A Week in the Life of a Data Scientist

Speaker: Dean Abbott

Duration: 60 minutes

Predictive modeling contains six stages of analysis according to the Cross Industry Standard Process Model for Data Mining (CRISP-DM). I will break this down into three primary tasks for predictive modelers including preparing data, building models, and explaining results. 

Data preparation often requires skills in SQL, python, or other languages to be able to pull data out of data stores and convert the normalized data into flattened data that the algorithms can use to build models

Modeling requires a qualitative (if not quantitative) understanding of the algorithms, including mathematics or statistics, in order to bulid the effectively. 

Finally, modelers should know how to explain the results of their findings to other analysts and to decision-makers and stakeholders.

The session will walk through the building of a predictive model for a retail appliction: predicting the days to next purchase propensity model. 

Dean Abbott
Co-Founder, SmarterHQ

Dean Abbott is Co-Founder and Chief Data Scientist of SmarterHQ. Mr. Abbott is an internationally recognized data mining and predictive analytics expert with three decades of experience applying... View More