two routes from what i've seen to use data effectively (i work with quite a few data scientists): 1. Go very technical, try to win kaggle competitions, try to

two routes from what i've seen to use data effectively (i work with quite a few data scientists): 1. Go very technical, try to win kaggle competitions, try to create the very best solutions possible. This probably means learning all the cutting edge techniques, being very up-to-date with what's going on in the algo space, and lots of practice/experience, and you probably have to have a knack/passion for this kind of competition as well. 2. Go very domain/business driven - force yourself to optimize thinking about high impact business questions that you'll be answering with data. If you can craft a strong answer that leads to high level business strategy, it doesn't matter what techniques you used, people value that. This kind of person is not ML centered, they are more of a stats person with some tensorflow mixed in. Generally speaking, Google and AI/tech companies prefer #1, non tech companies prefers #2. Most people who want to get into data science do it for the exact same reasons you do - it's math-y and new. Other types of programming are old, not so math-y, boring. However, data science at a business level is completely different from data science at a hobby level because doing data science is very expensive. It requires much more capital and infrastructure than regular programming. Definitely more expensive than developing hardware in my opinion, especially if you look at data collection costs, data scientist salaries, and how much work is needed to extract meaningful business value out of data. A hobby level data scientist is probably similar to a hardware or robotics hobbyist in my opinion - they both play with fun tools and spend money/time but have difficulty translating it into real businesses/profession due to the high capital cost needed to even access the really good tooling, let alone do something at a competitive level with those tools.