Furthermore, there's a number of practitioners that expect their data to be ready for them in some perfect state. Probably a majority of the task is create a pipeline for acquiring data and labeling it appropriately if necessary, which may require developing some ontology or classification with rigid guidelines such that someone in India can delegate the task to a large team. Then the practitioner spends an inordinate time optimizing some heuristic that has a meaning that drifts over time, or is completely inconsistent with the goals of the product. These are both problems outside the realm of domain knowledge or experience.
-Some candidates can write great code, but don't have the math background to understand what ML black boxes are doing.
-Then there are STEM PhDs that have never written non-research (i.e. maintainable) code or had to formulate a qualitative business problem into a quantitative problem they can solve.
Both types of candidates need to come in at a "junior" level and do some on-the-job learning in order to be fully successful data scientists. IMO it appears to be easier to teach STEM PhDs how to code than programmers how to do math, but that might be personal bias (since I came from the former group).
Also, quant devs are heavily involved in building the calculation engines that invokes the models. These engines handles real-time dataflow and calibrations etc and are often highly non-trivial.
My guess is that that type of role is relevant in a data science context. This is much more than data cleansing and piping data between databases.
Tuned out programming never really required much math background, it is the level brain teaser that programming posed is as much as math education. So anyone who's has survived math advanced degree would take program like piece of cake, but it doesn't mean people from non-STEM background is hopeless to master data science.
Yet it's a joke to refer data science without referencing to advanced math concept. Albeit significant domain knowledge, data science is not just business analysis aided with spreadsheet. Modelling is an essential part of.
Then what's the point of the Ph.D.? Why not just go straight from B.S. to junior data scientist then?
[1] http://www.tandfonline.com/doi/full/10.1080/00031305.2016.11...
Anyone have any good resources for self-teaching stats? I have a BS in math but only took one stats course, and it was as terrible as all intro-stats classes are. I have a strong, proof-based understanding of probability theory, but haven't found a similar approach to stats. It all seems to be "if data looks like this, use this test, watch for these pitfalls" which is terrible for building intuition.
Datacamp also launched a bunch of new stats courses recently. I haven't checked them out yet, but their courses are usually good quality. https://www.datacamp.com/courses/topic:probablity_and_statis...
A fundamental challenge I see here is how bottom-heavy data science feels now. There are tons of people out there trying to "get into data science" from other fields, but the number of people with substantive domain knowledge, strong programming skills, and the math background to be able to understand the ML black boxes is quite small relative to the number of people calling themselves data scientists. In other words, real insight definitely is (or should be) the goal, but real insight is really hard, and scikit-learn is so easy.
My hope is that this improves over the next 5-10 years - the more mature data science becomes as a discipline/career, the better the education will be and the more experienced people there will be. There is a risk in the mean time, though, that a flood of relatively inexperienced people causes a collapse in expectations for data science, making businesses less eager to hire them in the future.