Recommended Courses

Here are some recommended online courses for various topics in research computing.

They are useful complements to your studies and the various Training Courses we provide here.

Contents

• Software Engineering

  • Programming

    • Python

    • OpenMP

  • Algorithms and Data Structures

  • Distributed Systems

  • Cloud Computing

  • Testing

  • Research Software Engineering

  • Packaging

  • Version Control

• Data Science

  • Foundations

  • Causal Inference

  • Applied Maths

  • Statistics

• Machine Learning

  • Machine Learning

  • Deep Learning

  • Maths for Machine Learning

  • MLOps

  • Applications

• Numerical Modelling

  • Weather and Climate

Software Engineering

Programming

Python

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

  • Video lectures.

• Python Distilled, David Beazley, 2021.

  • Course

OpenMP

• Introduction to OpenMP from Cornell University

• Advanced OpenMP from EPCC

• Programming your GPU with OpenMP containing slides and exercises, from Bristol University, presented at SC22

• Intro to GPU Programming with the OpenMP API

• OpenMP in Nvidia’s HPC Compilers

Algorithms and Data Structures

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

Distributed Systems

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

Cloud Computing

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

Testing

• Software Testing Fundamentals

Research Software Engineering

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

Packaging

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

Version Control

• First Week on GitHub, GitHub Learning Lab.

Data Science

Foundations

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

  • Video lectures.

• The Turing Way Handbook of Data Science

Causal Inference

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

• Introduction to Causal Inference, Brady Neal.

  • Video lectures.

Applied Maths

• Engineering Mathematics (ME564 and ME565), Steve Brunton, University of Washington.

Statistics

• Statistical Rethinking, Richard McElreath.

  • Video lectures

Machine Learning

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.

Maths for Machine Learning

• 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

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

Applications

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

Numerical Modelling

Weather and Climate

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