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union and in course tasks for analytics course

Practical course work for BMI 569/669: Data analysis

This repo contains practical course work in R / SQL for our Hybrid Data Analytics Course, which is permanently informed with Kaiser. This course was taught from 2013 to today in the summer quarters for students in the biomedical computer science program in OHSU.

Please note that this is only half of the course, which also includes discussions on organizational behavior, implements Analytics projects in an organization, and discussions of pointed results. For the complete course syllabus you will find in the curriculum and the on-campus schedule of topics.

Learning goals:

  • understand the basics of using r / rstudio
  • learn and apply basic SQL requests to a synthetic patient cohort
  • Implement a metric for predicting 30-day hospitals (lace points) for these patient cohorts.
  • learn, understand, and apply simple visualizations to communicate insights
  • understand how to build and interpret logistical regression models

Schedule (online)

The goal of the online part of the course is to familiarize the students with R and how the synthetic patient data can query in r.

All work is performed within a RStudio workspace.

Schedule (in-class)

The aim of the online part is to build a predictive model with the synthetic patient dataset to predict 30 days of readmissions.


The synthetic data set and the original task of calculating the pointed results was developed by David Dorr. All other materials and tutorials were developed by Ted Lateras, Mark Click and Shannon Mceney.


This course material is available under a Apache 2.0 license.

This project is maintained by loaderast

hosted on github pages - topic of ordered list

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