Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

A review into the factors affecting declines in undergraduate Computer Science enrolments and approaches for solving this problem

There has been a noticeable drop in enrolments in Computer Science (CS) courses and interest in CS careers in recent years while demand for CS skills is increasing dramatically. Not only are such skills useful for CS jobs but for all forms of business and to some extent personal lives as Information Technology (IT) is becoming ubiquitous and essential for most aspects of modern life. Therefore it is essential to address this lack of interest and skills to not only fill the demand for CS employees but to provide students with the CS skills they need for modern life especially for improving their employability and skills for further study. This report looks at possible reasons for the lack of interest in CS and different approaches used to enhance CS education and improve the appeal of CS

Computing as the 4th “R”: a general education approach to computing education

Computing and computation are increasingly pervading our lives, careers, and societies - a change driving interest in computing education at the secondary level. But what should define a "general education" computing course at this level? That is, what would you want every person to know, assuming they never take another computing course? We identify possible outcomes for such a course through the experience of designing and implementing a general education university course utilizing best-practice pedagogies. Though we nominally taught programming, the design of the course led students to report gaining core, transferable skills and the confidence to employ them in their future. We discuss how various aspects of the course likely contributed to these gains. Finally, we encourage the community to embrace the challenge of teaching general education computing in contrast to and in conjunction with existing curricula designed primarily to interest students in the field

Creative Computation for CS1 and K9-12

We present the design and development of a new approach to teaching the introductory computing course (CS1), at both the college-level as well as K9-12, using the context of digital art and creative computation. Creative computation is a highly interdisciplinary area combining theory and methodology from computer science and engineering with aesthetic principles and creative practices from the arts. Using the Processing programming language, students create a portfolio of aesthetic visual designs that employ basic programming constructs and structures typically taught in traditional CS1 courses. The goal of this approach is to bring the excitement, creativity, and innovation fostered by the context of creative coding. We have developed a web portal containing an extensive set of resources for adoption by others. A comprehensive textbook has also been published in 2013 [Greenberg et al 2013]. We present results from a comparative study involving multiple offerings of the new course at the two lead institutions as well as several other partner institutions. We also describe the success of bringing creative computation via Processing into two very different high schools that span the range of possibilities of grades 9-12 in American education. We report on how contextualized computing that supports integration of media arts, design, and computer science can successfully motivate students to learn foundations of programming and come back for more. The work of two high school teachers with divergent pedagogical styles is presented. They successfully adapted a college-level creative computation curriculum to their individual school cultures providing a catalyst for significant increases in enrollment and female participation in high school computer science

Creative Computation for CS1 and K9-12

We present the design and development of a new approach to teaching the introductory computing course (CS1), at both the college-level as well as K9-12, using the context of digital art and creative computation. Creative computation is a highly interdisciplinary area combining theory and methodology from computer science and engineering with aesthetic principles and creative practices from the arts. Using the Processing programming language, students create a portfolio of aesthetic visual designs that employ basic programming constructs and structures typically taught in traditional CS1 courses. The goal of this approach is to bring the excitement, creativity, and innovation fostered by the context of creative coding. We have developed a web portal containing an extensive set of resources for adoption by others. A comprehensive textbook has also been published in 2013 [Greenberg et al 2013]. We present results from a comparative study involving multiple offerings of the new course at the two lead institutions as well as several other partner institutions. We also describe the success of bringing creative computation via Processing into two very different high schools that span the range of possibilities of grades 9-12 in American education. We report on how contextualized computing that supports integration of media arts, design, and computer science can successfully motivate students to learn foundations of programming and come back for more. The work of two high school teachers with divergent pedagogical styles is presented. They successfully adapted a college-level creative computation curriculum to their individual school cultures providing a catalyst for significant increases in enrollment and female participation in high school computer science

A Data Science Course for Undergraduates: Thinking with Data

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students with a variety of techniques to analyze small, neat, and clean data sets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that is considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.Comment: 21 pages total including supplementary material

Computing Foundations for the Scientist

There is a need for a new style of supporting a computer course. Although it is widely recognized that computer technology provides essential tools for all current scientific work, few university curricula adequately ground science majors in the fundamentals that underlie this technology. Introducing science students to computational thinking in the areas of algorithms and data structures, data representation and accuracy, abstraction, performance issues, and database concepts can enable future scientists to become intelligent, creative and effective users of this technology. The intent of this course is not to turn scientists into computer scientists, but rather to enhance their ability to exploit computing tools to greatest scientific advantage