Courses Taught by Sung Kyun Park

EHS675: Data Analysis for Environmental Epidemiology

  • Graduate level
  • Winter term(s)
  • 2 Credit Hour(s)
  • Instructor(s): Park, Sung Kyun
  • Prerequisites: BIOSTAT 560 and EPID 503 or 600
  • Description: This course will introduce non-parametric smoothing methods, such as splines, locally weighted polynomial regression (LOESS) and generalized additive models (GAM), and focus on continuous environmental exposure variables. It will also deal with analysis of correlated data, including longitudinal analysis and time-series analysis that are widely used in environmental epidemiology. It will provide an opportunity to analyze actual population data to learn how to model environmental epidemiologic data, and is designed particularly for students who pursue environmental epidemiologic research. The course will consist of lectures and hands-on practices in computer labs, homework assignments and final projects. R, a free software environment for statistical computing and graphics, will be used.
Concentration Competencies that EHS675 Allows Assessment On
Department Program Degree Competency Specific course(s) that allow assessment
EPID Occupational and Environmental Epidemiology MPH Interpret epidemiologic results from higher-order biostatistical techniques applied to these data, such as linear regression, logistic regression, mixed effects models, and graphic techniques EHS675

EPID642: Sampling and Power

  • Graduate level
  • Winter term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Park, Sung Kyun
  • Last offered Winter 2016
  • Prerequisites: EPID 600 (or equivalent), EPID 640 (or equivalent), and BIOSTAT 503 or 553 (or equivalent)
  • Description: This course introduces 1) various sampling methods (simple random sampling, stratified sampling, cluster sampling, convenience sampling, control sampling strategies in case-control design) and 2) power and sample size calculations. This course consists of lectures and hands-on exercises in computer labs, homework assignments, and a final project.
  • Course Goals: The goal of this course is to learn about how to design surveys with appropriate sampling methods widely used in epidemiologic research and how to compute sample sizes and/or powers given different epidemiologic study designs.
  • Syllabus for EPID642
Concentration Competencies that EPID642 Allows Assessment On
Department Program Degree Competency Specific course(s) that allow assessment
EPID General Epidemiology MPH Apply core aspects of field methods in epidemiology (e.g., survey design, sampling and power, surveillance) EPID642, EPID643

EPID675: Data Analysis for Environmental Epidemiology

  • Graduate level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Park, Sung Kyun
  • Last offered Winter 2016
  • Prerequisites: BIOSTAT 560 and EPID 503 or 600
  • Description: This course will introduce non-parametric smoothing methods, such as splines, locally weighted polynomial regression (LOESS) and generalized additive models (GAM), and focus on continuous environmental exposure variables. It will also deal with analysis of multi-level data including analyses of longitudinal data and complex sampling data, and time-series analysis that are widely used in environmental epidemiology. The course will cover how to handle limits of detection in environmental exposure data. It will provide an opportunity to analyze actual population data to learn how to model environmental epidemiologic data, and is designed particularly for students who pursue environmental epidemiologic research. The course will consist of lectures and hands-on practices in computer labs, homework assignments and final projects. R, a free software environment for statistical computing and graphics, will be used.
  • This course is cross-listed with EHS675 in the Environmental Health Sciences department.
  • Syllabus for EPID675

EPID798: Epidemiologic Data Analysis using R

  • Graduate level
  • Summer term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Park, Sung Kyun
  • Last offered Summer 2018
  • Prerequisites: Introductory level courses in Epidemiology (e.g., EPID 503 or EPID 600) and Biostatistics (e.g., BIOSTAT 503 or BIOSTAT 553). Experience in the use of Windows-based microcomputers. No experience of R is required.
  • Description: This course will introduce the R statistical programming language for epidemiologic data analysis. R is a freely available, versatile, and powerful program for statistical computing and graphics. This course will focus on core basics of organizing, managing, and manipulating data; basic graphics in R; and descriptive methods and regression models widely used in epidemiology.
  • Course Goals: The overall goal of the course is to provide students and public health professionals with a set of new data analysis tools.

EPID815: Modern Statistical Methods in Epidemiologic Studies

  • Graduate level
  • Fall term(s)
  • 4 Credit Hour(s)
  • Instructor(s): Park, Sung Kyun Berrocal, Veronica
  • Last offered Fall 2015
  • Prerequisites: EPID 600, BIOSTAT 523 and BIOSTAT 560 for epid students. Biostat 650, 651 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).
  • This course is cross-listed with BIOSTAT698.
  • Syllabus for EPID815