Courses Taught by Michele Peruzzi

BIOSTAT629: Case Studies In Health Big Data

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 3 credit hour(s) for residential students;
  • Instructor(s): Erin Craig, Michele Peruzzi, (Residential);
  • Prerequisites: Biostatistics or Health Data Science students only
  • Description: Being a project-based course, it integrates all competencies learned in HDS MS program to provide a culminating research experience. Students will work on two to three health big data projects, through which they learn to identify scientific objectives and analytical strategies and report findings through oral presentation and written documents.
  • Learning Objectives: Students will learn how to identify a scientific goal of the project and to develop analytic strategies. Students will learn to integrate and apply quantitative skills to handle real-world health big data, including data modification and cleaning, data visualization and scalable computing. From presentations, students will improve their communication skills.
  • Syllabus for BIOSTAT629
CraigErin
Erin Craig
PeruzziMichele
Michele Peruzzi
Concentration Competencies that BIOSTAT629 Allows Assessment On
Department Program Degree Competency Specific course(s) that allow assessment
BIOSTAT Health Data Science MS Apply quantitative techniques commonly used to summarize and display big public health data BIOSTAT629
BIOSTAT Health Data Science MS Apply descriptive and inferential methodologies according to the type of study design or sampling technique for answering a particular public health question BIOSTAT629

BIOSTAT696: Spatial Statistics

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 3 credit hour(s) for residential students;
  • Instructor(s): Michele Peruzzi (Residential);
  • Prerequisites: BIOSTAT 601, BIOSTAT 602, BIOSTAT 650, BIOSTAT 653
  • Description: This course will introduce the theory and methods of spatial and spatio-temporal statistics. It will present spatial and spatio-temporal statistical models and will discuss methods for inference on spatial processes within a geostatistical and a hierarchical Bayesian framework.
  • Syllabus for BIOSTAT696
PeruzziMichele
Michele Peruzzi

BIOSTAT815: Advanced Topics in Computational Statistics

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 3 credit hour(s) for residential students;
  • Instructor(s): Michele Peruzzi (Residential);
  • Prerequisites: BIOSTAT601, BIOSTAT602 and BIOSTAT615 or equiv and proficiency in C++ and R
  • Description: Modern numerical analysis for statisticians. Combination of theory and practical computational examples illustrating the current trends in numerical analysis relevant to probability and statistics. Topics choose from numerical linear algebra, optimization theory, quadrature methods, splines, and Markov chains. Emphasis on newer techniques such as quasi-random methods of integration, the EM algorithm and its variants, and hidden Markov chains. Applications as time permits to areas such as genetic and medical imaging.
  • Syllabus for BIOSTAT815
PeruzziMichele
Michele Peruzzi

BIOSTAT896: Spatial Statistics

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 3 credit hour(s) for residential students;
  • Instructor(s): Michele Peruzzi (Residential);
  • Prerequisites: BIOSTAT 601, BIOSTAT 602, BIOSTAT 650, BIOSTAT 653
  • Description: This course will introduce the theory and methods of spatial and spatio-temporal statistics. It will present spatial and spatio-temporal statistical models and will discuss methods for inference on spatial processes within a geostatistical and a hierarchical Bayesian framework.
  • Syllabus for BIOSTAT896
PeruzziMichele
Michele Peruzzi