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, Aurélien Géron, 2019, O’Reilly Media, Inc.

  • Jupyter Notebooks

Deep Learning

• Deep Learning Specialization, Coursera, DeepLearning.AI.

  • Video lectures, CS230, Stanford University.

  • Syllabus, CS230, Stanford University.

• NYU Deep Learning, Yann LeCun and Alfredo Canziani, NYU, 2021.

  • Video lectures.

• Physics-based Deep Learning, Nils Thuerey, Philipp Holl, Maximilian Mueller, Patrick Schnell, Felix Trost, Kiwon Um, 2021.

Artificial Intelligence

• Artificial Intelligence: A Modern Approach, 4th edition, Stuart Russell and Peter Norvig, 2021, Pearson.

• Artificial Intelligence: Principles and Techniques, Percy Liang and Dorsa Sadigh, CS221, Standord, 2019.

Maths

• Linear Algebra, Gilbert Strang, MIT 18.06, 2005.

• Essence of linear algebra, 3Blue1Brown.

• Essence of calculus, 3Blue1Brown.

• Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Gilbert Strang, MIT 18.065, 2018.

MLOps / ML Engineering

• Machine Learning Engineering for Production (MLOps) Specialization, Coursera, DeepLearning.AI.

• Production Machine Learning Systems, Google Cloud, Coursera.

• Full Stack Data Science.

• Designing Machine Learning Systems, Chip Huyen, 2022.

• Effective Data Science Infrastructure, Ville Tuulos, 2022.

Applications

• Machine Learning for Healthcare, MIT 6.S897, David Sontag and Peter Szolovits, 2019.

• Artificial Intelligence for Earth System Science (AI4ESS) Summer School, 2020, National Center for Atmospheric Research.

• Artificial Intelligence (AI) for Earth Monitoring, EUMETSAT, ECMWF, MOi, EEA, Copernicus.

Misc.

• MLExpert.

• Machine Learning Yearning, Andrew Ng.

Causal Inference

• Causal Diagrams: Draw Your Assumptions Before Your Conclusions, Miguel Hernan, Harvard University.

• Introduction to Causal Inference, Brady Neal.

  • Video lectures.

Numerical Modelling

• Engineering Mathematics, University of Washignton, Mechanical Engineering 564 and 565.

• Atmospheric science:

  • Overview of air pollution and climate change

  • Art of Climate Modeling, Paul Ullrich, UC Davis.

Software Engineering

If you’re interested in jobs in this area, I highly recommend Teach Yourself Computer Science by Oz Nova and Myles Byrne. This will help navigate the key topics and best resources. Many of the resources below are directly taken from this great guide.

Programming

Python

• Composing Programs, John DeNero, 61A course, UC Berkeley.

  • Video lectures.

• Practical Python Programming, David Beazley.

• Python Distilled, David Beazley, 2021.

• The Pragmatic Programmer, David Thomas and Andrew Hunt, 2019.

Algorithms and Data Structures

• Introduction to Algorithms, Srini Devadas and Erik Demaine, MIT 6.006, 2011.

• AlgoExpert.

Distributed Systems

• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, O’Reilly Media, Inc., 2017.

• Distributed Systems, MIT 6.824, Robert Morris, 2020.

High Performance Computing

• High Performance Computing Course - Advanced Scientific Computing, Prof Ing Morris Riedel, University of Iceland.

Cloud Computing

• AWS Cloud Technical Essentials, AWS, Coursera.

• Microsoft Azure Fundamentals (AZ-900), Adam Marczak.

Testing

• Software Testing Fundamentals

• Unit Testing Principles, Practices, and Patterns, Vladimir Khorikov, 2020.

Containers

• Python on Docker Production Handbook, Itamar Turner-Trauring.

• Kubernetes Basics.

Refactoring

• Refactoring, Refactoring Guru.

Design Patterns

• Design Patterns, Refactoring Guru.

Misc.

• Python Packages, Tomas Beuzen & Tiffany Timbers, 2021.

• Modern Python Developer’s Toolkit, Sebastian Witowski, PyCon US 2020.

• Research Software Engineering with Python, The Alan Turing Institute.