How do we learn how to learn fast?

I can mostly only speak for myself, and even then, I know I'm not the fastest learner. But some principles come to mind, which appear to have been battle-tested.

1. The first is to Build a project portfolio.
2. The second is to Learn like Feynman.
3. The third is to Learn adjacent topics, rather than distant ones.

What are the end goals of research data science?

One of them is being able to explain the world, ideally in a causal fashion. (See: The goal of scientific model building is high explanatory power)

The kind of data scientist we need for this kind of work is different from that of business data science (see: The end goals of business data science).

The context in which research data science operates is one where business processes (and their corresponding business outcomes) are oftentimes not well defined. Thus, defining the ROI is a less straightforward task than it might otherwise be. As such, we need to view research data science as an investment for the future, just like any research organization is.

Also a thing to read: Researchers think mechanistically about the world

This was inspired by my participation in the TAO Data Science Panel.

I'm starting to see a bifurcation in research vs business data science.

• Research vs Business Data Science

How this translates to training needs and hiring

• Researchers think mechanistically about the world
• Learn how to learn fast
• Operate outside your pay grade
• Data scientists should know the data generating process behind their data
• Data scientists should learn how to write good code
• Avoiding technical debt with ML pipelines

And notes for managing data scientists:

• How to motivate your problem solvers