There is no magical set of qualifications to become a data scientist. Just learn enough linear algebra, probability. Show people you can code. Maybe setup some github projects. It is not like people in tech are doing something magical with all these fancy data scientists. A little bit of math, a slap and dash of code.
Are data scientists whom are in demand today dealing with neural networks and machine learning, or are a large majority still working with large sets of data and running correlation analyses/regressions? Your response above seems to indicate that it's not overly complicated.
It depends; I personally haven't used neural networks since graduating. Standard machine learning algorithms get used. I work with large data sets all the time. Sometimes correlations, regression things. The point is that 95% of the stuff that gets used on a day to day basis is not hard to learn. Especially if your background in linear algebra, probability and statistics is good. The five percent that delves into more complicated things can be figured out on the job.
