Data Science Expertise in the public interest
Social Science Methods
The University of Michigan Institute for Social research is a leader in developing and applying new social science methods. This portal is a resource to research using data science to solve challenging questions.
The National Neighborhood Data Archive (NaNDA) is a publicly available data archive containing contextual measures for locations across the United States. NaNDA offers theoretically derived, spatially referenced, nationwide measures of …
The political communication project studies how impressions of political candidates are formed by focusing on the role of media representations of them in conjunction with discussions on social media.
Featured Event: Weekly CoderSpaces Winter 2020
Tuesdays 10-11:30 AM, ISR Thompson Room 1450, with PDHP Data Scientists Paul Schulz and Chen Chen
- Paul Schulz is a senior consulting statistician and data scientist for ISR's Population Dynamics and Health Program. He specializes in statistical methods and computing, including hypothesis testing, data analysis and modeling, sampling (including weight creation and adjustment, and power calculation), as well as the use of secure computing enclaves (SRCVDI, Likert cluster, and Great Lakes). Paul writes code in Stata and SAS for general-purpose desktop computing, and R and Python for selected applications, such as data visualization and web scraping/automation, among other uses.
- Chen Chen is a data scientist, programmer, and consultant for ISR's Population Dynamics and Health Program. He specializes in survey methods (with a particular focus on survey statistics, sampling, and weighting), data management, and statistical computing, including large scale simulations of complex samples and statistical modeling using complex and longitudinal survey datasets. Chen is a high-level programmer who specializes in R, Python, and Stata, with a focus on computing in a Linux environment.
Wednesdays 10-11:30 AM, ISR Thompson, Room 6080, with ARC-TS/UMSI Research Data Scientist Armand Burks and SRC Research Assistant Professor Erin Ware
- Dr. Burks is a Research Data Scientist in Advanced Research Computing Technology Services (ARC-TS) and the School of Information. He specializes in evolutionary computation (genetic programming), and has professional experience in software development and writing cloud analytics. Dr. Burks is available to assist in general programming using C++, Java, and Python, bash commands/scripting, automation of tasks such as data parsing, transformation/conversion, workflow automation, etc., HPC job creation/submission, version control in git, and other related topics.
- Dr. Ware is an Assistant Professor of Research in the Population, Neurodevelopment, and Genetics group at ISR, a self-taught HPC user, and an occasional instructor in the School of Information. Her training has been in genetic epidemiology, public health, and statistics using SAS (local), R (server), Linux (on GreatLakes, MBNI, and other personal servers), and batch scripting (SGE, PBS, Slurm). Dr. Ware has taught SAS (data management and statistical modeling), introductory statistics using R, and math methods for data scientists. She is experienced in teaching high-performance computing to individuals with a limited programming background.
Thursdays 4:30-6 PM, ISR Thompson, Room 1450, with CPS Research Faculty Yuki Shiraito and ISR Program Manager Jule Krüger
- Dr. Shiraito is a Research Faculty with the Center for Political Studies and an Assistant Professor in the Political Science Department. He is available to assist with a variety of topics that include Bayesian statistics, parallel computing in R, OpenMP and Rcpp, web scraping using Python, working with the University’s high-performance computing clusters (Great Lakes and Cavium), and other computational methods.
- Dr. Krüger is the ISR Program Manager for Big Data and Data Science, based within the Center for Political Studies at the Institute for Social Research. She has more than 10 years of experience in processing, analyzing and interpreting data for social science research, and automating workflows for scalable, auditable and reproducible analysis. Dr. Krüger can assist with R, Python, Markdown, Make, bash, LaTeX programming, and version control in git.