Available courses

Survival Analysis 091622

Aim of Course:

This course describes the various methods used for modeling and evaluating survival data, also called time-to-event data.

Introduction to Bayesian Computing & Techniques 082622

Aim of Course:

Participants in this course will learn why Bayesian computing has gained wide popularity, and how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.

Discrete Choice Modeling and Conjoint Analysis 082622

Aim of Course:

After taking this course, participants will be able to design appropriate conjoint and choice studies, using surveys, panels, and designed experiments. They will also be able to analyze and interpret the resulting data.

Friends of Data Science 2 082222

Aim of Course:


Introduction to Data Literacy 081922

Aim of Course:

This course covers how to read, understand, manipulate, and use data. There is no prerequisite knowledge for this course, but it does require access to Excel.

Responsible Data Science 081922

Aim of Course:

This course covers how to develop and implement data science projects in a responsible and ethical way. It includes a review of predictive modeling, but some prior familiarity is helpful.

Friends of Data Science 3 Optimization 081522

Aim of Course:


Friends of Data Science 3 ML-Ops and Cloud Tools

Aim of Course:

This course will teach you how to build and monitor a machine learning pipeline, given a predictive model that has been provided by a data science team.

ESEN: PA 2 R 081222

Aim of Course:

This course continues the work of Predictive Analytics 1, and introduces you to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

Introduction to Item Response Theory 081222

Aim of Course:

This course will teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks.

All courses