Courses Taught by Sung Kyun Park

EHS675: Data Analysis for Environmental Epidemiology

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 2 credit hour(s) for residential students;
  • Instructor(s): Sung Kyun Park (Residential);
  • Offered Every winter semester (next offering: Winter 2025)
  • Last offered Winter 2024
  • 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.
  • Syllabus for EHS675
ParkSung
Sung Kyun Park

EPID675: Data Analysis for Environmental Epidemiology

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 3 credit hour(s) for residential students;
  • Instructor(s): Sung Kyun Park (Residential);
  • Offered Every Winter
  • 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
ParkSung
Sung Kyun Park
Concentration Competencies that EPID675 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 EPID675

EPID798: Epidemiologic Data Analysis using R

  • Graduate level
  • Residential
  • Summer term(s) for residential students;
  • 1 credit hour(s) for residential students;
  • Instructor(s): Sung Kyun Park (Residential);
  • 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.
ParkSung
Sung Kyun Park

EPID815: Modern Statistical Methods In Epidemiologic Studies

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 3 credit hour(s) for residential students;
  • Instructor(s): Sung Kyun Park (Residential);
  • Prerequisites: EPID 601 (or equivalent) and BIOSTAT 522 and BIOSTAT 523 (or equivalent), Experience using R required (at least the levels covered in EPID 674 (Epidemiologic Data Analysis using R)).
  • Advisory Prerequisites: BIOSTAT 512 (Longitudinal Analysis) recommended
  • Description: This course will cover modern statistical methods in the context of epidemiological applications to address public health problems. This course is intended for PhD students in Epidemiology and related areas. Specific topics may vary each year.
  • Syllabus for EPID815
ParkSung
Sung Kyun Park