Shannon Ellis

Shannon Ellis is an Assistant Teaching Professor in the Cognitive Science Department at UC San Diego where she teaches data science and programming to undergraduates. Shannon is particularly passionate about data science, data science ethics, pedagogy, and education. She aims to ensure that data science education is accessible to everyone, with a particular focus on individuals from marginalized groups who typically have not had access to such materials and training.

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Modeling Data in the Tidyverse (Coursera)

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 [...]

Visualizing Data in the Tidyverse (Coursera)

Data visualization is a critical part of any data science project. Once data have been imported and wrangled into place, visualizing your data can help you get a handle on what’s going on in the data set. Similarly, once you’ve completed your analysis and are ready to present your [...]

Wrangling Data in the Tidyverse (Coursera)

Data never arrive in the condition that you need them in order to do effective data analysis. Data need to be re-shaped, re-arranged, and re-formatted, so that they can be visualized or be inputted into a machine learning algorithm. This course addresses the problem of wrangling your data so [...]

Importing Data in the Tidyverse (Coursera)

Getting data into your statistical analysis system can be one of the most challenging parts of any data science project. Data must be imported and harmonized into a coherent format before any insights can be obtained. You will learn how to get data into R from commonly used formats [...]

Introduction to the Tidyverse (Coursera)

This course introduces a powerful set of data science tools known as the Tidyverse. The Tidyverse has revolutionized the way in which data scientists do almost every aspect of their job. We will cover the simple idea of "tidy data" and how this idea serves to organize data for [...]