Toolkit Of Dbms: Fill & Download for Free

It depends on what you mean with "data scientist". Here are a few scenarios. If your idol is Nate Silver, or if your data sets are not web-scale (but still running in the millions of observations), and if you want access to state-of-the-art statistical techniques and visualization, I would recommend R. It is distinctively better than MATLAB almost everywhere, including data manipulation, statistics and visualization. If you are doing numerical analysis, then MATLAB is much better, but that's not the typical workload of a data scientist. R is not very hard or strange, even if it's fashionable to say so by people who don't know better.If your work includes a lot of Econometric Analysis (time series, VAR, factor models), go with Stata. Which is the language actually used by Nate Silver.If you want to integrate some simple-to-intermediate statistical techniques in a production environment (some stand-alone app, or a web server), I recommend Python. Among the general-purpose languages, it's the friendliest to data analysis. I also recommend Python as an approachable "glue language", from which to call R, C, Fortran, and a large number of libraries.If you want to do web-scale data analysis, you will have a different toolkit altogether. Various libraries and frameworks like Hadoop, key-value stores, distributed DBMS, columnar DBMS, etc. Some of these tools are managed via a scripting language, but also some inner-loop coding has to happen in C++.

As mentioned above, "data science" is a misnomer. When a new discipline arises, the immediate impulse of its practitioners is to label it science, in an effort to dignify it. Usually, this strategy backfires. Cases in point: "Management Science" in the 60s and "Services Science" in the 00s were both hyped, and both petered out into irrelevance. In light of this, the insistence on "data science" seems a tragic marketing mistake. Better to call it "Data Craftsmanship" or "Data Art": out of a lot of raw, cheap material, the master craftsman makes beautiful things, and augments the intelligence of the guild and society as a whole.Broadly, "data science" is the set of practices targeted at the storage, management and analysis of data sets large enough that require distributed computing and storage resources. Currently the majority of data sources are internet- and transaction-related, but one should not ignore early applications from high-energy physics, meteorology, military simulations, as well as future applications in life sciences.The toolkit is a very broad one. It includes distributed DBMS, noSQL, data-oriented languages, mapReduce (and for some pure functional languages), complexity of algorithms, visualization of large data sets, and some online algorithms from Machine Learning. The best way to get a sense of the toolkit is to follow the dozen or so top blogs written by practitioners.

As an intern, I don't think you'd miss out that much in London, as significant R&D happens in London with some of the most senior experienced development managers based there.I don't know if the training program happens in London however, and our stack includes a lot of proprietary technology (home-grown DBMS, custom development environment and UI toolkit) that makes it hard to just dive into. Our London location is quite nice however and worth trying to join. (Although our new London building, when ready, will be extraordinary. Not quite there yet though.)