The schedule and description for the courses this Summer are being worked out. Please visit later for more information.

The Data Science Institute for Summer 2018 will take place in Schaeffer Hall Room # 40. 

June 4, 2018 - 8:30 Am - 12:30 Pm: Network Analysis Using R - Dr. Elizabeth Menninga

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. At least a small amount of prior experience with R is recommended. 


June 5, 2018 - 8:30 Am - 12:30 Pm:  Data visualization - static and interactive using R - Dr. Brandon Lebeau

This course will show how interactive visualizations can be created directly within R for use within web pages, R markdown documents, R notebooks, or Shiny. This hands-on workshop will provide specific examples of creating interactive visualizations using popular JavaScript libraries without needing to know JavaScript. JavaScript libraries that will be shown within R include: plotly, leaflet, DataTables, highcharts, D3. The workshop will work through examples as a group and also give time for users to create interactive visualizations on their own. Individuals will leave with example R scripts and resulting output for reference later on.

Prerequisites: Beginner R knowledge; knowledge of the tidyverse helpful, but not required. No prior JavaScript experience required.

June 6, 2018 - 8:30 Am - 12:30 Pm: Geovisual Analytics - Dr. Caglar Koylu

The course covers the topics of Geographic Information Science, Geovisualization and Visual Analytics. 


June 7, 2018 - 8:30 Am - 12:30 Pm: Social Media Analytics - 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 8, 2018 - 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)