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Activity Number: 217
Type: Invited
Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
Sponsor: Quality and Productivity Section
Abstract #314382 View Presentation
Title: On Becoming a Data Scientist: From Novice to Expert
Author(s): Joel B. Greenhouse*
Companies: Carnegie Mellon University
Keywords: Statistical Thinking ; Learning Sciences ; Guided Practice ; Communication Skills

How to teach good statistical thinking is the central challenge in training students for work as statisticians in industry. Good statistical thinking requires a nontrivial understanding of the real-world problem and the population for whom the research question is relevant. It involves judgments such as those about the relevance and representativeness of the data, about whether the underlying model assumptions are valid for the data at hand and about causality and the role of confounding variables as possible alternative explanations for observed results. Finally, an essential component of good statistical thinking is the ability to interpret and communicate the results of a statistical analysis so nonstatisticians can understand the findings.

In this presentation I will discuss how Carnegie Mellon's Master's of Statistical Practice program, using principles from the learning sciences, is training students to think and act like expert data scientist.

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

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