I really enjoyed this post from a data science outsider. The author is a mobile engineer working on Android, primarily using Java and chronicles a year-long stint working to implement some ML features within a mobile app.
I enjoyed the total outsider’s perspective—the author is technically quite competent, and it’s neat to see what he finds hard and what he finds easy. One of the more interesting things to me was how important language choice is in data science:
I experienced other challenges too, one of them was frequent: translating the Python solutions to Java. Since Python already has built in support for data science tasks, the code felt more concise in Python. I remember pulling my hair out when I tried to literally translate a command: scaling a 2D array and adding it as a transparent layer to an image. We finally got it to work and everyone was excited.
Industry people all know much of a default Python has become, but it can create challenges for outsiders who typically view Python as a second-class citizen (justifiably so in many contexts!). There’s no reason why the author would ever use Python in his day job, and his hesitance to want to learn the language at all was an interesting juxtaposition with how dominant it is in our world.