Curriculum Modules

This 15 week course utilizes an applied approach to defining, exploring, analyzing and summarizing Big Data in health with a focus on brain health and neurosciences.

COURSE OBJECTIVES

1. Review and present Big Data in relation to neuroscience.
2. Explore various sources of Big Data.
3. Conduct modern approaches to handling, cleaning, storing, slicing, mining, and visualizing Big Data.
4. Introduce primary methods associated with Big Data analytics and R programming (statistical programing),
5. luding pattern mining through clustering, classification, and outlier detection.
6. Summarize applications of Big Data analytics in genetics, neuroimaging, and health sciences.

STUDENT LEARNING GOALS

By the end of this course, students will be able to:

1. Demonstrate knowledge and general comprehension of brain health (neuroanatomy), neuroimaging and molecular biology.
2. Identify sources of Big Data in relation to health, genetics, neuroscience, and brain health.
3. Understand Big Data challenges including storage, cleaning, cloud computing, and sharing.
4. Explore, manipulate, analyze and visualize Big Data in relation to brain health, specifically Parkinson’s and Alzheimer’s disease.
5. Synthesize, summarize, and present Big Data findings and outcomes.

Module 1:  Introduction to applications Big Data analytics in neuroimaging and health science.

Module 2:  Introduction to modern approaches to handling, cleaning, storing, study design, visualizing, and other means of summarizing Big Data.

Module 3:  Introduction to some predominant methods associated with Big Data analytics and R programming, including pattern mining through clustering, classification, and outlier detection.