- 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)
-ML/Product questions (google/glassdoor/blind/etc)
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?
- New GreedyAlgoWtf. Don't move from swe to ds. It's a very broad domain and gets paid less than swe for the same level.
- Study plan is good, though I urge you to rethink about your decision to move to ds, or at least gather enough data. How much time have you allocated for it?
I am also preparing for a change, trying to move to a better company in a similar role. Pm me if you want to study together and are 'serious' about it.Apr 15 1
- New KKnf67Fast.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.