数据科学教育的未来

Brian Wright, Peter Alonzi, Ali Riveria
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摘要

数据科学的定义是一个备受争议的话题。然而,数据科学领域的深度和细微差别远非简单的捷径所能比拟。弗吉尼亚大学数据科学学院为数据科学的定义开发了一个新颖的模型。该模型基于对数据科学所有领域数据工作的统一理解。它代表了我们在如何理解和教授数据科学方面的一次飞跃。在本文中,我们将介绍该模型的核心特征,并解释它是如何统一各种概念,远远超出人工智能的分析部分。在此基础上,我们将介绍我们的数据科学本科专业课程,并展示它是如何培养学生成为全面的数据科学团队成员和领导者的。本文最后将深入概述数据科学基础课程,该课程旨在向学生介绍该领域,同时还采用了经过验证的、以 STEM 为导向的教学方法。例如,这些方法包括规范评分、主动学习讲座、行业专家客座讲座和每周游戏化实验。
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The Future of Data Science Education
The definition of Data Science is a hotly debated topic. For many, the definition is a simple shortcut to Artificial Intelligence or Machine Learning. However, there is far more depth and nuance to the field of Data Science than a simple shortcut can provide. The School of Data Science at the University of Virginia has developed a novel model for the definition of Data Science. This model is based on identifying a unified understanding of the data work done across all areas of Data Science. It represents a generational leap forward in how we understand and teach Data Science. In this paper we will present the core features of the model and explain how it unifies various concepts going far beyond the analytics component of AI. From this foundation we will present our Undergraduate Major curriculum in Data Science and demonstrate how it prepares students to be well-rounded Data Science team members and leaders. The paper will conclude with an in-depth overview of the Foundations of Data Science course designed to introduce students to the field while also implementing proven STEM oriented pedagogical methods. These include, for example, specifications grading, active learning lectures, guest lectures from industry experts and weekly gamification labs.
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