Three myths about data scientists and big data

What I found useful during my PhD (this could apply to master program too) is that I immediately started to work for a company on GIS, digital cartography, and water management (predicting extreme floods locally - how much the water could rise, at worse in 100 years, at any (x,y) coordinate on a digital map, modeling how any drop of water falling somewhere runs down, goes underground, eventually reaches low elevation and merges with other water drops on the way down - the digital maps had elevation and land use data available for each pixel; by land use I mean crop, forest, water, rock and so on, as this is important to model how water moves). Very applied and interesting stuff. My first paper (after an article about flood predictions, in a local specialized journal) was in Journal of Number Theory though I never attended classes on number theory. I then started to publish in computational statistics journal, but also in IEEE Pattern Analysis and Machine Intelligence, and Journal of the Royal Statistical Society, series B. I'm currently finishing a book on data science (Wiley, exp. The take away from this is that it helps getting polyvalent, if the PhD/Master student can do applied work for a real company, hired and paid as a real employee (partnership between university and private sector), at the beginning of his program. In my case, it was a small R&D company (20 people) so I had the chance to be exposed to many things, not least learning how to write good code used by a team, for real apps (for instance merging hundreds of small images to produce a big map, rotating, filtering images taken by a plane, make sure roads were not broken when moving from one image to another, and putting the whole stuff into some kind of hierarchical database to retrieve and display any portion of the map very fast including adjacent parts, to the end user querying the database).