Department of Epidemiology

Master of Science 42.5 Credit | Academic Year

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The 42.5 credit SM is typically completed over Fall and Spring semesters of one academic year and is designed for applicants with a medical degree or master’s-level background in relevant disciplines (e.g., biology, chemistry, genetics, physiology, bioengineering, and related social and computational sciences). Some students begin the program by also completing one of the Summer programs in Clinical Effectiveness or Public Health Studies. Students must complete a culminating experience for this program.

Questions regarding this program can be directed to either our Faculty Director, Pamela Rist (prist@mail.harvard.edu) or our program administrator Jeffrey Noyes (jnoyes@hsph.harvard.edu).

Course Requirement
(2.5 credits = 1/2 term course or ~7-8 weeks, 5 credits =full semester or ~15-16 weeks)
Epi 201 Introduction to Epidemiology (2.5 cr)
Epi 202 Elements of Epidemiologic Research (2.5 cr)
Epi 203 Study Design in Epidemiological Research (2.5 cr)
Epi 204 Analysis of Case-Control and Cohort Studies (2.5 cr)
Bst 201 Introduction to Statistical Methods (5 cr)
Bst 210
OR 213		 Analysis of Rates and Proportions(5 cr) OR
Applies Regression for Clinical Research (5 cr)

Course Requirements

  • 42.5 total credits earned
  • 30/42.5 credits are ordinal
  • 10/30 Ordinal credits must be in Epidemiology
  • 10/30 Ordinal credits must be in Biostatistics

**All core courses listed above must be taken for ordinal grading**

Competencies

By the end of the program, it is anticipated that the student will acquire the ability to:

1. Critically evaluate and apply principles of epidemiologic methods, including exposure and outcome measures, measures of association, bias and confounding, and study design options.

2. Demonstrate the ability to critically evaluate and apply appropriate biostatistical techniques for data arising from evaluation of public health problems (e.g., including basic probability theory and common distributions, effect measure estimation, continuous and categorical data analysis, parametric and non-parametric hypothesis tests, confidence intervals and p-values, correlation and basic regression techniques, and power/sample size calculations).

3.Apply appropriate biostatistical modeling methods using software packages (e.g., Stata, SAS, or R) to perform multivariable data analyses including control of confounding and detection of effect modification.

4. Evaluate and synthesize epidemiological studies published in the medical and public health literature.