Artificial intelligence integrates with our daily life in more ways each day with the invention of new tools and applications. For example, businesses utilize machine learning, which is a subfield of artificial intelligence, to identify the ideal customer for targeted advertisement. Machine learning and data science scale previous manual problems to a wider customer base. With all the various applications for artificial intelligence, there is still a noticeable gap in the number of qualified job applicants who can solve some of the machine learning problems of the day. There is a movement to introduce computer science education earlier to K-12 students. One motivation behind an earlier introduction is to better prepare students to study computer science at universities and fulfill these coveted technology positions in the future. Given this motivation, there needs to be more computer science instructional activities and lesson plans to support K-12 teachers. Currently there are several new tools and curricula available for computer science. However, there is a need for beginner and K-12 accessible data science and machine learning instructional methods. These topics are advanced computer science electives but an earlier introduction can still benefit students. Interactive visual activities support high school students to learn challenging topics. In this paper, we consider how a visual analytic process can present difficult data science concepts. We present an interactive visual educational tool to teach data science to high school students. The first activity introduces the visual analytic pipeline. The next interactive activities present machine learning classification and regression topics. We select datasets and applications which are relatable to our main audience. This paper describes the design and implementation of the data science educational tool for high school students. CCS CONCEPTS • Applied computing → Interactive learning environments.

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