I have been preparing to land a job as a senior data scientist for the past 6 months, did some online courses and know the basics of DS workflow. My job currently involves SQL(easy Leetcode), Python(to some extent not everyday -- Little bit more than "Hello World!"), I am confused on where to start and how to go about it. So, I am planning to breakdown my preparation into three buckets SQL (StrataScratch or Leetcode) Statistics and probability (once a week Kaggle) Python (StrataScratch or some course in Coursera that teaches me Data structures & Algo) But then I already started learning TensorFlow from coursera.... so should i stop that and go with the preparation above or do both? ( I have a poll on this) My Target timeline is to start working in the job within 6 months from now. Note: If you are looking to join me for preparation ping me. #tech #dataanalytics #datascientist #datascience
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what are you trying to get into? product analytics or ml engineering?
Experience, experience, experience. Most critical is if you have shown yourself to independently drive many stage of a data science project end-to-end, working with stakeholders, getting the data, getting collaborators to work with you on the things you don’t know (e.g., build a shiny UI if that’s part of the product). If you don’t have that kind of experience (maybe not in data science, but other, related fields), then perhaps not ready for a senior role yet. Data science is much more than coding and algorithms. My two cents (maybe not the consensus here) would be to focus on stats.
Thank you for the feedback. I think I understand what you are saying there and thats where my dilemma is ... Google cloud platform has all the tools in the book to walk through the DS workflow ( Pubsub, Big query, Data studio ) so doesn't it makes sense to work on a end to end project and learn the coding & algorithms as part of the process?
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Yoe?