README.md

This repository contains various sources of open-source reading and reference material to get jump started on different areas of work that we are interested in.

Table of Contents

• Table of Contents
• Introductory Courses
• Data Science
  • Fundamentals
  • Statistics
  • Data Visualization
• Machine Learning
• Deep Learning
• Software Engineering
• Academic Writing
• Miscellaneous
• Acknowledgements

Introductory Courses

• Math primer: https://aman.ai/primers/math/
• General-purpose AI: https://microsoft.github.io/AI-For-Beginners/
• ML: https://microsoft.github.io/ML-For-Beginners/
  • Backprop primer: https://aman.ai/primers/backprop/
• Data science: https://pub.towardsai.net/simple-but-effective-free-roadmap-to-start-a-career-in-data-science-ai-in-2023-9d17c76a184b
• Data analytics: https://levelup.gitconnected.com/comprehensive-data-analytics-roadmap-for-2023-with-free-resources-adfefc0d0d7f
• Roadmaps of interest
  • Python: https://roadmap.sh/python
  • AI and Data scientist: https://roadmap.sh/ai-data-scientist

Data Science

Fundamentals

• Fundamental Numerical Methods and Data Analysis - George W. Collins - Beginner
• Introduction to Metadata - Murtha Baca - Beginner
• Modeling with Data: Tools and Techniques for Scientific Computing - Ben Klemens - Beginner
• Foundations of Data Science - Avrim Blum, John Hopcroft, and Ravindran Kannan - Intermediate

Statistics

• Think Stats - Allen B. Downey - Beginner
• Introduction to Probability - Charles M. Grinstead and J. Laurie Snell - Beginner
• Introduction to Statistical Thought - Michael Lavine - Beginner
• OpenIntro Statistics - Second Edition - David M. Diez, Christopher D. Barr, and Mine Cetinkaya-Rundel - Beginner
• Applied Data Science - Ian Langmore and Daniel Krasner - Intermediate
• Concepts and Applications of Inferential Statistics - Richard Lowry - Beginner
• Forecasting: Principles and Practice - Rob J. Hyndman and George Athanasopoulos - Intermediate

Data Visualization

• Interactive Data Visualization for the Web - Scott Murray - Beginner
• Plotting and Visualization in Python - Beginner
• ggplot2: Elegant Graphics for Data Analysis - Hadley Wickham - Beginner

Machine Learning

• A Course in Machine Learning - Hal Daume - Beginner
• A First Encounter with Machine Learning - Max Welling - Beginner
• Machine Learning Yearning - Andrew Ng. - Beginner
• Introduction to Machine Learning - Alex Smola and S.V.N. Vishwanathan - Intermediate
• Probabilistic Programming & Bayesian Methods for Hackers - Cam Davidson-Pilon (main author) - Intermediate
• The LION Way: Machine Learning plus Intelligent Optimization - Robert Battiti and Mauro Brunato - Intermediate
• ML Systems with TinyML - Prof. Vijay Janapa Reddi - Intermediate

Deep Learning

• Understanding Deep Learning: Collection of notes and resources for a graduate course on deep learning. - Beginner and Intermediate
• Deep Learning Book: An MIT Press book by Ian Goodfellow, Yoshua Bengio and Aaron Courville. - Beginner and Intermediate
• Deep Learning - Ian Goodfellow, Yoshua Bengio and Aaron Courville - Intermediate

Software Engineering

• Documentation - Beginner
• AI for software development - Intermediate

Academic Writing

• Ten simple rules for structuring papers - Beginner
• The Structure of An Academic Paper - Beginner
• How to write a first-class paper - Beginner
• Structuring Paragraphs - Beginner
• Writing an Abstract - Beginner
• Academic Phrasebank - Beginner
• Essay and dissertation writing skills - Intermediate
• Setting up a local Latex editor for Overleaf projects - Intermediate:
  1. Ensure that the owner of the Overleaf project has a premium subscription, because we need the Git integration feature.
  2. Once the new project is created, follow this guide to ensure that the project is linked to a GitHub repository. It is recommended to keep the repository private.
  3. Set up a Latex compiler on your local machine. For Windows, MikTex is recommended. For Linux, TexLive is recommended.
  4. Set up an editor. For example, you can follow these instructions for VS Code.
  5. Use the Git integration feature to clone the repository to your local machine.
  6. Make changes to the Latex files on your local machine and push them to the repository. The changes will be reflected on the Overleaf project once someone pulls the changes from the repository.
  7. Anyone with write access to the Overleaf project can push the changes to the repository from the Overleaf project itself. Once you "Pull" the changes one VS Code, this will update the local repository on your machine. References for using Git with VS Code can be found here.

Miscellaneous

• Intro to Latex and Overleaf - Beginner
• Introduction to reproducibility in cancer informatics - Beginner
• Advanced reproducibility - Intermediate

Acknowledgements

• https://github.com/chaconnewu/free-data-science-books
• https://www.itcrtraining.org/
• https://github.com/chinasatokolo/academic_writing