Description: The "Transforming Analytical
Learning in the Era of Big Data" course is a a six week undergraduate summer program that exposes students to diverse techniques, skills and problems in the field of Big Data and Human Health. Students receive a broad and interdisciplinary introduction to statistical theory and concepts during morning lectures led by faculty from across campus. Afternoons are spent in small faculty-mentored research groups analyzing real big datasets to address focused research questions. The course also includes professional development designed to prepare students for the graduate school application process.
Course Goals: The overarching goal of the summer institute is to recruit and train the next generation of big
data scientists using a non-traditional, action-based learning paradigm. Specifically, the course goals are to: 1) Teach a select group of STEM undergraduate students selected topics in statistics, biostatistics, computer science, and information science necessary to raise the skills and interest of students to a sufficient starting level to consider pursuing graduate studies in 'Big Data' science. 2) Mentor small groups of students focused on a specialized research topic in big data in the biomedical
sciences. These small groups will bring together students from various undergraduate backgrounds to work on a research topic led by a faculty member of biostatistics, statistics, or computer science. 3) Disseminate teaching and research products via creation of open-source tools, lectures, problem sets.
Learning Objectives: At the conclusion of the course, students will have the skills required to pursue graduate studies in Big Data science.
BIOSTAT698: Modern Statistical Methods in Epidemiologic Studies
Prerequisites: EPID600, BIOSTAT522 and BIOSTAT523 for epid students; BIOSTAT650, BIOSTAT651 for biostat students
Advisory Prerequisites: EPID 798 for epid students; BIOSTAT 695 for Biostat students
Description: The goal of this pilot course is to create an interdisciplinary educational experience for Ph.D. students in Epidemiology (also available as an optional elective for Masters students in Biostatistics) through a uniquely designed course that contains lectures on advanced biostatistical methods, but places them in the context of epidemiological applications.
Course Goals: Students enrolled in the class will learn about cutting edge statistical methods in these four contemporary topics that arise frequently in the present scientific context. These four topics are: (a) Modern techniques for model building and variable selection; (b) Methods for analyzing longitudinal data; (c) Spatial regression methods; (d) Methods for studies of interaction/effect modification. The course will equip the new generation epidemiologists with state-of-the-art statistical methods in these domains, and teach them the craft of translating a practical problem into mathematical equations. However, the entire theoretical learning process will be placed in the context of sophisticated modeling of data from large complex studies with a focused problem to solve. Data for the projects will come from two studies that Professors Park and Mendes de Leon are involved with: the Normative Aging Study (NAS) and the Chicago Health and Aging Project (CHAP).