Courses Taught by Matt Zawistowski

BIOSTAT449: Topics In Biostatistics

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 3 Credit Hour(s) for residential students;
  • Instructor(s): Zawistowski, Matt (Residential);
  • Prerequisites: Statistics 401 or permission of instructor
  • Description: This course will make use of case studies to discuss problems and applications of biostatistics. Topics will include cohort and case control studies, survival analysis with applications in clinical trials, evaluation of diagnostic tests, and statistical genetics. The course will conclude with a survey of areas of current biostatistical research.
  • This course is cross-listed with Statistics 449 in the Literature, Science and the Arts department.

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.

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 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) term for online MPH students; term(s) term for online MS students.
  • 2 Credit Hour(s) for residential students; 2 Credit Hour(s) for online MPH students for residential 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

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