"Leaders in business, education and government must take action to foster a new generation of talent with the technical expertise and unique ideas to make the most of this tsunami of Big Data."

-Richard Rodts, Manager of Global Academic Programs, IBM

Level 4 Course Offerings


DSCI 352

This is a project-based undergraduate data science research class, in which project teams in which teams constituted of both data science minor (domain expert) and major students, identify a research project under the guidance of a domain expert professor. The research is structured as a data analysis project including the 6 steps of will focus on developing a reproducible data science project, including 1: Define the ADS question, 2: Identify, locate, and/or generate the data 3: Exploratory data analysis 4: Statistical modeling and prediction 5: Synthesizing the results in the domain context 6: Creation of reproducible research, Including code, datasets, documentation, and reports.  During the course special topic lectures will include Ethics, Privacy, Openness, Security, Ethics. Value.  Recommended Preparation: ADS Level 1,2,3 Qualified Courses.

SYBB 387

This course provides students research experience in data science, proteomics, bioinformatics, and clinical informatics under the guidance of faculty affiliated with the Systems Biology and Bioinformatics program. Areas of research include the production of big data at bench (cellular proteomics, structural proteomics, genomics, and  interaction proteomics) and analysis of  big data such as computational/statistical biology, bioinformatics tool development, and clinical research informatics. A written report must be approved by the sponsor and submitted to the director of the Center for Proteomics and Bioinformatics before credit is granted.