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
- Review and present Big Data in relation to neuroscience.
- Explore various sources of Big Data.
- Conduct modern approaches to handling, cleaning, storing, slicing, mining, and visualizing Big Data.
- Introduce primary methods associated with Big Data analytics and R programming (statistical programing),
- luding pattern mining through clustering, classification, and outlier detection.
- 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:
- Demonstrate knowledge and general comprehension of brain health (neuroanatomy), neuroimaging and molecular biology.
- Identify sources of Big Data in relation to health, genetics, neuroscience, and brain health.
- Understand Big Data challenges including storage, cleaning, cloud computing, and sharing.
- Explore, manipulate, analyze and visualize Big Data in relation to brain health, specifically Parkinson’s and Alzheimer’s disease.
- 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.
