ENGG 199: Big Data Analytics for Biomarkers (ENGG 199-05 18S)


18S: Mondays 4-7PM

No Textbook Required


ENGS 93 or QBS 120 or equivalent or permission of instructor.

Extracting critical features from large datasets, such as those produced in the ‘omics field requires appropriate data reduction as well as statistical analyses. Knowing how to handle these datasets effectively is increasingly expected of the STEM graduate student. Students will be exposed to three modules.

  1. Best practices for initial data screening and processing, including data-: cleaning, normalization, transformation, centering, and scaling; utilizing each part of the process to interrogate publically-available ‘omics databases.
  2. Model generation, including: variable selection, end goal optimization context, and data characteristics.
  3. Biomarker evaluation (accuracy, sensitivity, specificity, predictive values, likelihood ratios, and other statistics).