My background - masters degree in CS - working as a SWE for a few years now My study path towards DS - started off by brushing my stats/calculus/linear algebra - completed machine learning course by andrew ng on coursera - completed the machine learning for coders course by fast.ai - built a couple of projects and published on github to showcase to potential employers. My interview prep method -SQL practice (leetcode) -Stat question practice(brilliant.org) -Coding (leetcode) -ML/Product questions (google/glassdoor/blind/etc) My question 1. Is my study path and interview prep accurate for someone transitioning from swe to ds role? 2. Also for the coding round do DS interviews get in to data structures or are they more generic algorithmic problems. I want to know if i should spend time brushing up on all the DS/time complexity/space complexity etc. 3. What kind of role can someone of my background expect? i have heard some DS roles are more analytical and others are balance of analytics/modeling. 4. I plan to apply in the east coast(NYC,Boston,etc). What kind of TC should i be expecting on an average?
There’s multiple kinds of data scientist though
True , what do you think of my approach ? Am I missing something
It’s good
Wtf. Don't move from swe to ds. It's a very broad domain and gets paid less than swe for the same level.
Are you DS ? Also does it not depend on the role . I’m def not looking for a analytical role.
Yes, I am.
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Fast.ai is for hobbyists. It teaches you to regurgitate a few algorithms, but none of the critical thinking skills needed to be successful long-term. Focus your efforts on the Stanford courses.
True, I do feel after taking andrew ngs course i have a better intuition for ML compared to all the other high level black box udemy courses out there etc. Do you have any pointers in terms of my study path overall and my interview prep route.