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

Multinational perspectives on information technology from academia and industry

As the term \u27information technology\u27 has many meanings for various stakeholders and continues to evolve, this work presents a comprehensive approach for developing curriculum guidelines for rigorous, high quality, bachelor\u27s degree programs in information technology (IT) to prepare successful graduates for a future global technological society. The aim is to address three research questions in the context of IT concerning (1) the educational frameworks relevant for academics and students of IT, (2) the pathways into IT programs, and (3) graduates\u27 preparation for meeting future technologies. The analysis of current trends comes from survey data of IT faculty members and professional IT industry leaders. With these analyses, the IT Model Curricula of CC2005, IT2008, IT2017, extensive literature review, and the multinational insights of the authors into the status of IT, this paper presents a comprehensive overview and discussion of future directions of global IT education toward 2025

Industry and faculty surveys call for increased collaboration to prepare information technology graduates

Academic and industry collaborations can help improve computing curricula and student learning experiences. Such collaborations are formally encouraged by accreditation standards. Through the auspices of ACM and IEEE-CS, the IT2017 task group is updating curriculum guidelines for information technology undergraduate degree programs, similar to the regular updates for other computing disciplines. The task group surveyed curriculum preferences of both faculty and industry. The authors, with the group\u27s cooperation, compare US faculty and US industry preferences in mathematics, IT knowledge areas, and student workplace skill sets. Faculty and industry share common ground, which supports optimism about their productive collaboration, but are also distinct enough to justify the effort of actively coordinating with each other

Skills and Knowledge for Data-Intensive Environmental Research.

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap

Latin American perspectives to internationalize undergraduate information technology education

The computing education community expects modern curricular guidelines for information technology (IT) undergraduate degree programs by 2017. The authors of this work focus on eliciting and analyzing Latin American academic and industry perspectives on IT undergraduate education. The objective is to ensure that the IT curricular framework in the IT2017 report articulates the relationship between academic preparation and the work environment of IT graduates in light of current technological and educational trends in Latin America and elsewhere. Activities focus on soliciting and analyzing survey data collected from institutions and consortia in IT education and IT professional and educational societies in Latin America; these activities also include garnering the expertise of the authors. Findings show that IT degree programs are making progress in bridging the academic-industry gap, but more work remains

Framework to Enhance Teaching and Learning in System Analysis and Unified Modelling Language

Cowling, MA ORCiD: 0000-0003-1444-1563; Munoz Carpio, JC ORCiD: 0000-0003-0251-5510Systems Analysis modelling is considered foundational for Information and Communication Technology (ICT) students, with introductory and advanced units included in nearly all ICT and computer science degrees. Yet despite this, novice systems analysts (learners) find modelling and systems thinking quite difficult to learn and master. This makes the process of teaching the fundamentals frustrating and time intensive. This paper will discuss the foundational problems that learners face when learning Systems Analysis modelling. Through a systematic literature review, a framework will be proposed based on the key problems that novice learners experience. In this proposed framework, a sequence of activities has been developed to facilitate understanding of the requirements, solutions and incremental modelling. An example is provided illustrating how the framework could be used to incorporate visualization and gaming elements into a Systems Analysis classroom; therefore, improving motivation and learning. Through this work, a greater understanding of the approach to teaching modelling within the computer science classroom will be provided, as well as a framework to guide future teaching activities

Scaffolded autoethnography: a method for examining practice-to-research

Teachers often perceive educational research as confusing and can be disenfranchised by the research process. We propose scaffolded authethnography as a method to support principled examination of authentic practice. The approach is appealing because it is motivated by the teacher’s own day-to-day practice in a research context. Our demonstration of this method uses an analytical autoethnographic approach coupled with a data capture tool that documents the pedagogic content knowledge of a practicing teacher. We include a short case-study description where the method was used in the context of research in the area of threshold concept identification