Helping Business Students Acquire the Skills Needed for a Career in Analytics: A Comprehensive Industry Assessment of Entry‐Level Requirements

Fact‐based decision making is changing job functions within organizations more than any other technology. Analytics, once the purview of the data scientist, is now spread throughout organizations. No longer is there a single job title, job function, or set of required skills and credentials for an analytics career. Companies have moved away from seeking applicants with a specific degree to now recruiting analytics talent based on required skill sets. For more than a decade, business schools have been developing new programs in analytics in response to industry's needs. However, in developing meaningful career‐ready professionals, business programs must understand the skills required across different analytics job functions. In this article, the authors present a comprehensive assessment of the skills sought by employers when considering a candidate for an entry‐level analytics position. The authors describe the demand for various types of analytics professionals, identify the job titles and functions with the most significant demand, and then draw a comparison of the job requirements of hard skills, soft skills, software skills, and credentials between three of the most sought‐after analytics areas: data science, data analytics, and business analytics. The authors conclude by providing faculty and administrators with recommendations on how to adapt their courses and programs to provide students with the fundamental preparation necessary for careers in data science, data analytics, and business analytics.     

Creating Impactful Student Learning in a Business Analytics Program through Leveraging Scholar‐Practitioner Engagement

Industry demand for talented analytics professionals has created a significant increase in degree programs (e.g., MS in Analytics) around the globe. Many of these programs incorporate experiential learning in the curriculum to foster a deeper understanding. This article focuses on the value and challenges in implementing experiential learning in an analytics‐focused degree program by incorporating and scaffolding multiple organizational analytics projects throughout the curriculum. In addition, this article focuses on the manner in which these organizational analytics projects can create value for scholars, beyond student learning. Scholar‐practitioner partnerships have the potential to advance not only the field of academic research but also the rigor of actual practice. We report our experiences and best practices in creating and leveraging scholar‐practitioner partnerships in the context of project‐based experiential learning in a Master of Science in Business Analytics (MSBA) program. We provide motivation for creating such partnerships for teaching analytics, document student and organizational success, offer illustrations of exemplary experiential learning projects, and discuss the challenges that need to be managed.     

Knowledge,Skills, and Abilities for Entry‐Level Business Analytics Positions: A Multi‐Method Study

It is impossible to deny the significant impact from the emergence of big data and business analytics” on the fields of Information Technology, Quantitative Methods, and the Decision Sciences. Both industry and academia seek to hire talent in these areas with the hope of developing organizational competencies. This article describes a multi‐method research agenda that was executed to ascertain insights regarding which knowledge, skills, and abilities, (KSAs) are valued by employers seeking to hire entry‐level analytics professionals from schools of business. Current undergraduate business analytics programs are first examined to define the research scope. A triangulated mixed‐method research approach is then used to determine the knowledge, skills, and abilities that are in demand for entry‐level jobs in this area. Finally, the multi‐method triangulation of data is combined with experiences in building academic programs in business analytics at two nationally‐ranked state universities to offer insights for those seeking to develop academic programs in this area.     

Determining Optimal Skillsets for Business Managers Based on Local and Global Job Markets: A Text Analytics Approach

We are experiencing a significant shift in management practices—moving from intuition, experience, and gut‐feeling driven decision‐making to one that is driven by data, evidence, and computational sciences. This shift, which is often called the analytics revolution, is not only changing the business landscape from a practical perspective but also redefining the white‐collar job market. The goal of this study is to employ a data‐ and analytics‐driven approach to analyze a large and feature‐rich dataset (which is composed of the recent job postings and the extant published literature) to characterize the state of the current business job market, especially to magnify the analytics related features and expectations including the geographic (i.e., local vs. global) differentiators. Using several graphical and tabular representations, in this paper we report on our thought‐provoking findings that collectively illustrate the changing face of knowledge, skills, and abilities required by the current local and global business job markets.     

Operationalizing Design Thinking in Business Intelligence and Analytics Projects

Design Thinking has been applied successfully in many fields; however, in Information Systems research most early studies focus on applying the specific toolsets to developing product and system designs to solve strategic, managerial, and operational problems. There is little research on how Design Thinking can be embedded in the learning processes in design‐oriented IS research and enabled in the context of business intelligence (BI) and business analytics (BA). How can Design Thinking be embedded in the learning processes and enabled in the context of BI/BA projects in the classroom environment, especially in the proof of concept stage? A practical view on integrating the mindset and toolset of the Design Thinking approach and in the learning process as a case based in‐class experience is presented along with a guideline for coaching the Design Thinking team and the lessons learned from each stage of Design Thinking. The results of this study show that Design Thinking practices can be enlisted to help students frame their creations and that these practices have a valuable contribution as alternatives when designing curricula for teaching and learning.     

