Links

This page contains links to useful resources. *Please note that these lists are in no way exhaustive and are meant to provide a foundation from which to discover the massive amount of material out there and to do so in a digestible and manageable way. Keep checking back here as this is a fluid list and will evolve over time.

Happy exploring!

General

Apache Software Foundation
FigShare's 'The State of Open Data'
Data Science Weekly
Association for Computing Machinery (ACM)
The Python Graph Gallery
- In general, graph galleries can be very helpful to learn about data visualization techniques, quickly create new visualizations, and improve you understanding of the data
Open Scientist Handbook - a reference work you can use to become an open science change agent in your department, laboratory, college, learned society, or research agency

Books

The Elements of Statistical Learning
Data Science From Scratch
Python Data Science Handbook
Introduction to Machine Learning with Python
Computer Age Statistical Inference
Machine Learning - Tom Mitchell
Think like a data scientist: Tackle the data science process step-by-step
Developing Analytical talent: Becoming a data scientist
An introduction to statistical learning with applications in R
A simple introduction to Data Science: Book 2
Networking for Big Data
Python for data analysis
Big Data Management for Dummies, 2nd Informatica Special Edition
Neural Networks and Deep Learning
Deep Learning Cookbook: Practical recipes to get started quickly
Machine Learning with TensorFlow, Second Edition
(for absolute beginners) Model-Based Machine Learning, John Winn, Chris Bishop, et al. Bishop wrote the book on ML in the early 2000's (PRML); this is his latest book aimed at a non-expert audience, presented using a fun murder mystery narrative
(intro to deep learning) Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville. Has become the standard book on DL, by one of the "godfathers" of the field (Bengio). Very pragmatic view (aimed towards DL engineers)
(intro to probabilistic ML) Machine Learning: a Probabilistic Perspective, Kevin Murphy. Has come to replace PRML (the book on ML from the early 2000's). Murphy has been in the field forever.
(practical coding in DL) Dive into Deep Learning, Zack Lipton, Alex Smola, et al. An interactive deep learning book with code, math, and discussions; Provides NumPy and PyTorch iPython tutorials. Smola has been in the field forever, Zack is well known in the field
(maths foundations for ML) Mathematics for Machine Learning, Marc Deisenroth et al. Mathematical foundations needed to understand ML beyond just implementing stuff. Deisenroth has been around for quite some time as well (working mostly in MBRL actually)

Academic Journals

Learning Communities/Communities of Practice

The NASA Center for HelioAnalytics (or contact Ryan McGranaghan) - building a Community of Practice around an informatics/data science approach to Heliophysics science
The Earth Science Informatics Partners - one of the foremost leaders in promoting the collection, stewardship and use of Earth science (and related) data, information and knowledge that is responsive to societal needs
The AI Learning Salon - a weekly forum to explore bridges and contentions in biological and artificial learning
The Royal Institution - youtube series covering a wide range of science topics, including data and intelligence

Tutorials

Online courses

Learning From Data (Introductory Machine Learning) - California Institute of Technology
Santa Fe Institute's "Introduction to Open Science" course - the new language to understand open science principles and tools
CS109 Data Science - Harvard
Computational Probability and Inference - Massachusetts Institute of Technology
Data Science - Johns Hopkins
Machine Learning - Andrew Ng
Stanford CS231n: Convolutional Neural Networks for Visual Recognition
How to Process, Analyze and Visualize Data
Data Visualization with Python
Lynda tutorials -- this is an excellent resource, though requires a membership
Earth Data Science -- contains open tutorials and course materials covering topics including data integration, GIS and data intensive science (>250 Earth data science lessons)
Interpretability and Explainability in Machine Learning Harvard University
Wonderful example of online learning resource for data science topic: Elements of AI

Elements to look for in online courses

Focuses on practical skills. Those that are perhaps most wide-reaching include Python and R programming, Jupyter Notebooks, scikit-learn, TensorFlow and Keras, pandas, xarray
Provides quick, quality, and consistent feedback
Is free or inexpensive (paying for a course that is worthwhile is a good way to get yourself to commit to it!)
Is project-oriented
Contains an excellent social interaction component

Data Visualization resources

Compilations of resources

A fantastic set of links and resources is available on the HelioAnalytics website operated by Barbara Thompson and colleagues at the NASA Goddard Space Flight Center
Top Data Science Resources

Blogs

Podcasts

Ways to become active (i.e., the best way to learn)

Start working on open source projects (see links below)
Compete in a Kaggle competition
Join or start a Meetup and attend or host a Hackathon
Collaborate with a data scientist (e.g., find one at your university or work)
Reach out to a potential mentor
Take an online course
Explore your passions in a data-driven manner
'Lurk' - join community email lists or forums to gain exposure to the language before contributing more actively

Open source projects and links

Apache Software Foundation: Mission is to provide software for the public good
papers with code: Mission is to create a free and open resource with Machine Learning papers, code and evaluation tables
Python Scikit-Learn: Mission is to provide free and open machine learning library in the Python programming language
Go: Mission is to provide an open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to making use of data.

Specific topics

Explainable Artificial Intelligence (XAI)

DARPA XAI Program
- Here's a good overview of explainable AI methods that performers in DARPA's XAI program explored. It's a good retrospective on the entire program. Excellent reference list
The Ethics and Governance of AI (an MIT Media Lab Course)
Interpretability and Explainability in Machine Learning (a Harvard University course)
The Mythos of Model Interpretability
The AI Now Institute - an interdisciplinary research center dedicated to understanding the social implications of artificial intelligence
(from Mark Wronkiewicz) This is an aging blog post, but it helped me quite a bit when I was trying to get up to speed. It’s high level with good breadth (explains why you should care, name drops many important methods, and gives frameworks for mentally organizing those different methods). It’d also be good for the reference list

Frameworks for trustworthy, accountable, explainable systems (AI and other)

Findability, Accessibility, Interoperability, and Reusability (FAIR) Principles
A framework for model/system accountability: Algorithmic Impact Assessments

AI and ML applied to science

Scientific/research workflows

ASSET (Accelerating Scientific workflowS using EarthCube Technologies)

Open Science

NASA's Transform to Open Science (TOPS) - excellent compilation of resources
Santa Fe Institute's "Introduction to Open Science" course - the new language to understand open science principles and tools

The social component of data science - becoming transdisciplinary

Tools to improve virtual collaboration

Compilations of resources:

A fluid list from the NSF EarthCube community

Post-it Note-like boards and resources:

MilaNote
Whimsical
Kumu - very useful for quickly building networks and knowledge graphs
Miro - LOVE this - easy and free; powerful for post-it notes board
Mural
Smartsheet
Figma
Whova
Padlet - collaborative Super-powered Sticky Notes with Drawing
Pinup
Google Jamboard

Polling resources:

XLeap - excellent collective decision-making resource, but expensive
Menti
Sli.Do

Virtual Conference Tools:

General Interaction Tools:

Github - under-rated as a full-stack collaboration tool (even for writing papers and proposals, not just for software)
Slack
Discord - place for teams to 'hang out' and work
QiqoChat - Recommended by the Earth Science Information Partners group; a wrapper around Zoom (but other platforms are possible including Jitsi). Take virtual meetings to the next level and encourage engagement in a variety of ways, not just webinar-style watching
Whereby - like Zoom, but in many ways simpler, easier (no downloads or installs, same link each time); free for individual use