"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 3 Course Offerings



Engineering & Physical Sciences

DSCI 351: Exploratory Data Science for Energy & Manufacturing

Course Description: Data Sources, Data Assembly, and Exploratory Data Analytics

In this course, we will learn data science and analysis approaches applicable to energy and manufacturing technologies, to identify statistically significance relationships and better model and predict the behavior of these systems. We will assemble and explore real-world datasets, perform clustering and pair plot analyses to investigate correlations, and logistic regression will be employed to develop associated predictive models. Results will be interpreted, visualized and discussed.

We will introduce the basic elements of data science and analytics using R Project for Statistical Computing.  R is an open-source software project with broad abilities to access machine-readable open data resources, data cleaning and munging functions, and a rich selection of statistical packages, used for data analytics, model development, and prediction. This will include an introduction to R data types, reading and writing data, looping, plotting and regular expressions so that one can start performing variable transformations for linear fitting and developing structural equation models while exploring for statistically significant relationships.

R Analytics will be applied to the case of energy systems (such as PV power plant  degradation, and building energy efficiency) over time, by analyzing system responses, combined with results of experiments to identify fundamental principles that are statistically significant in the observed system performance.  And it will be applied to manufacturing systems to understand the principles of statistical process control and identify critical factors of variability and uniformity.

Learning Outcomes:

Familiarity with R Statistics, scripting, functions, packages, automated data analysis.

Familiarity with exploratory data analysis, statistical model building

Applications of domain knowledge and statistical analytics to identify important predictors and develop initial predictive models

Dataset characteristics will include:

Variety of types of information including both, structured and unstructured data.

Volume: Data from human sources (vendors, suppliers, distributors, customers, etc.) and sensor networks of the energy system of factory,  both small and large data volumes.

Velocity: Energy system and manufacturing supply chain changes will be included.



SYBB 311/411 Survey of Bioinformatics. 4 Units

This course is offered as four separate 1-month long units (1 credit each): (1) Technologies in Bioinformatics, (2) Data Integration in Bioinformatics, (3) Translational Bioinformatics, and (4) Programming for Bioinformatics. These courses are designed to take a student through the entire workflow of a bioinformatics research project - from data collection to data integration, to research applications. Graduate students can select specific units based on their needs. Overall course grade will be an average of the unit grades. Course Unit Descriptions:

  1. 311/411A: Technologies in Bioinformatics: This course introduces students to the high-throughput technologies used to collect data for applications in genomics, proteomics, and metabolomics (e.g. mass spectrometry; gene sequencing; yeast-two-hybrid; microarrays).
  2. 311/411B: Data Integration in Bioinformatics: This course introduces students to the conceptual models used to integrate and interpret data collected by high-throughput technologies. These models range from knowledge organization structures (e.g. biomedical ontologies) to models of interaction (e.g. gene coexpression networks or protein interaction networks), as well as statistical concepts for dealing with such data.
  3. 311/411C: Translational Bioinformatics: This course introduces students to the clinical and real-world applications of bioinformatics, e.g. pharmacogenomics, GWAS of particular diseases, personalized medicine, systems medicine, microbiome analysis, etc. This course shows students how bioinformatic technologies and methods of data integration can be combined for various applications in biomedical research.
  4. 311/411D: Programming for Bioinformatics: This course will serve as a basic introduction to 1-2 programming languages, focusing on the applications, tools, and packages specifically related to bioinformatics. R, Python, Java, C++, and/or Perl may be taught as the instructor sees fit.

SYBB 321/421. Clinical Informatics at the Bedside and the Bench Part I. 3 Units

This two-semester series provides students with an overview of the field of clinical informatics, focusing on the content areas outlined by the American Medical Informatics Association; the first semester will emphasize the use of informatics in clinical settings (i.e. "the bedside"), and the second semester will emphasize the use of informatics in public health, epidemiology, and translational bioinformatics (i.e. "the bench"). Through lectures, readings, and projects, students will learn to approach problems in clinical medicine through the lens of .informatics,. the science of information, with a focus on applications over theory. As clinical informatics revolves around the development and use of electronic medical records (EMRs), students will be familiarized with EMRs through a hands-on lab simulating clinical workflows.


This is the second of a two-part course sequence in Nursing Informatics. The focus of this course is the transdisciplinary nature of informatics in health care and the use of advanced information technologies(IT) to support decision-making, promote safety, and ensure quality in patient care. Current issues in health care policy and legislation relating to health information technology will be discussed.



MKMR 201

This is an introductory marketing course designed to provide students with the concepts and theories necessary for understanding the fundamental principles of marketing and its role in any organization. Students will learn concepts such as marketing orientation, marketing-mix, relationship marketing and service logic, as well as behavioral theories of customer response and strategic frameworks for customer brand management. Students develop capabilities for understanding marketing issues in real world situations and to create and implement basic marketing plans.