When I discuss future career plans with my college peers I say, very vaguely: 'I want to work with data'. They give me strange looks and we go on to discuss Czech beer and potential beer-picking apps for iOS.
My point is, I want to work with data acquired through satellites, I want to design and build distributed systems for genome sequencing, and I wanna make huge, complicated, incompatible datasets suddenly work together to produce clear results.
Regrettably, I'm unlikely to crawl near a decent Hadoop site in the near future, and my courseload for the next semester has nary a mention of a single complex query, so there I am taking University of Washington's Data science course on Coursera. I'm watching the videos on my commute to my awesome mainframe-testing internship, which is made more awesome by the fact that before I got it I would also never have had the opportunity to come near a mainframe system.
The course makes me regret that I did not take it before Machine Learning. I'll be repeating ML from September, and thankfully I still have most of my notes and exercises; hopefully I can now practice ML on a custom dataset (I'm thinking Flickr tags, in the vein of the great Vision Lab at Stanford). The data science course is still in its first week, so I have some time to get familiar with Python syntax before the assignment's due.
I'm just struggling to find a way in which I can get some experience working with biological data before applying for my final BSc project. For several months I will be working on my testing and automation skills, and hopefully implement a small version control system in Node, for fun and glory. Where that leaves me is up for debate as some ridiculously talented people two years my junior are currently cross-referencing Nematoda nerve impulses when inside an active blender.
My point is, I want to work with data acquired through satellites, I want to design and build distributed systems for genome sequencing, and I wanna make huge, complicated, incompatible datasets suddenly work together to produce clear results.
Regrettably, I'm unlikely to crawl near a decent Hadoop site in the near future, and my courseload for the next semester has nary a mention of a single complex query, so there I am taking University of Washington's Data science course on Coursera. I'm watching the videos on my commute to my awesome mainframe-testing internship, which is made more awesome by the fact that before I got it I would also never have had the opportunity to come near a mainframe system.
The course makes me regret that I did not take it before Machine Learning. I'll be repeating ML from September, and thankfully I still have most of my notes and exercises; hopefully I can now practice ML on a custom dataset (I'm thinking Flickr tags, in the vein of the great Vision Lab at Stanford). The data science course is still in its first week, so I have some time to get familiar with Python syntax before the assignment's due.
I'm just struggling to find a way in which I can get some experience working with biological data before applying for my final BSc project. For several months I will be working on my testing and automation skills, and hopefully implement a small version control system in Node, for fun and glory. Where that leaves me is up for debate as some ridiculously talented people two years my junior are currently cross-referencing Nematoda nerve impulses when inside an active blender.
