One of my colleagues (well, strictly speaking my boss' boss) recently crystallized a very important and key idea for my colleagues: the difference between biomedical research data science and tech business data science. I gave his ideas some thought, and decided to pen down what I saw as the biggest similarities and differences.

The goals between the two "forms" of data science are different:

• The end goals of business data science
• The end goals of research data science

There are issues that I'm seeing in the data science field. Some of the problems I have seen thus far.

• Model-focused enhancements are saturating
• Experimentation is getting a bit out of hand

And what I think is needed:

• The goal of scientific model building is high explanatory power
• The place for overparametrized models is tooling
• Finding the appropriate model to apply is key

The key difference, I think is that The end goals of business data science is about capturing value from existing processes, while The end goals of research data science is about expanding new avenues of value from unknown, un-developed, and un-captured business processes. The latter is and has always been an investment to make; in a well-oiled system, the former likely generates profit that can and should be invested in the latter.

Experiment with overparameteized models in domains that may not necessarily make sense, particularly from a first-principles perspective. One example: I have peer-reviewed a paper where 1-dimensional convolutional neural networks were unleashed on on tabular data: this makes no sense from a first-principles perspective, because in tabular data, there's usually no semantically meaningful spatial correlations between columns. These experiments, to the trained practitioner, leave the impression of the experimenter trying too hard to shoehorn a problem into a newly learned tool, with the model's assumptions not being sufficiently understood before being applied.

In the spirit of Finding the appropriate model to apply is key, I'd rather see models custom-built for each problem. After all, Every well-constructed model is leverage against a problem

News article on ScienceDaily. The original paper backing the news article is published in PNAS.

Quotables from the news article:

He also noted that new embedding methods are mostly being compared to other embedding methods. Recent empirical work by other researchers, however, shows that different techniques can give better results for specific tasks.

Benchmarks are quite important! See also: The craze with embeddings.

Given the growing influence of machine learning in our society, Seshadhri said it is important to investigate whether the underlying assumptions behind the models are valid.

Relates to the idea that Finding the appropriate model to apply is key. This is because Every well-constructed model is leverage against a problem; when the underlying assumptions behind our models are valid for our specific problem at hand, we gain leverage to solve our problems. (We should also keep keenly aware of When is your statistical model right vs. good enough.)

Why does mechanistic thinking matter? In The end goals of research data science, we are in pursuit of the invariants, i.e. knowledge that stands the test of time. (How our business contexts exploit that knowledge for win-win benefit of society and the business is a matter to discuss another day).

When we build models, particularly of natural systems, predictive power matters only in the context of explanatory power, where we can map phenomena of interest to key parameters in a model. For example, in an Autoregressive Hidden Markov Model, the autoregressive coefficient may correspond to a meaningful properly in our research context.

Being able to look at a natural system and find the most appropriate model for the system is a key skill for winning the trust of the non-quantitative researchers that we serve. (ref: Finding the appropriate model to apply is key)