TraCS Biostatistics Seminar Series: Exploring high-dimensional datasets: Principal Component Analysis and clustering

Exploratory data analysis is useful to understand a dataset—to find insights and generate hypotheses, rather than test hypotheses. t’s easy to understand a dataset when it’s small, just a couple of patients and relatively low number of variables. What do you do if there are many variables or many data? How can we understand what’s going on in the data, then? In this session of the TraCS Biostatistics Seminar series, you will learn more about statistical methods to explore high-dimensional datasets.

Exploratory data analysis is useful to understand a dataset—to find insights and generate hypotheses, rather than test hypotheses. It’s easy to understand a dataset when it’s small, just a couple of patients and relatively low number of variables. What do you do if there are many variables or many data? How can we understand what’s going on in the data, then?

In this session of the TraCS Biostatistics Seminar series, you will learn more about statistical methods to explore high-dimensional datasets.

Presenter: Jeff Laux, PhD
Research Associate, Biostatistics Team
NC Translational and Clinical Sciences Institute, UNC-Chapel Hill