Analytics in the Business School: Insights from the Literature

The demand for business and data analysts is growing. The business school is well positioned to offer programs to meet these needs. This paper presents both the findings from a review of the existing literature on data analytics job roles, skills required for those roles and also feedback from industry experts on findings. Three different types of articles are included in the design: faculty writing about their personal experiences and observations (faculty voice), data gathered from expert practitioners and other academics (nonresident expertise), and empirical data from online job service platforms (content analysis). The narrative review method is used to integrate these disparate sources of information and deliver cohesive observations. This knowledge can be used to build better analytics programs in business schools

IS Programs Responding to Industry Demands for Data Scientists: A Comparison Between 2011-2016

The term data scientist has only been in common use since 2008, but in 2016 it is considered one of the top careers in the United States. The purpose of this paper is to explore the growth of data science content areas such as analytics, business intelligence, and big data in AACSB Information Systems (IS) programs between 2011 and 2016. A secondary purpose is to analyze the effect of IS programs’ adherence to IS 2010 Model Curriculum Guidelines for undergraduate MIS programs, as well as the impact of IS programs offering an advanced database course in 2011 on data science course offerings in 2016. A majority (60%) of AACSB IS programs added data science-related courses between 2011 and 2016. Results indicate dramatic increases in courses offered in big data analytics (583%), visualization (300%), business data analysis (260%), and business intelligence (236%). ANOVA results also find a significant effect of departments offering advanced database courses in 2011 on new analytics course offerings in 2016. A Chi-Square analysis did not find an effect of IS 2010 Model Curriculum adherence on analytics course offerings in 2016. Implications of our findings for an MIS department’s ability to respond to changing needs of the marketplace and its students are discussed

Building a Business Data Analytics Graduate Certificate

In this paper we present the evolution of the Business Data Analytics Graduate Certificate (BDA Certificate) at our institution, Loyola University Chicago. This certificate is a successful and expanding program that attracts a diverse group of dynamic professionals from local, national, and international populations. The program evolution described in this paper involves multiple revisions of the curriculum, additions, and subtractions of individual courses, expansions of delivery methods, and program name changes. The core principles of acknowledging the centrality of data, mandating the modeling-based course sequencing, and recognizing the proper role of software tools, are outlined and recognized as the foundation of the program’s success

Information Technology and Computing Topics and Their Relevance to Medical Undergraduate and Graduate Program Curricula at RIT

Two healthcare domain related programs in which this author has curricular relationships are the undergraduate Diagnostic Ultrasound (DU), and the graduate Master of Science in Health Informatics (MSHI). He teaches one course in the former and is the program coordinator for the latter. The undergraduate course is titled, “Computers in Medicine”, and is a rough 50% combination of a first-semester computing hardware course taught to our IT undergrads and another 50% of material from a textbook covering all the ways in which computing has benefitted various healthcare domains like, surgery, pharmacy, imaging, dentistry, psychiatry, remote medicine and the like. The MSHI program is a 30 semester credit hour program offered in an online format with a capstone experience (no thesis required) that was designed for professionals expecting to retool themselves for continued employment in a healthcare setting. This paper will discuss the details of the DU course and the MSHI program, the kind of computing content covered in each, and the rationale for and program design input of each. In conclusion, the reader will be left with an understanding of the what, when, how and why computing topics are necessarily required by these curricula, our justification for such, and how we might use that information in the development of future healthcare-related computing courses and potential programs. Course definition and program outline documents will be attached as appendices to the paper

Using Active Learning, Group Formation, and Discussion to Increase Student Learning: A Business Intelligence Skills Analysis

