This is a session of Topic 3: Statistics education at the post-secondary level

Session 3A (Tuesday 10th, 14:00-15:30,   Terrsa Hall)

Developing undergraduate data science programs grounded in statistics

Organizer

• Nicholas Horton (United States)

Abstract

As a result of accelerating technological developments, larger bodies of available data, and increased interest in modeling and quantification, statistics is understood and taught quite differently today than it was in the 1990s. The rise of data science, representing an integration of data management, statistics, machine learning, and distributed computation will require additional changes. Statistics plays an important role in data science because it allows questions to be framed in a way that encourages better use of the data, inferences to aid in quantifying uncertainty, interventions to be identified by distinguishing between causation and correlation, methods to be used for prediction and estimation, and findings to be reproducible. This session will explore post-secondary courses and programs intended to ensure that statistics is a key component of data science education. The first speaker will consider data science as a general education course for all students. The second speaker will describe models for introductory data science courses, while the last speaker will discuss a variety of undergra This will be a set of 3 papers plus discussant.duate data science degree programs. These integrated presentations will focus on ways that statistical concepts and learning outcomes are tightly woven into future data science offerings.

Papers