[czep]

To address some of the skepticism about when and where javascript would be appropriate in data science, would you want to fit a logistic regression model in javascript? Probably not, but to build a solver that takes model outputs and visualizes the changes in predicted probabilities based on different combinations of variables? This is definitely where javascript would make sense. Visualization, dashboards, reporting, and exploratory analysis are all ripe domains for developing rich responsive UIs. Basically, any layer where you have a data-to-human interface can be leveraged with javascript.

There is a lot of great work happening in this space already. In the R world for example, shiny makes heavy use of js to the point that you often can't tell where R code ends and javascript begins. Plotly's Dash provides bindings for R, Python, and Julia. Personally, as a data scientist, I have been excitedly learning React because it really rips the landscape wide open for all the use cases I mentioned above. It then makes sense to have libraries that give JS users a good data model and can do most of the same numerical computation that we'd be doing in other languages. Again, you probabaly don't want to do serious numerical work in js, but remember people said that about Python ten years ago too.

I love the framing of this book, because I want more data scientists to start thinking about the presentation of data and spark some bits of ingenuity to make datasets and model outputs accessible to non-data scientists. Data scientists should be the ones writing the tools that interface data with humans because of their domain knowledge. But this is a different skillset and usually the work of SW engineers. Of course engineers can also have great data intuition too, but I really do encourage data scientists to develop their front end skills, it's well worth it.