Teaching
Fall 2020 STAT 490: Data Science Methods in Epidemiology - COVID-19 (I)
Instructor: Prof. Alessandro Maria Selvitella
Description
This course aims at learning, understanding, and discussing several topics related to epidemiology and specifically the COVID-19 pandemic. The main tools we will use will dig into Data Analysis at different levels and participants in the course will develop tools and analyses suitable to their individual backgrounds. The course is project-based and lectures will have a big interactive component, where everybody will discuss the progress made and pose questions to which everybody will contribute answers.
Course Objectives:
Upon completing the work of this course, you should be able to:
Understand what epidemiology is and how data science can contribute to the study of COVID-19 pandemic.
Have specific understanding of the topic chosen for your project.
Have a general understanding of the other projects.
Execute a data science project from conception to the production of a complete, novel research paper on the chosen topic.
Learn how to perform a formal scientific review.
Grade Breakdown:
Participation: 20%
Presentations: 20%
Final Project: 50%
Peer Review: 10%
Schedule (tentative)
Week 1: Overview of COVID-19.
Week 2: What is Epidemiology?
Week 3: Supervised vs Unsupervised Learning.
Week 4: Linear and Nonlinear Regression.
Week 5: Classification and Clustering.
Week 6: Bootstrap and Uncertainty Estimation.
Week 7: Generalization and Cross-Validation.
Week 8: Time Series models.
Week 9: Ex. Socio-Economic Factors of COVID-19.
Week 10: ODE - Compartmental Models.
Week 11: More advanced mathematical models.
Week 12: Examples of mathematical models for COVID-19.
Week 13: Biology of COVID-19 (ex. Virology, Genetic Studies, etc…).
Week 14 - 15: Project Presentations.
Team & Co-Coaches
Prof. Alessandro Maria Selvitella (PFW)
Prof. Kathleen Foster (BSU)
Prof. Peter Dragnev (PFW)
Spring 2021 STAT 490: Data Science Methods in Epidemiology - COVID-19 (II)
Instructor: Prof. Alessandro Maria Selvitella
Description
This course aims at learning, understanding, and discussing several topics related to epidemiology and specifically the COVID-19 pandemic. The main tools we will use will dig into Data Analysis at different levels and participants in the course will develop tools and analyses suitable to their individual backgrounds. The course is project-based and lectures will have a big interactive component, where everybody will discuss the progress made and pose questions to which everybody will contribute answers. Some of the lectures will be given by world-renowned researchers in the fields of epidemiology, statistics, mathematics, and AI and by government representatives.
Course Objectives:
Upon completing the work of this course, you should be able to:
Understand what epidemiology is and how data science can contribute to the study of COVID-19 pandemic.
Have specific understanding of the topic chosen for their project.
Have a general understanding of the other projects.
Execute a data science project from conception to the production of a complete, novel research paper on the chosen topic.
Learn how to perform a formal scientific review.
Grade Breakdown:
Participation: 20%
Presentations: 20%
Final Project: 50%
Peer Review: 10%
Schedule (tentative)
Week 1: Overview of COVID-19 and Epidemiology .
Week 2: Research Seminar 1.
Week 3: Introduction to Statistical Learning and R software.
Week 4: Regression and Classification.
Week 5: Research Seminar 2.
Week 6: Uncertainty Estimation and Generalization.
Week 7: Research Seminar 3.
Week 8: Autoregressive and Moving Average Models.
Week 9: Research Seminar 4.
Week 10: Spatio-Temporal Models.
Week 11: Research Seminar 5.
Week 12: ODE - Compartmental Models.
Week 13: Research Seminar 6.
Week 14 - 15: Project Presentations (Virtual Conference open to the public).