Big ILL (conditioned) Data - What to Do?

Join us at our next meetup for Big ILL (conditioned) Data - What to Do?

This workshop is about the various methods (regularization methods) available in Python (from scipy, statsmodels, sklearn) for handling ill conditioned data. A new generalized method will also be introduced. We will walk through examples of what ill conditioned data are - i.e. when variables are highly correlated and parameter estimates are sensitive to noise - the consequences of such data, and the methods available.

Feel free to bring laptops. We will walk through some examples in a Jupyter notebook. [This is an intermediate level talk with regards to understanding statistical concepts such as linear regression, estimation and prediction.]

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The event will be held at Cal Poly, building 38 room 121. We encourage alternatives to driving onto campus however, if you plan on driving note that parking permits are required from 7am to 10pm on Wednesday's. The closest parking lots for metered parking are H2 and H10, for more info visit: https://afd.calpoly.edu/parking/parkingoncampus/