Recently developed // taught courses. Click to view the course websites.
Introduction to Regression and Data Science. Learn to explore, visualize, model, evaluate, and communicate data in a reproducible manner. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.
Seminar in Mathematical Business Analysis. The main purpose of this seminar is to develop the capability to apply quantitative knowledge to real and ill-defined problems. It tries to bridge the gap between the theory of quantitative decision approaches such as management science/operations research, information systems, and statistics (now mainly collected in the Business Analytics field), with the application of these approaches to the solution of actual business problems.
Causal Inference. From Correlation to Causation. The goal of this course is to give students the skills needed to conduct analyses and communicate results when causality is the goal. Students will learn how to implement causal inference techniques including matching and weighting, evaluate assumptions, and conduct sensitivity analyses.
Introduction to Regression and Data Science. Learn to explore, visualize, model, evaluate, and communicate data in a reproducible manner. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.
Statistical learning. Learn the theory behind cutting edge statistical and machine learning techniques. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.
Seminar in Mathematical Business Analysis. The main purpose of this seminar is to develop the capability to apply quantitative knowledge to real and ill-defined problems. It tries to bridge the gap between the theory of quantitative decision approaches such as management science/operations research, information systems, and statistics (now mainly collected in the Business Analytics field), with the application of these approaches to the solution of actual business problems.
Statistical models. Learn to explore, visualize, model, evaluate, and communicate data in a reproducible manner. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.
Causal Inference. From Correlation to Causation. The goal of this course is to give students the skills needed to conduct analyses and communicate results when causality is the goal. Students will learn how to implement causal inference techniques including matching and weighting, evaluate assumptions, and conduct sensitivity analyses.
Whether a medical student reading their first journal article or a healthcare professional trying to use the latest research to improve patient care, this course will help you understand data and statistics in the medical literature in an efficient and conceptual manner.
Shinydashboards. This course teaches how to use the shinydashboard R package. By the end you should be able to build a simple dynamic dashboard!
This course aims to provide managers and developers of contact tracing programs guidance on the most important indicators of performance of a contact tracing program, and a tool that can be used to project the likely impact of improvements in specific indicators.
Statistical learning. Learn the theory behind cutting edge statistical and machine learning techniques. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.
Linear models. Learn the theory behind linear models. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.
Statistical learning. Learn the theory behind cutting edge statistical and machine learning techniques. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.
Statistical models. Learn to explore, visualize, model, evaluate, and communicate data in a reproducible manner. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.
This course was designed to teach the fundamentals of the ggplot2 R package in a quick two hours.