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Abstract Details

Activity Number: 179
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Education
Abstract #322914
Title: Data Science Tutorials
Author(s): Shonda Kuiper* and Laura Chihara and Adam Loy
Companies: Grinnell College and Carleton College and Lawrence University
Keywords: Data Science ; RMarkdown ; Online ; Statistics Education ; Blended Course
Abstract:

This project describes a collaborative work across three institutions to develop, implement, and evaluate a series of tutorials and interdisciplinary case studies that incorporate data science and modern statistical methods into the undergraduate curriculum. These materials were successfully incorporated into a variety of blended as well as brick and mortar courses. We believe these activities provide a new pedagogical model for teaching students from all disciplines how to make data-based decisions with relevant, real-world data. These freely available tutorials have improved the ability of our institutions to address the ASA Curriculum Guidelines by providing materials that emphasize 1) facility with programming languages and database systems, 2) the ability to access and manipulate data in various ways, 3) the ability to perform algorithmic problem-solving, 4) experience working with complex data, and 5) the ability to communicate complex statistical methods in basic terms to diverse audiences as well as visualize results in an accessible manner.


Authors who are presenting talks have a * after their name.

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