An anonymous reader writes "I am a mid-career IT professional in the middle of a transition from IT to a domain within the biological sciences. My planned academic route to the target new domain will take at least 3-5 years to finish. In the interim, I want to work in (and earn from) the IT domain of Big Data/Data Science, since that is more aligned with the skills I need in my target new domain: data analysis, visualization, signal processing, imaging, simulation etc. The problem is that apart from early career stints, I've very little and only surface level experience with these topics. So I want to ask Slashdot for suggestions on the tasks Ive set myself to accomplish this transition. Specifically:
- What are the foundational topics I need to learn. What parts of math, statistics, machine learning, text analysis, scientific programming...?
- What books to read?
- What courses (preferably open/online) to take?
- I want to set up an online portfolio of big-data projects that I work on to showcase skills that I acquire in this domian. What are some of the more challenging, topical and novel applications areas and open problems to showcase in a portfolio, such that it is distinctive and interesting. E.g., consumer behavior, neuro-/bio-informatics, socio-economic trends ...
- How do I find sources of open/non-propreitary data sets to use for my portfolio projects?
- What hosting resources do I need to set up a portfolio of big-data projects? Any suggestions on specific hosting providers?
- What tools should I strive to learn (preferably FOSS): E.g., Hadoop, R, Octave, Python ...?
- What are the industry and trade bodies that cater to big-data professionals?
- How do I acquire mentor(s)/guide(s) who can informally guide me through the above skill acquisition and portfolio creation tasks?
- Any othe Data Science related wisdom