Using Synthetic Data for Teaching Data Science

Notes on our preprint about synthetic clinical data.


December 14, 2017

Hi Everyone, our paper called Teaching data science fundamentals through realistic synthetic clinical cardiovascular data is now available to read on Biorxiv.

In this paper, we talk about a dataset that we synthesized for teaching aspects of clinical data that may be tricky to understand in data science. This dataset is interesting because it’s derived from a multivariate distribution based on real patient data, modeled as a Bayesian Network. Even when we knew true marginals for the real data, there was a lot of fine tuning to the Bayesian Network.

We’ve used this dataset for a couple of classes, and we’ve found that it helps highlight real issues in predictive modeling of clinical data. One of the largest is that most predictive models are based on a much older patient cohort (50+), which means that we don’t know much about how to predict cardiovascular risk in younger patients. Part of the teaching exercise is having the students choose a cohort of interest and then attempt to predict on that patient cohort.

The data is currently available as an R package here, including vignettes about how the data was generated:


BibTeX citation:
  author = {Ted Laderas},
  title = {Using {Synthetic} {Data} for {Teaching} {Data} {Science}},
  date = {2017-12-14},
  url = {},
  langid = {en}
For attribution, please cite this work as:
Ted Laderas. 2017. “Using Synthetic Data for Teaching Data Science.” December 14, 2017.