This paper describes the process used to integrate active learning, group formation, and classroom discussion in a college-level business intelligence class. To assess the impact of active learning and discussion on learning outcomes, we captured student performance on their final data challenge term project across increasingly collaborative and discussion-based sections. To stimulate reflective discussion and to promote cooperative and collaborative teamwork during in-class assignments, we established small groups based on an incoming business intelligence-related skills self-assessment. Our regression results indicate that a skills-based group formation approach enabled an enhanced level of in-class assignment completion and promoted reflective discussion in the classroom. We also find that active learning and discussion increased appropriation of business intelligence concepts and analytical tools. The inherent nuances of business intelligence education, as well as the implications and strategies for improved classroom discussion in a technology class setting, are reviewed

Virginia Digital Shipbuilding Program (VDSP): Building an Agile Modern Workforce to Improve Performance in the Shipbuilding and Ship Repair Industry

Industry 4.0 is the latest stage in the Industrial Revolution and is reflected in the digital transformation and use of emergent technologies including the Internet of Things, Big Data, Robotic automation of processes, 3D printing and additive manufacturing, drones and Artificial Intelligence (AI) in the manufacturing industry. The implementation of these technologies in the Shipbuilding and Ship Repair Industry is currently in a nascent stage. Considering this, there is huge potential to increase cost savings, decrease production timelines, and drive down inefficiencies in Lifecyle management of ships. However, the implementation of these Industry 4.0 technologies is hindered by a noticeable gap in workforce capability and capacity. The shipbuilding and ship repair industry is projected to lose approximately 33% of skilled workforce and 48% of management by 2028. With an aging workforce and an incoming digital generation that excels in tech savviness, flexibility, global thinking, and multi-tasking it is crucial to be innovative in workforce development. The Virginia Digital Shipbuilding Program responds to this need by providing a process and platform to address education, training, adoption of innovative new technology and the ability to provide real-time solutions to current and future industry problems. This paper will focus on the three pillars of Digital Shipbuilding – Career Pathway Mapping and Curriculum Development, Outreach and Workforce Development, and Research and Development. Additionally, this paper will address how the team is ensuring that stackable, transferable education and certification processes are implemented between military and industry to facilitate the transition of veterans to the civilian workforce

Industry 4.0 enabling sustainable supply chain development in the renewable energy sector:A multi-criteria intelligent approach

The aim of this paper is to provide a multi-criteria decision-making intelligent approach based on Industry 4.0 and Triple Bottom Line principles for sustainable supply chain development in the renewable energy sector. In particular, the solar photovoltaic energy supply chain is used as a case study, encompassing the entire energy production process, from supply to disposal. An exhaustive literature review is conducted to identify the main criteria affecting social, economic and environmental sustainability in the photovoltaic energy supply chain, and to explore the potential impact of Industry 4.0 on sustainability. Subsequently, three Fuzzy Inference Systems combining quantitative and qualitative data are built to calculate the supply chain's social, economic and environmental sustainability. Experts' opinions are used to identify the impact of Industry 4.0 technologies on the three pillars of sustainability for each supply chain stage. Finally, a novel sustainability index, Sustainability Index 4.0, is formulated to compute the overall sustainability of the photovoltaic energy supply chain in seven countries. The results show the applicability and usefulness of the proposed holistic model in helping policy makers, stakeholders and users to make informed decisions for the development of sustainable renewable energy supply chains, taking into account the impact of Industry 4.0 and digital technologies

An Investigation Dimension for Understanding and Characterizing Computing Disciplines

Computing disciplines are diverse and overlap extensively. ACM provides two dimensions, theory and target level, as a tool to describe the problem spaces of five disciplines of computing: computer science, information systems, information technology, computer engineering, and software engineering. However, there are still many studies reporting that even majors are not entirely clear about the scopes and tasks of their computing disciplines. Various supplementary approaches and models have been proposed to assist the understanding and characterization of computing disciplines, such as through computing traditions, research-focuses, and positions in the business-technology continuum. This paper proposes a new investigation dimension based on a popular inquiry approach as a complementary third dimension to serve as an additional high order lens for understanding computing disciplines. The application of the model on understanding and characterizing the five ACM disciplines and data science is discussed. The model encourages systematic critical thinking, meaningful learning, and deep reasoning