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.
Competencies: Students will have gained an understanding of advanced data science methods beyond typical coursework, and translated these concepts to practical analytic skills for performing research on Big Data topics. Students will showcase their research at a concluding capstone symposium via poster and oral presentations.
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).
Competencies: After completing this class, students are expected to be able to attain the following competencies:
-Describe preferred methodological alternatives to commonly used statistical methods when assumptions are not met.
-Distinguish among the different measurement scales and the implications for selection of statistical methods to be used based on these distinctions.
-Apply descriptive techniques commonly used to summarize public health data.
-Apply common statistical methods for inference.
-Apply descriptive and inferential methodologies according to the type of study design for answering a particular research question.
-Interpret results of statistical analyses found in public health studies.
-Develop knowledge to communicate and collaborate effectively with scientists in a variety of health-related disciplines to which biostatistics are applied (e.g. public health, medicine, genetics, biology; psychology; economics; management and policy).
-Become well-versed in the application of core statistical techniques (biostatistical inference, linear regression, generalized linear models, nonparametric statistical methods, linear mixed models) and 4-5 selected statistical specialization techniques.
-Select appropriate techniques and apply them to the processing of data from health studies.
-Interpret the results of statistical analysis and convert them into a language understandable to the broad statistical community.
-Develop written and oral presentation skills and other scientific reporting skills, based on statistical analyses for public health, medical and basic scientists and educated lay audiences
-Employ state-of-the-art statistical and other quantitative methods in the analysis of epidemiologic data.
-Demonstrate a thorough understanding of causal inference, sources of bias, and methods to improve the validity of epidemiologic studies.
-Understand the principles and methods of data-collection and data-processing procedures in the design and conduct of epidemiologic research, with sound knowledge of measurement validity and reliability, data quality control, data management, documentation, and security