Research Analytics & Predictive Analytics
3 CREDITS EACH
Description of Research Analytics Course
This module prepares students to leverage big data through the emerging field of predictive analysis. Predictive analytics is the most significant technology trend in business today for organizations that want to achieve competitive advantage. It provides clear, actionable initiatives based on existing company data and is a natural extension of related corporate initiatives in areas such as web analytics, business analysis and data mining. The module covers models such as multiple linear regression, logistic regression, autoregressive integrated moving average (ARIMA), decision trees and neural networks in solving predictive analytics problems.
Description of Predictive Analytics Course
Predictive analytics is a powerful tool for knowledgeable practitioners. Health care data enables analysts to identify with greater precision than ever before which patients will contract a disease, benefit most from a test or treatment, suffer an adverse outcome, comply with instructions or fill a prescription. Data also helps health care businesses operate more efficiently and ethically by identifying which patients are most likely to file a lawsuit or fail to generate a profit. This module trains students to use this information responsibly and effectively.
The course content includes an overview of research methods and ethics, analysis and interpretation techniques, and the logic underlying sampling, measuring and identifying variables. These concepts are explored through the lens of health informatics. Students use R programming language, a free package that is the preferred software for research statisticians and data scientists.
Upon completion of this course, students will be able to:
- Explain the potential value of predictive analytics for improving the performance of an organization.
- Discuss the organizational requirements for carrying out a predictive analytics project.
- Apply the principal methods of predictive analysis to a health care data set.
- Describe examples of real-world predictive analytics projects.
- Understand the inferential, legal and ethical limitations of predictive analysis.