Courses Taught by Matt Zawistowski

BIOSTAT499: Transforming Analytical Learning in the Era of Big Data

  • Undergraduate level
  • Spring-Summer term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Mukherjee, Bhramar Zawistowski, Matt
  • 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.
  • Course Goals: The overarching goal of the summer institute is to recruit and train the next generation of big data scientists using a non-traditional, action-based learning paradigm. Specifically, the course goals are to: 1) Teach a select group of STEM undergraduate students selected topics in statistics, biostatistics, computer science, and information science necessary to raise the skills and interest of students to a sufficient starting level to consider pursuing graduate studies in 'Big Data' science. 2) Mentor small groups of students focused on a specialized research topic in big data in the biomedical sciences. These small groups will bring together students from various undergraduate backgrounds to work on a research topic led by a faculty member of biostatistics, statistics, or computer science. 3) Disseminate teaching and research products via creation of open-source tools, lectures, problem sets.
  • 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
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Zawistowski, Matt
  • 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

PUBHLTH383: Data Driven Solutions in Public Health

  • Undergraduate level
  • Winter term(s)
  • 4 Credit Hour(s)
  • Instructor(s): Zawistowski, Matt
  • 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.
  • Course Goals: To introduce students to the importance of data in public health, including its collection, analysis, interpretations and dissemination. To provide examples of how data are used to evaluate and assess public health decisions, policy and resource allocation. To introduce students to critical thinking of public health studies and media reports.
  • 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