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In the era of artificial intelligence (AI), big data (BD), and digital transformation (DT), analytics students should gain the ability to solve business problems by integrating various methods. This teaching brief illustrates how two such methods--Bayesian analysis and Markov chains--can be combined to enhance student learning using the Analytics Project Life Cycle Management (APLCM) approach and a case study involving qualitative forecasting. The theoretical frameworks for combining Bayesian and Markov methods are developed, and a forecasting solution is implemented in both MS Excel and Python. Based on an assessment of student learning, applying this pedagogical approach helps students better use these disjoint methods and appreciate the value of integrating them. Although this teaching brief is designed and most appropriate for graduate students with previous BA courses, it can also be used in upper-level courses within an undergraduate BA curriculum. Finally, this teaching brief provides the instructors wishing to use this pedagogical approach in their appropriate courses with the necessary resources (i.e., case study, in-class example, and the MS Excel and Python templates).

Recent technological advancements in data storage and processing have changed how companies conduct their business. An increasing number of firms have started putting their efforts in extracting information from their databases to improve profitability and reduce costs using quantitative approaches. Thus, the job market has been experiencing a rapidly growing demand for business analytics (BA) practitioners, and universities across the globe are increasingly responding to this newly emerged field by offering both undergraduate and graduate level degrees as well as certificate programs. Thus, this research aims to provide a framework for academic institutions to develop a state-of-the-art master's in business analytics (MSBA) curriculum by identifying concepts, skills, knowledge, and tools (CSKT) that industry seeks in BA practitioners. Our data-driven methodology utilizes peer institution analysis, indeed.com web scraping, and focus group analysis with mid- and senior-level analytics leaders from major companies. Our contribution to the literature and recommendations to institutions developing MSBA programs are offered at the end.

AACSB has now mandated that analytics be integrated into the undergraduate business curriculum. Given that the subject of statistics provides the underpinnings of the developing discipline of business analytics, this article focuses on effective course delivery aimed at enhancing the learning of introductory business statistics in the modern world of big data. To this end, the choice and use of computational software tools are essential to the successful delivery of the course. A set of seven competing tools are compared and contrasted, their advantages and disadvantages are discussed, and a link is provided to show demonstrations of needed instructions and resulting output.

Background To protect healthcare workers (HCWs) from the consequences of disease due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is necessary to understand the risk factors that drive exposure and infection within hospitals. Insufficient consideration of key socioeconomic variables is a limitation of existing studies that can lead to bias and residual confounding of proposed risk factors for infection. Methods The Co-STARs study prospectively enrolled 3679 HCWs between April 2020 and September 2020. We used multivariate logistic regression to comprehensively characterize the demographic, occupational, socioeconomic, and environmental risk factors for SARS-CoV-2 seropositivity. Results After adjusting for key confounders, relative household overcrowding (odds ratio [OR], 1.4 [95% confidence interval {CI}, 1.1–1.9]; P =.006), Black, Black British, Caribbean, or African ethnicity (OR, 1.7 [95% CI, 1.2–2.3]; P =.003), increasing age (ages 50–60 years: OR, 1.8 [95% CI, 1.3–2.4]; P <.001), lack of access to sick pay (OR, 1.8 [95% CI, 1.3–2.4]; P <.001). Conclusions Socioeconomic and demographic factors outside the hospital were the main drivers of infection and exposure to SARS-CoV-2 during the first wave of the pandemic in an urban pediatric referral hospital. Overcrowding and out-of-hospital SARS-CoV-2 contact are less amenable to intervention. However, lack of access to sick pay among externally contracted staff is more easily rectifiable. Our findings suggest that providing easier access to sick pay would lead to a decrease in SARS-CoV-2 transmission and potentially that of other infectious diseases in hospital settings. Clinical Trials Registration NCT04380896. [ABSTRACT FROM AUTHOR]

Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants' answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy. [ABSTRACT FROM AUTHOR]