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Inference for Categorical Data in R

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5

(3)

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Categorical data is all around. It's in the most recent polling results, new genomics breakthroughs, and the huge amounts of data that internet businesses gather to market their products. This course will help you distinguish between signal and noise, as well as the tools to determine when data can be used as a source for interesting phenomena or random noise.

Course Overview

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Virtual Labs

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International Faculty

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Case Based Learning

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Post Course Interactions

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Case Studies,Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

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Foundations of Inference

What You Will Learn

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In this course you'll learn how to leverage statistical techniques for working with categorical data

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In this course you'll learn techniques for parsing the signal from the noise; tools for identifying when structure in this data represents interesting phenomena and when it is just random noise

Course Instructors

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Andrew Bray

Assistant Professor of Statistics at Reed College

Andrew Bray is an assistant professor of statistics at Reed College. His interests are in computing, differential privacy, environmental statistics, and statistics education. He is a co-author of the infer package for tidy statistical inference.

Course Reviews

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