This Data Science and Predictive Analytics (DSPA) course is offered by Prof. Ivo Dinov at the University of Michigan. The package of materials collectively aim to provide learners with a solid foundation of the challenges, opportunities, and strategies for designing, collecting, managing, processing, interrogating, analyzing and interpreting complex health and biomedical datasets. Readers that finish the textbook and successfully complete the examples and assignments will gain unique skills and acquire a tool-chest of methods, software tools, and protocols that can be applied to a broad spectrum of Big Data problems.

You can view the General DSPA Prerequisites. To ensure students are comfortable in this DSPA course, consider taking the self-assessment (pretest) prior to enrolling in the course.

To summarize, students should have prior experience with college level (undergrad) mathematical modeling, statistical analysis, or programming courses or permission of the instructor. Some MOOCs may be taken as prerequisites, e.g., Corsera, EdX1, EdX2. Additional examples of remediation courses are provided in the self-assessment (pretest).

Before diving into the mathematical algorithms, statistical computing methods, software tools, and health analytics covered in the remaining chapters, we will discuss several driving motivational problems. These will ground all the subsequent scientific discussions, data modeling, and computational approaches.