Graduate Course Directory and Schedules

The key culminating step for each MPH student is the Integrative Project (IP). Through the IP, a high-quality written product is produced, which we call the “IP paper.” Through the IP paper, students demonstrate their academic learning and public health practice skills through the synthesis of foundational and program competencies and application of those competencies to complex public health issues. The paper will take the form of a substantial written product such as a program evaluation, policy or economic analysis, grant proposal, health promotion or community engagement program plan, publishable manuscript, or other written product that demonstrates integration of three foundational (one must include Foundational Competency #6) and three program competencies. Appropriate types of written products vary by program, type of practice experience (if the two are integrated), and the student’s career goals. We recommend (but do not require) that the IP paper build upon work conducted in the Practice Experience. For example, students may write a high-quality written paper using the findings from a statistical analysis performed in support of a research project that is separate from their Practice Experience.

More Information About the Integrative Project

This course covers a broad range of basic statistical methods used in the health sciences. The course begins by covering methods of summarizing data through graphical displays and numerical measures. Basic probability concepts will be explored to establish the basis for statistical inference. Confidence intervals and hypothesis testing will be studied with emphasis on applying these methods to relevant situations. Both normal theory and nonparametric approaches will be studied including one- and two-sample tests of population means and tests of independence for two-way tables. Students will be introduced to one-way analysis of variance (ANOVA), correlation, and simple linear regression. The course focuses on understanding when to use basic statistical methods, how to compute test statistics and how to interpret and communicate the results. Computer applications are included as part of the course to introduce students to basic data management, reading output from computer pack-ages, interpreting and summarizing results.

Slash Listed Courses

Doctoral students register for the BSTA 611 section.

Categorical Data Analysis is the third course in the required sequence for applied Biostatistics (Bsta 511, Bsta 512, Bsta 513 or Bsta 611, Bsta 612, Bsta 613). This course covers topics in categorical data analysis such as cross tabulation statistics, statistics for matched samples, and methods to assess confounding and interaction via stratified tables. Students will learn logistic regression, and relate results back to those found with stratified analyses. Similar to linear regression in Bsta 512/Bsta 612, topics for logistic regression will include parameter interpretation, statistical adjustment, variable selection techniques and model fit assessment. Students will have the opportunity to be exposed to other analysis methods, such as Poisson regression and multinomial logistic regression, etc. We will also learn some machine learning techniques other than logistic regression model. Most homework assignments for this course require the use of statistical software.
 

Prerequisites:

1. BSTA 511/611: Estimation & Hypothesis Testing for Applied Biostatistics
2. BSTA 512/612: Linear Models

Slash Listed Courses

Also offered as BSTA 613 for doctoral students.

This course is designed for students who want to develop and expand their skills in data management, statistical analyses and graphics for the real world applications using SAS. The course will give students opportunities to build their data management programing skills from basic to advanced levels in SAS. As part of the course competencies, students will have chance to learn how to export SAS data sets and create ODS files for Microsoft Excel. Students will have chance to build their analysis skills from basic to advanced levels using SAS. The class will be taught in a computer lab in order to give the student hand on experience using SAS to manage data, perform analyses and produce graphs. Class sessions and homework will be oriented around particular data management and analysis tasks. Health-related data sets will be provided for students to practice. This course could be extremely helpful in preparation for thesis, capstone or other research projects.

This is an online course designed for students and researchers who are interested in learning statistical methods in the design and analysis of clinical trials. Students are expected to have certain statistical background in order to gain deep understanding to the topics covered in this course. Topics to be discussed in the course include introduction to clinical trials, fundamentals of Bayesian statistics, sample size computation for trials with dichotomous, continuous and time-to-event outcomes, methods of randomization, design challenges for oncology clinical trials, Bayesian methods in clinical trials, adaptive clinical trial design and designs for predictive biomarkers.

This course is designed to introduce theory and methods for statistical learning and data science. Data science is an emerging field that overlaps with computer science, artificial intelligence, machine learning/deep learning and statistics. This is an exciting time to observe the birth of the new field. In recent years, statistical learning has been increasingly becoming crucial in data science. Ever-increasing data complexity and unconventional data create new challenges for traditional statistical learning, and this is an active research area. This course will cover traditional statistical learning methods as well as newer methods for such challenges.

Prerequisites:

• BSTA 512/612 Linear Models

The goal of this course is to cover the broad range of statistical methods used in health sciences. Methods of summarizing data through graphical displays and numerical measures will be discussed. Basic probability concepts will be explored to establish the basis for statistical inference. Confidence intervals and hypothesis testing will be studied with emphasis in applying these methods to relevant situations. Both normal theory and non-parametric approaches will be studied. Course focus will be to understand when to use basic statistical methods how to compute tests to statistics and how to interpret results. Computer applications (using statistical software) are included as part of the course.

Prerequisite

Graduate standing

The course provides hands-on data analysis and/or biostatistical consulting experience to students outside classroom settings. Students will have opportunities to perform data analysis with inputs from faculty members. Students should have adequate skills in at least one statistical program among STATA, SAS or R and finished BSTA 512 Linear Models or equivalent. Students meet weekly for 1~2 hour with the course instructor for discussion on their projects and are required to have regular meetings with an assigned faculty advisor and/or consultee(s), if applicable. Students are expected to work individually or in a team of 2~3 on actual data analysis. In addition, there is weekly reading assignment. The workload will be at least 9 hours per week including all activities (classes, meetings, readings, coding, and analysis).
 

Prerequisites:

• BSTA 512/612: Linear Models

Statistical Inference I is the first course of a two term course (BSTA 551 & 552) covering the foundations of statistical inference. It is targeted to graduate students majoring in biostatistics and other disciplines requiring an understanding of statistical theory. The course starts with a review of the probability theory that is the basis for that inference. We will then focus on principles of data reduction and estimation (frequentist and Bayesian methods). We will also introduce hypothesis testing, time permitting. The two courses must be taken in sequence.

Prerequisites

1. BSTA 550 Introduction to Probability
2. Differential and Integral calculus