Abstract: 

On first visit, the field of data science can look like a vast uncharted wilderness. In this session, we'll take an aerial tour of data science’s major subfields and explore strategies for acquiring skills in each of them. Most importantly, we will talk about how to choose where to invest your limited study and development time. By the time we are done, you will have a better map of the data science territory and feel more comfortable charting a path that is both satisfying and professionally rewarding.

One way to split data science into subfields is Analysis, Engineering, and Modeling. Analysis focuses on the question of what decisions we can make using the data we have. Modeling focuses on the question of how can we estimate the data that we wish we had. And engineering focuses on how to do it all faster, more robustly, and at greater scale. We will step through each of these sub fields, what skills they include, and what training they typically require. We will also talk about how to customize your own Data science resume, whether it is specializing in one of the three data science subfields, striking a balance between them, or diving deep in all three areas in a particular application domain.

Bio: 

I love solving puzzles and building things. Data science gives me the opportunity to do both in equal measure. I started by studying robotics and human rehabilitation at MIT (MS '99, PhD '02), moved on to machine vision and complex system modeling at Sandia National Laboratories, then to predictive modeling of agriculture DuPont Pioneer, and cloud data science at Microsoft. At Facebook I work to get internet and electrical power to those in the world who don't have it, using deep learning and satellite imagery, and to do a better job identifying topics reliably in unstructured text. In my spare time I like to rock climb, write robot learning algorithms, and go on walks with my wife and our dog, Reign of Terror.