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It's Ondřej Vostal.
I have been working as a data scientist & quantitative analyst for 3 years. My career focus is mathematical statistics applications in statistical learning. A big part of this is doing machine learning, i.e. implementing and using statistical learning algorithms practical in terms of memory, speed and parallelization requirements.
Ability to collect and deal with large to big data is a necessity. I worked as linux programmer & administrator for seven years hence I'm able to use lots of standard and even many exotic UNIX (and of course GNU) utilities, which enables me to complete all sorts of data collection and maintenance tasks quickly and brilliantly.
I use python, R to do data science and sometimes scala or C++ to do build concrete data-centric solutions.
I like new programming languages and can use a lot of them on a basic level. This gives me a unique programmer insight and enables me to write simple and expressive code which does what it is supposed to efficiently.
I always write complete and concise documentation. That is to your benefit, because you'll be able to understand my programs even after years pass.
Data programing is specific in that it is not sometimes obvious that you are indeed constructing a complex program with multitude of entry points.
Since you need to have fast starts on some projects, you'd be spending a lot of time unentangling the parts to extracts components which could be reused nicely.
During the years at school a number of seminal papers accumulated. You can use the included LaTeX sources to your benefit while learning LaTeX. If there is no Makefile to compile the document, try plain `latex' or `pdflatex'.
High schol projects