Thinking about data science and modeling

The Statistics Package for the Social Scientist (SPSS) has always made sense to me. For years workplace tools were less complex than SPSS. The new generation of data science focused applications are highly complex. Those applications facilitate slicing and dicing data into epic infographics. That class of applications does allow end users to use highly complex models. Some of those models are not well understood. Access to highly advanced statistical modeling tools does not guarantee understanding. Data science and dating modeling fell flat during this last election.

Executive level reporting has always involved both art and science. Modeling the latest presidential election involved did not go very well for pollsters and pundits. I have done a ton of data modeling over the years. Anybody can pick up a copy of Armstrong’s Principles of Forecasting (2001). Diving in is really the best way to get started. Data science has turned into a first in the pool type of profession. People strive toward predicting the future. Most models that are aimed at predicting the future are not tested against the past. Modeling public sentiment is a challenge. Sometimes it is fleeting. Sometimes permanence exists.

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