Why is this course important for academics? 

This course is very important to researchers who use empirical data analysis in their research in the 21st century. Data science problems in academia now often involve large data sets which provide challenges related to variable selection, clustering among a large number of cases, missing data issues, and prediction classification. New tools in this area such as machine learning algorithms, neural networks, nonparametric clustering, penalized regression, imputation methods, and more will be covered.

Why is it important for the labor market?

This course provides the most important set of skills available today. There is no more valued expertise in the global labor market than machine learning and big data analysis, and these are in high demand by corporations, government, and academia. The labor market for data scientists in every modern country in the world exceeds the number of job candidates.

Description

This workshop covers many of the technical essentials for doing data science as practiced in the 21st century. It takes place over five days from 10AM to 3PM with a lunchbreak. Data science is now a vast field and rather than describe a lot of topics in a shallow way we delve deeply into some core topics in the field. These include: a general discussions about data science in the 21st century, machine learning, Bayesian inference, regularization, neural networks, and handling missing data. All of the tools will be motivated by real-world data examples. All slides, code, and example data will be provided on a dedicated GitHub page.

>> Programe

Instructor

Jeff Gill Distinguished Professor, Department of Government, Department of Mathematics & Statistics. Founding Director, Center for Data Science. Member, Center for Neuroscience and Behavior. American University. Editor de Political Analysis. PhD en Ciencia Política, American University; Post-Doc, Harvard University.

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