Daniela Hochfellner

Research Assistant Professor, NYU Wagner; Research Assistant Professor, NYU Center for Urban Science and Progress

Daniela Hochfellner

Daniela Hochfellner is a Senior Research Scientist and Research Assistant Professor at CUSP. She also is an Adjunct Research Assistant Professor at the Institute for Social Research, Survey Research Center at the University of Michigan.

For CUSP’s Data Facility, Daniela implements statistically grounded approaches to data integration, data use and dissemination of policies and procedures. She takes part in user support and quality assurance processes for data providers and data facility users. Her research addresses the economics of labor markets, migration, aging and health and ethics in human subject research. For instance, she has been studying the integration processes of immigrants. In addition, Daniela pursues research on labor market participation of older workers, and effects of social security reforms on retirement transition and health outcomes. Her work on research ethics addresses data confidentiality and methods of protecting privacy in the presence of an increasing demand of “big data” in social sciences.

Prior to joining CUSP, Daniela was a Research Investigator at the Institute for Social Research, Survey Research Center at the University of Michigan. Daniela also was a Researcher at the Research Data Centre at the Institute for Employment Research in Nuremberg, Germany. She has worked with survey data, administrative data, and big-data and has a deep knowledge and extensive experience in linkages of social security records, administrative information and survey data. Daniela has been awarded funding for her research from the Alfred, P Sloan Foundation, the National Science Foundation and from the German Ministry of Education and Research.

Daniela Hochfellner received her PhD in Sociology from the University of Bamberg, Germany.

The goal of the Big Data Analytics for Public Policy is to develop the key data analytics skill sets necessary to harness the wealth of newly-available data. Its design offers hands-on training in the context of real microdata. The main learning objectives are to apply new techniques to analyze social problems using and combining large quantities of heterogeneous data from a variety of different sources. It is designed for graduate students who are seeking a stronger foundation in data analytics.

The course video provides more information.

 

Download Syllabus

The goal of the Big Data Analytics for Public Policy is to develop the key data analytics skill sets necessary to harness the wealth of newly-available data. Its design offers hands-on training in the context of real microdata. The main learning objectives are to apply new techniques to analyze social problems using and combining large quantities of heterogeneous data from a variety of different sources. It is designed for graduate students who are seeking a stronger foundation in data analytics.

The course video provides more information.

 

Download Syllabus