Classroom Resource Materials- Data Science

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Bol Motivated by the rapid growth of interest in data science in recent years, many faculty are seeking ways to incorporate data-related concepts into their existing curricula. This book presents a collection of classroom-tested activities that enable instructors to integrate meaningful, data-rich experiences into their mathematics courses. Each module has been carefully designed for seamless incorporation into courses such as finite mathematics, calculus, and linear algebra. Through these activities, students encounter mathematical concepts in authentic, data-driven contexts. Many of the featured scenarios are inspired by real-world problems, such as determining optimal locations for concession stands in a theme park or analyzing the fairness of NFL overtime rules. Each chapter discusses the motivation and structure of the activity, the tools and techniques used to address the problem, and pedagogical considerations for implementation. In addition, every module includes guidance on locating or generating the relevant datasets. Quickstart boxes at the beginning of each chapter make it easy to identify the key ideas and essential components of each module at a glance. To maximize flexibility, student prerequisites have been intentionally kept to a minimum. Instructors are not required to have prior experience or training in data science.

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Motivated by the rapid growth of interest in data science in recent years, many faculty are seeking ways to incorporate data-related concepts into their existing curricula. This book presents a collection of classroom-tested activities that enable instructors to integrate meaningful, data-rich experiences into their mathematics courses. Each module has been carefully designed for seamless incorporation into courses such as finite mathematics, calculus, and linear algebra. Through these activities, students encounter mathematical concepts in authentic, data-driven contexts. Many of the featured scenarios are inspired by real-world problems, such as determining optimal locations for concession stands in a theme park or analyzing the fairness of NFL overtime rules. Each chapter discusses the motivation and structure of the activity, the tools and techniques used to address the problem, and pedagogical considerations for implementation. In addition, every module includes guidance on locating or generating the relevant datasets. Quickstart boxes at the beginning of each chapter make it easy to identify the key ideas and essential components of each module at a glance. To maximize flexibility, student prerequisites have been intentionally kept to a minimum. Instructors are not required to have prior experience or training in data science.


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Merk American Mathematical Society
EAN
  • 9781470477950
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