Courses Taught by Matt Zawistowski

BIOSTAT499: Transforming Analytical Learning in the Era of Big Data

  • Undergraduate level
  • Residential
  • Spring-Summer term(s) for residential students;
  • 3 credit hour(s) for residential students;
  • Instructor(s): Mukherjee, Bhramar Zawistowski, Matt (Residential);
  • Prerequisites: Admission to BDSI Program
  • Description: The "Transforming Analytical Learning in the Era of Big Data" course is a a six week undergraduate summer program that exposes students to diverse techniques, skills and problems in the field of Big Data and Human Health. Students receive a broad and interdisciplinary introduction to statistical theory and concepts during morning lectures led by faculty from across campus. Afternoons are spent in small faculty-mentored research groups analyzing real big datasets to address focused research questions. The course also includes professional development designed to prepare students for the graduate school application process.
  • Learning Objectives: At the conclusion of the course, students will have the skills required to pursue graduate studies in Big Data science.

BIOSTAT501: Introduction to Biostatistics

  • Graduate level
  • Residential and Online MPH and Online MS
  • This is a first year course for Online students
  • Fall term(s) for residential students; Winter term(s) for online MPH students; Spring-Summer term(s) for online MS students.
  • 3 credit hour(s) for residential students; 2 credit hour(s) for online MPH students; 3 credit hour(s) for online MS students;
  • Instructor(s): Zhao, Lili Zawistowski, Matt Zhou, Xiang (Residential); Braun, Thomas (Online MPH); Braun, Thomas (Online MS);
  • Prerequisites: SPH MPH or permission of instructor
  • Description: Statistical methods and principles necessary for understanding and interpreting data used in public health and policy evaluation and formation. Topics include descriptive statistics, graphical data summary, sampling, statistical comparison of groups, correlation, and regression. Students will learn via lecture, group discussions, critical reading of published research, and analysis of data.
  • This course is required for the school-wide core curriculum
  • Residential Syllabus for BIOSTAT501
Concentration Competencies that BIOSTAT501 Allows Assessment On
Department Program Degree Competency Specific course(s) that allow assessment
Population and Health Sciences MPH Compare population health indicators across subpopulations, time, and data sources PUBHLTH515, BIOSTAT592, EPID590, EPID592, EPID643, BIOSTAT595, BIOSTAT501
Population and Health Sciences MPH Estimate population health indicators from high quality data resources from diverse sources PUBHLTH515, EPID643, NUTR590, BIOSTAT592, BIOSTAT501

BIOSTAT521: Applied Biostatistics

  • Graduate level
  • Residential
  • Fall term(s) for residential students;
  • 3 credit hour(s) for residential students;
  • Instructor(s): Zawistowski, Matt (Residential);
  • Prerequisites: Calculus
  • Description: Biostatistical analysis provides the means to identify and verify patterns in this data and to interpret the findings in a public health context. In this course, students will learn the basic steps in analyzing public health data, from initial study design to exploratory data analysis to inferential statistics. Specifically, we will cover descriptive statistics and graphical representations of univariate and multivariate data, hypothesis testing, confidence intervals, t-tests, analysis of contingency tables, and simple and multiple linear regression.
  • This course is required for the school-wide core curriculum
  • Syllabus for BIOSTAT521
Concentration Competencies that BIOSTAT521 Allows Assessment On
Department Program Degree Competency Specific course(s) that allow assessment
NUTR MS Analyze quantitative data using biostatistics, informatics, computer-based programming and software, as appropriate BIOSTAT521

BIOSTAT595: Applied Longitudinal Analysis Using R

  • Graduate level
  • Residential and Online MPH and Online MS
  • This is a first year course for Online students
  • Fall term(s) for residential students; Fall term(s) for online MPH students; term(s) for online MS students.
  • 2 credit hour(s) for residential students; 2 credit hour(s) for online MPH students; 2 credit hour(s) for online MS students;
  • Instructor(s): Zawistowski, Matt (Residential); Zawistowski, Matt (Online MPH); Zawistowski, Matt (Online MS);
  • Prerequisites: Biostat 501, Biostat 591, Biostat 592
  • Description: This course provides an overview of statistical methods for analyzing correlated data produced by longitudinal measurements taken over time. Topics include study design, exploratory data analysis techniques and linear mixed effects regression models. This course provides practical concepts and hands-on R computing skills to perform longitudinal data analysis.
  • Learning Objectives: 1. Identify causes and patterns of correlated outcomes in health data 2. Perform exploratory data analysis of longitudinal outcomes 3. Fit linear mixed effects regression models 4. Interpret and perform hypothesis testing of regression parameters for mixed models
Concentration Competencies that BIOSTAT595 Allows Assessment On
Department Program Degree Competency Specific course(s) that allow assessment
Population and Health Sciences MPH Compare population health indicators across subpopulations, time, and data sources PUBHLTH515, BIOSTAT592, EPID590, EPID592, EPID643, BIOSTAT595, BIOSTAT501

BIOSTAT834: Pedagogical Methods For Biostatistics Courses

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 2 credit hour(s) for residential students;
  • Instructor(s): Zawistowski, Matt (Residential);
  • Prerequisites: None
  • Advisory Prerequisites: Biostatistics PhD Candidacy or permission of instructor
  • Description: Biostatistics faculty teach a wide range of courses over diverse student backgrounds, presenting distinct challenges and considerations. This course will develop ideas and skills for building biostatistics courses tailored for specific student groups and course levels. It will prepare PhD students for their first teaching assignment in a faculty position.
  • Learning Objectives: 1. Understand how the audience of a specific course impacts course design, content and learning objectives. 2. Implement the concepts of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report into course planning. 3. Become familiar with technology resources to increase active learning techniques (e.g. Poll Everywhere) and improve course management (Canvas). 4. Develop teaching skills that transcend biostatistics: inclusive teaching practices, mentoring and conflict resolution.

PUBHLTH383: Data Driven Solutions in Public Health

  • Undergraduate level
  • Residential
  • Winter term(s) for residential students;
  • 4 credit hour(s) for residential students;
  • Instructor(s): Zawistowski, Matt (Residential);
  • Prerequisites: STAT250; PUBHLTH 200
  • Description: This course introduces the importance of data in public health, including collection, analysis, interpretations, and dissemination. It provides examples of data used to evaluate public health decisions, policy and resource allocation. It is an introduction to biostatistical and epidemiological methods, informatics, and big data including usage, management and challenges.
  • Learning Objectives: 1)Identify key strategies and methods for obtaining current public health data 2)Explain the use of basic epidemiological methods in study design and implementation to generate new data and metrics to address public health issues 3)Illustrate how analyses and results are used to inform intervention development and influence appropriate public health policies. 4)Apply statistical methods in order to describe data, make inferences and test hypotheses regarding population parameters 5)Apply results from data analyses to explore, define, identify and prioritize public health challenges and solutions
  • Syllabus for PUBHLTH383