TRANSLATIONAL CARDIOVASCULAR BIOMECHANICS AND MAGNETIC RESONANCE IMAGING
This course will cover some more advanced topics in cluster computing on the U-M Flux Cluster. Topics to be covered include a review of common parallel programming models and basic use of Flux; dependent and array scheduling; advanced troubleshooting and analysis using checkjob, qstat, and other tools; and parallel debugging and profiling of C and […]
This course will familiarize the student with the basics of accessing and interacting with Linux computers using the GNU/Linux operating system’s Bash shell, also generically referred to as “the command line”. Topics include: a brief overview of Linux, the Bash shell, navigating the file system, basic commands, shell redirection, permissions, processes, and the command environment. […]
Learn data analysis with Python. We’ll be using pandas, the go-to Python library used for data wrangling and analysis. We’ll be practicing with several different real-world datasets (e.g. time-series, text) and learning how to read, write, clean, transform, merge and reshape data. The workshop is intended for users with basic Python knowledge. Anaconda Python 3.6 […]
Multilevel modeling is the state-of-the-art approach for handling data with complex dependence structure in a regression analysis. This workshop will discuss fitting multilevel models in Python using the Statsmodels package. We will discuss the motivation and main use cases for multilevel modeling, and illustrate by example how to fit linear and generalized linear mixed models. […]
What can we learn from neurochemical and cellular perturbations of in vitro neuronal network dynamics?
BRIDGING THE DIVIDE: FOSTERING INTERDISCIPLINARY COLLABORATIVE RESEARCH IN COMPUTATIONAL COGNITIVE NEUROSCIENCE
Do you have experience in working alongside community partners in data analysis or program evaluation? Do you want to connect with others who are using their skills for public good? National efforts from organizations such as DataKind, Data Science for Social Good, and Statistics without Borders have been expanding in recent years as more individuals […]
Survival analysis is used when working with data that may be censored, as often is the case in studies of human subjects with incomplete follow-up. The presence of censoring makes most forms of regression and other standard statistical analyses inappropriate. A body of specialized techniques for analyzing this type of data has been developed, including […]
Nonlinear relationships abound in nature, though typical statistical models ignore this in favor of simplicity, often at a cost of both predictive capabilities and better understanding of the underlying phenomenon of interest. One means to explore such relationships is through generalized additive models (GAM). This workshop will introduce participants to GAMs as a means to […]