Modeling Data in the Tidyverse

О курсе

Developing insights about your organization, business, or research project depends on effective modeling and analysis of the data you collect. Building effective models requires understanding the different types of questions you can ask and how to map those questions to your data. Different modeling approaches can be chosen to detect interesting patterns in the data and identify hidden relationships. This course covers the types of questions you can ask of data and the various modeling approaches that you can apply. Topics covered include hypothesis testing, linear regression, nonlinear modeling, and machine learning. With this collection of tools at your disposal, as well as the techniques learned in the other courses in this specialization, you will be able to make key discoveries from your data for improving decision-making throughout your organization. In this specialization we assume familiarity with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.

Навыки, которые вы освоите

Учитесь у экспертов

Shannon Ellis, PhD

Assistant Teaching Professor, Cognitive Science

Stephanie Hicks, PhD

Assistant Professor, Biostatistics

Roger D. Peng, PhD

Professor of Statistics and Data Sciences

Carrie Wright, PhD

Senior Staff Scientist