I decided to turn down looking for $100k+ data science jobs in the Bay Area to join Signal as an assistant instructor
I think looking for jobs as a data scientist would be a valuable experience for you, and "I turned down an offer from ____" and "I decided to not look" send very different signals. There's almost a month between now and the start of your next cohort; that should be plenty of time to see how far you'd get through the funnel.
Subscribe to RSS Feed
= f037147d6e6c911a85753b9abdedda8d)
This actually sounds about right.
I think that I care more about job-preparedness, potential for impact, and preparing people for being able to earn-to-give or do direct EA work. I think that Robert also cares about those things, which is why I liked his weekly interview sessions, as I mentioned above.
However, I didn't get the sense that Jonah, the instructor for the first cohort, really cared about these things quite as much. Jonah strikes me as an intelligent individual whose heart is in academia, rather than in data science or industry. This was quite problematic, because, among other reasons, it meant that even his explanations of grittier things were too focused on the big picture, and too spare on details for some people to figure out how to actually do the thing at all. It also skewed the distribution of topics taught away from things relevant to industry.
Could you please elaborate with specific examples of times when Jonah's explanations were too abstract and not sufficiently practical?
This will be useful information for us, because we certainly want to identify areas in which our curriculum needs further improvement. My personal recollection of Jonah's lectures is that they involved a lot of example code, visualization, back-and-forth Q&A, and interactive exploration of real datasets in lieu of presenting, say, abstract mathematical proofs.
Along similar lines, what are some specific topics that you think were neglected in favor of more abstract but less applicable material?
I'm particularly interested in what material you thought was overemphasized in the curriculum--my impression is that all of the topics covered were very fundamental to data science as a whole. While one can express a valid preference for certain fundamental topics over others, I would be hard-pressed to say that any of the topics covered in the Signal curriculum weren't extremely industry-relevant.