I’ve known people who built lucrative careers on expertise in specialised technologies, and others who’s entire careers disappeared because they were too specialised and the technology got deprecated. You might be fine, but I’d definitely recommend diversifying your skill set a bit. Being a generalist has served me well.

the libraries have grown nicely over the years, though I am surprised they still lack some basic stuff.

why in 2019 is there no good way to visualize a decision tree? having to install, configure, and get working graphviz feels very hoaky.

I prefer python, but R still does some things really well that the python libraries are just not up to par

1) anything geospatial, including drawing maps. here is a list of projects my students did to give examples: https://pennmusa.github.io/MUSA_801.io/

2) time series

3) linear models, is it so hard to give me a good summary?

If anyone knows of any packages that do these >= R, I'd love to see them :)

I came in to the pydata library after extended time in Matlab/Octave. Learning there first was terrible for programming basics (e.g. polymorphism) but was excellent for ensuring I knew what a given algorithm does/should do.

I highly recommend spending some time in C/C++, Go, or Julia to pydata-first folks that ask.

As a data scientist myself, I learned to write code that utilized arrays and matrices - from the most basic library. From cleaning to analysis to machine learning (I supposed numpy was used for ML). Is this the most correct manner to write code? I don't know but learning to do DS without specific Python libraries has improved my coding ability. Aside from being able to read in different data formats I believe I could do a fair portion of my work in C.

What is the pydata library set? numpy, pandas, matplotlib, sklearn and the like? My google fu is failing me...