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

EPID604: Applications Of Epidemiology

• Graduate level
• Residential
• Fall, Winter, Spring, Spring-Summer, Summer term(s) for residential students;
• 1-6 credit hour(s) for residential students;
• Instructor(s): Ella August, James Buskiewicz, Sara Adar, Matthew Boulton, Andrew Brouwer, Melissa Beck, Kelly Bakulski, Miatta Buxton, Joseph Eisenberg, Marisa Eisenberg, Nancy Fleischer, Betsy Foxman, Aubree Gordon, Alexis Handal, Jennifer Head, Jihyoun Jeon, Spruha Joshi, Sharon Kardia, Carrie Karvonen-Gutierrez, Lindsay Kobayashi, Peter Larson, Aleda Leis, Elizabeth Levin-Sparenberg, Lynda Lisabeth, Juan Marquez, Emily Martin, Briana Mezuk, Alison Mondul, Lewis Morgenstern, Belinda Needham, Marie O'Neill, Sung Kyun Park, C. Leigh Pearce, Laura Power, Alex Rickard, Jennifer Smith, Eduardo Villamor, Abram Wagner, Xin Wang, Douglas Wiebe, Zhenhua Yang, Jonathan Zelner, (Residential);
• Prerequisites: Instructor Permission
• Description: Application of epidemiological methods and concepts to analysis of data from epidemiological, clinical or laboratory studies. Introduction to independent research and scientific writing under faculty guidance.
• This course is cross-listed with .
• Syllabus for EPID604

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 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

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

EPID698: Ms Capstone In Epidemiology

• Graduate level
• Residential
• Fall, Winter term(s) for residential students;
• 1-3 credit hour(s) for residential students;
• Instructor(s): Howard Stein, Evan Snitkin, Jennifer Smith, Aruna Sarma, Alex Rickard, Julia Richards, Paul Resnick, Sarah Reeves, Meza Rafael, Laura Power, Patricia A Peyser, C. Leigh Pearce, Sung Kyun Park, Gilbert Omenn, Marie O'Neill, Duane Newton, Belinda Needham, Hal Morgenstern, Lewis Morgenstern, Arnold S Monto, Alison Mondul, Eve Mokotoff, Briana Mezuk, Dan McConnell, Emily Martin, Juan Marquez, Lynda Lisabeth, Elizabeth Levin-Sparenberg, Aleda Leis, Paul Lephart, Peter Larson, Lindsay Kobayashi, Devon Keen, Mark Katz, Carrie Karvonen-Gutierrez, Sharon Kardia, Spruha Joshi, Jihyoun Jeon, Tyler James, Kirsten Herold, William Herman, Jennifer Head, Michael Hayashi, Sioban Harlow, Alexis Handal, Aubree Gordon, Betsy Foxman, Nancy Fleischer, Marisa Eisenberg, Joseph Eisenberg, Philippa Clarke, Carol Chenoweth, Miatta Buxton, James Buskiewicz, Andrew Brouwer, Matthew Boulton, Melissa Beck, Kelly Bakulski, Ella August, Sara Adar, Michael Swain, Eduardo Villamor, Abram Wagner, Xin Wang, Douglas Wiebe, Mark L Wilson, Zhenhua Yang, Jonathan Zelner, (Residential);
• Prerequisites: Enrolled in Epidemiology MS programs
• Description: This capstone research project course is designed for Epidemiology MS students (30-credit or 48-credit CESM programs). Working with their mentor, students are expected to develop an original research project to address public health problems using epidemiologic methods. Students will have the opportunity to apply what they learned in their coursework to important public health questions. Students will work with a faculty mentor to conduct a literature review, develop a research project, develop and implement an analysis plan, write up the results and discuss the implications of the findings, and present their work in the annual Epidemiology Poster Day. Students are expected to begin their capstone project in their first term and complete it in the second term of their final year (or only, for one-year programs) of training (three credits per term, for a total of six credits). The Epidemiology Master’s committee will help students find an appropriate mentor. Details regarding the structure of capstone writing products and evaluation guidelines will be provided in the MS Student Handbook.
• Learning Objectives: The learning objectives of and skills employed in this course are determined by the specific research project. The list below (which is not exhaustive) provides examples of learning objectives for this course: 1. Assess knowledge gaps in the scientific literature; 2. Develop a scientific research question designed to address a gap in the scientific literature 3. Identify appropriate data sources to address a research question; 4. Better understand the role of data in understanding public health problems; 5. Create a data collection instrument and/or collect data; 6. Analyze data (quantitative or mixed data – including both quantitative and qualitative) to test research hypotheses relevant to public health in a manner that reflects principles of epidemiology (e.g., study design, measurement, confounding, etc); 7. Generate appropriate data visualizations and/or presentations; 8. Communicate the significance, approach, and implications of epidemiological research in a written format appropriate for the target audience; 9. Complete research ethics training through the Program for the Education and Evaluation of Responsible Research and Scholarship (PEERRS). Two modules are required: Human Subjects Research Protections and Responsible Conduct of Research and Scholarship (RCRS).
• Syllabus for EPID698

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.

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).
• Advisory Prerequisites: Experience using R required (at least the levels covered in EPID 639), BIOSTAT 512 (Longitudinal Analysis) and EPID/EHS 675 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. Currently, three topics are covered: (a) Variable Selection and Statistical Learning; (b) Inverse Probability Weighting and Causal Survival Analysis; and (c) Causal Mediation Analysis.
• Syllabus for EPID815