Courses Taught by Sung Kyun Park

EHS675: 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 semester (next offering: Winter 2025)
  • Last offered Winter 2024
  • Prerequisites: (BIOSTAT 522 and EPID 600 (or equivalent)) or PhD standing in Epidemiology
  • Advisory Prerequisites: BIOSTAT 523 is recommended
  • Description: This course is an applied epidemiologic data analysis using R statistical software, a free software environment for statistical computing and graphics, and especially covers unique features of environmental exposure data. The course will cover important statistical issues in environmental health data, such as log-transformation and standardization of chemical biomarkers. It will introduce parametric and non-parametric smoothing methods, such as natural splines, penalized splines, and locally weighted polynomial regression (LOESS), and how to implement them using generalized linear models (glm) and generalized additive models (gam). The course will also cover data analysis subject to the dependence issue, such as longitudinal data and complex sampling data (e.g., NHANES) and time-series analysis in air pollution health effects that are widely used in environmental epidemiology. This course 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.
  • 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 522 and (EPID 600 or EPID 601 or PH 500)) or PhD standing in Epidemiology
  • Advisory Prerequisites: BIOSTAT 523 is recommended
  • Description: This course is an applied epidemiologic data analysis using R statistical software, a free software environment for statistical computing and graphics, and especially covers unique features of environmental exposure data. The course will cover important statistical issues in environmental health data, such as log-transformation and standardization of chemical biomarkers. It will introduce parametric and non-parametric smoothing methods, such as natural splines, penalized splines, and locally weighted polynomial regression (LOESS), and how to implement them using generalized linear models (glm) and generalized additive models (gam). The course will also cover data analysis subject to the dependence issue, such as longitudinal data and complex sampling data (e.g., NHANES) and time-series analysis in air pollution health effects that are widely used in environmental epidemiology. This course 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.
  • 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