Time Series Analysis | Tutorials
Time series analysis is more relevant than ever with the rise of big data, the internet of things, and the general availability of data that follows events through time. This tutorial will introduce participants to the many versatile tools Python offers for exploring, analyzing, and predicting time series data. The tutorial will be a mix of lecture and practice, and it will be broken down into four components: (1) Handling timestamped data in Python (2) Commonly encountered problems with time series (3) Time series prediction exercises (4) Time series classification exercises
Aileen Nielsen
Since completing degees in anthropology, law, and physics from Princeton, Yale, and Columbia respectively, Aileen Nielsen has worked in corporate law, physics research laboratories, and, most recently, NYC startups oriented towards improving daily life for under-served populations - particularly groups who have yet to fully enjoy the benefits of mobile technology. She has interests ranging from defensive software engineering to UX designs for reducing cognitive load to the interplay between law and technology. Coming off a recent stint as a data scientist in Hillary Clinton's presidential campaig, Aileen now engineers One Drop's diabetes-management products.
Room 6
Wednesday, 17th May, 13:20 - 16:40
