Keep in mind that many of the areas you state will mature and you will not need to be a subject matter expert to use them. Take for example Hadoop. 5 years ag

Keep in mind that many of the areas you state will mature and you will not need to be a subject matter expert to use them. Take for example Hadoop. 5 years ago you had to be able to code to use Hadoop effectively and many people were convinced their big data careers would collapse if they didn't immediately learn to code in the Hadoop ecosystem. Today people are starting to use GUI tools (e.g. Pentaho) for ETL and SQL is replacing writing map-reduce code for many people. Proper Data Engineers with solid technical skills are still heavily in demand, but many of the pseudo-technical people are finding their jobs are changing. You see something similar with AI and ML. 10 years ago AI was "dead" and ML/Stats was for expert CS and Stats people. Now you can do a lot with a few lines of code or API calls. What makes a good Data Scientist/Machine Learning person is not so much understanding every aspect of an algorithm, but being able to understand what is takes to quantify a business problem into a form that is suitable for ML/AI. That requires a blend of technical and business skills. I'd echo what mbrock says. Look at these areas and others (example - The Blockchain: the next thing that is supposedly going to solve all the problems in the universe). Get a solid foundation in CS, find an area that interests you and pursue that with focus. Realize that every 5-10 years you'll need to update your skillset to the next big thing in your area of interest. I'd say that out of all the areas you've listed MR is the newest. There's going to be a lot of hype around that for a few years, and hype = companies want to spend money = jobs.