Great teachers(if you can find them): Andrej Karpathy, Andrew Gelman & Ben Goodrich(Columbia), Subbarao Khambampati(ASU) to name a few I know of.
Go Where the hard problems are (or find someone who is doing it): If you don't have a good intuition where to get good problems to practice on for a pay, choose a place to work where a Data Scientist is not just building dashboards/analytics but the company/team relies on them for answer to questions like: "What goals should we set for the next half based on what you see"?
ML practitioner (read: use ML tech to do/debug X) different from ML Engineer (Read: Implement ML algorithm X e2e on data ) is different from Applied Statistician (think marketing sciences or powering experiments like A/B tests): All three areas of work in different areas of ML in one form or another. But make sure what you want to work in is clear in your head and your expectations from it.
A lot of ML/Stats can be not with big data and yet really intuitive: I would say look for a problem domain in social/life/pharma/eco/political/survey/edtech sciences. They are full of intuitive models that need to be explainable and are often debuggable. An example here is usage of Stan software for Multilevel/Heirarchical Regression problems. Training here also makes you a great DS.
Go Where the hard problems are (or find someone who is doing it): If you don't have a good intuition where to get good problems to practice on for a pay, choose a place to work where a Data Scientist is not just building dashboards/analytics but the company/team relies on them for answer to questions like: "What goals should we set for the next half based on what you see"?
ML practitioner (read: use ML tech to do/debug X) different from ML Engineer (Read: Implement ML algorithm X e2e on data ) is different from Applied Statistician (think marketing sciences or powering experiments like A/B tests): All three areas of work in different areas of ML in one form or another. But make sure what you want to work in is clear in your head and your expectations from it.
A lot of ML/Stats can be not with big data and yet really intuitive: I would say look for a problem domain in social/life/pharma/eco/political/survey/edtech sciences. They are full of intuitive models that need to be explainable and are often debuggable. An example here is usage of Stan software for Multilevel/Heirarchical Regression problems. Training here also makes you a great DS.