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.

翻译：数据科学的定义是一个备受争议的话题。对许多人而言，该定义仅是人工智能或机器学习的简单代称。然而，数据科学领域的深度与细微之处远非一个简单的代称所能涵盖。弗吉尼亚大学数据科学学院提出了一种新颖的数据科学定义模型。该模型基于对数据科学各领域数据工作的统一理解而构建，代表了我们在理解和教授数据科学方式上的代际飞跃。本文将阐述该模型的核心特征，并解释其如何统一远超人工智能分析范畴的各类概念。基于此基础，我们将介绍数据科学本科专业课程体系，展示其如何培养学生成为全面发展的数据科学团队成员与领导者。文章最后将深入概述"数据科学基础"课程，该课程旨在引导学生入门该领域，同时实施经过验证的STEM导向教学法，例如：规范评分制、主动式授课、行业专家客座讲座以及每周游戏化实验环节。