The Data Science Institute for Summer 2019 will be held in 140 Schaeffer Hall with the following sessions.
June 10, 2019 - 8:30 Am - 12:30 Pm: Network Analysis Using R - Dr. Daniel Sewell
Introduction to Social Network Analysis with R will introduce attendees to concepts of social network analysis by illustration. The course will walk through R code, learning what the code does and introducing network concepts along the way. Attendees will leave with knowledge of commonly used R packages useful for network analysis. beginner R knowledge is recommended.
June 11, 2019 - 8:30 Am - 12:30 Pm: Mixed-effects models and related topics with R - Dr. Ariel Aloe
This course provides a practical introduction to mixed-models and related topics with R. These models allow for the analysis of nested and cross-classified data. Nested and cross-classified data structures occur often in many contexts (e.g. students nested within classrooms or schools, patients nested within clinicians, teeth nested within mouth, repeated observations nested within subject, etc). Participants will learn how to use a variety of mixed models and related packages available in R (e.g., nlme, lme4, sandwich, geepack)
June 12, 2019 - 8:30 Am - 12:30 Pm: Data visualization - static and interactive using R - Dr. Brandon Lebeau
June 13, 2019 - 8:30 Am - 12:30 Pm: Social Media Analytics with Python - Dr. Kang Lee
This unique hands-on course will cover the basics of social media analytics in Python. Participants will be able to learn how to collect, process, and analyze and visualize Twitter data using commonly-used data analytics tools and libraries in Python such as Jupyter Notebook, pandas, and networkx. Prior experience with Python is recommended, but not required.
June 14, 2019 - 8:30 Am - 12:30 Pm: Survival Analysis - Dr. Frederick Boehmke
Introduction to Survival Analysis will cover the basics of specifying, estimating, and interpreting discrete-time and continuous-time survival models. Survival models are appropriate for time-to-event data used in the social and health sciences, among other disciplines, and can be analyzed with logit and probit models, with parametric models including the Weibull, or with the Cox semi-parametric estimator. The workshop will cover many of the important features and key decisions for these models and touch on a few extensions such as competing risks, repeated failures, and cure models. Stata code and exercises will be provided and worked through – a basic knowledge of Stata will be helpful but is not essential.