888 research outputs found
A conceptual framework for developing dashboards for big mobility data
Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets
Teaching the Foundations of Data Science: An Interdisciplinary Approach
The astronomical growth of data has necessitated the need for educating
well-qualified data scientists to derive deep insights from large and complex
data sets generated by organizations. In this paper, we present our
interdisciplinary approach and experiences in teaching a Data Science course,
the first of its kind offered at the Wright State University. Two faculty
members from the Management Information Systems (MIS) and Computer Science (CS)
departments designed and co-taught the course with perspectives from their
previous research and teaching experiences. Students in the class had mix
backgrounds with mainly MIS and CS majors. Students' learning outcomes and post
course survey responses suggested that the course delivered a broad overview of
data science as desired, and that students worked synergistically with those of
different majors in collaborative lab assignments and in a semester long
project. The interdisciplinary pedagogy helped build collaboration and create
satisfaction among learners.Comment: Presented at SIGDSA Business Analytics Conference 201
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