Examining Undergraduate Students’ Attitudes toward Business Statistics in the United States and China

The rapid growth of analytics is bringing more attention to quantitative core curriculum requirements in undergraduate business programs. Statistical knowledge and skills are unequivocally recognized as essential cornerstone of business analytics. Furthermore, educational research has shown that academic performance in statistics classes is related to the attitudes that students bring to the course. This article assesses the reliability and validity of the Survey of Attitudes toward Statistics (SATS) in measuring noncognitive dimensions of attitudes among undergraduate business students. Sample data from U.S. and Chinese introductory business statistics classes were collected and analyzed to learn more about this aspect of student engagement across business schools located in countries with substantially different levels of success in international mathematics achievement testing, as well as differing cultural and educational practices. Results show that the six‐factor model structure of the SATS provides a good fit in both populations, with students entering business statistics holding only slightly positive attitudes toward the subject. Significant distinctions between four of the six attitude components were identified. Implications of measuring and improving these attitudes are discussed. Business statistics instructors are encouraged to use the survey as a standardized instrument to measure effects of interventions and make evidence‐based pedagogical decisions.     

Visualization of Twitter Data in the Classroom

The expression “big data” is ubiquitous in the business world today, but few undergraduate business students have the opportunity to gain practical experience with how new business analytics tools can be used in decision making. This article describes a set of hands‐on labs that prepare students to incorporate streaming data analysis into group research projects. Splunk is used to help students analyze and visualize streaming social media data. An evaluation of student projects and student survey results show that this practical approach of training students to manipulate and visualize big data was largely successful in achieving instructional goals.     

Flipping Online Analytics Classes: Achieving Parity with Their Face‐To‐Face Counterparts

We show how the principles of flipped learning that have been successfully applied to analytics classes taught face‐to‐face (F2F) at the undergraduate and graduate levels were emulated in corresponding online classes. Student satisfaction in the online flipped analytics classes was compared to student satisfaction in the F2F flipped analytics classes. Data were collected between the Spring 2016 and Fall 2018 semesters and involved two instructors with a sample of 726 students. The results of an independent samples t‐test showed that there was no significant difference in satisfaction between the online and F2F offerings. A binary logistics regression analysis on the data revealed that whether the flipped course was taught F2F or online had no significant effect on students recommending the course to their peers. The results suggest that flipped learning is transferrable to online analytics courses and yields student satisfaction at par with equivalent F2F flipped courses.     

An Active Learning Exercise for Introducing Agent‐Based Modeling

Recent developments in agent‐based modeling as a method of systems analysis and optimization indicate that students in business analytics need an introduction to the terminology, concepts, and framework of agent‐based modeling. This article presents an active learning exercise for MBA students in business analytics that demonstrates agent‐based modeling by solving a knapsack optimization problem. For the activity, students act as naïve agents by using dice to randomly selecting items for a finite capacity knapsack to maximize the value of the knapsack. Students then design a greedy heuristic to skew the probability of selection item. These pencil‐and‐paper models are then implemented in a spreadsheet model to demonstrate the effects of altering the agents’ behavior. Finally, a binary integer programming model is examined to contrast agent‐based modeling with traditional mathematical programming formulations. This exercise is innovative because it combines student engagement via active learning with an innovative, individual‐based, modeling methodology.     

An Excel Solver Exercise to Introduce Nonlinear Regression

Business students taking business analytics courses that have significant predictive modeling components, such as marketing research, data mining, forecasting, and advanced financial modeling, are introduced to nonlinear regression using application software that is a “black box” to the students. Thus, although correct models are estimated, students often do not obtain a thorough understanding of the nonlinear estimation process. The exercise presented in this article was created to demonstrate to students the need for nonlinear regression estimation—rather than using linear transformations and Ordinary Least Squares (OLS) and subsequently demonstrate the nonlinear optimization process to estimate nonlinear regression models. Using the spreadsheet exercise, students can see effects on the fit of the model by changing the model parameters as they change the values of the decision variables. After applying the spreadsheet to further exercises, students have expressed a deep understanding of the linear regression software. This exercise is innovative because the active learning exercise requires the students to make the logical connections between the structure of the model, the model's parameters, and the objective function.     

Foundations of dynamic learning analytics: Using university student data to increase retention

With digitisation and the rise of e‐learning have come a range of computational tools and approaches that have allowed educators to better support the learners' experience in schools, colleges and universities. The move away from traditional paper‐based course materials, registration, admissions and support services to the mobile, always‐on and always accessible data has driven demand for information and generated new forms of data observable through consumption behaviours. These changes have led to a plethora of data sets that store learning content and track user behaviours. Most recently, new data analytics approaches are creating new ways of understanding trends and behaviours in students that can be used to improve learning design, strengthen student retention, provide early warning signals concerning individual students and help to personalise the learner's experience. This paper proposes a foundational learning analytics model (LAM) for higher education that focuses on the dynamic interaction of stakeholders with their data supported by visual analytics, such as self‐organising maps, to generate conversations, shared inquiry and solution‐seeking. The model can be applied for other educational institutions interested in using learning analytics processes to support personalised learning and support services. Further work is testing its efficacy in increasing student retention rates.     

Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course

In this article, we present an experiential perspective on how a big data analytics course was designed and delivered to students at a major Midwestern university. In reference to the MSIS 2006 Model Curriculum, we designed this course as a level 2 course, with prerequisites in databases, computer programming, statistics, and data mining. Students in the class were mostly seniors or at the graduate level, and had a strong technical and quantitative background. We include details of concepts covered in the course, as well as summaries of four major sample course assignments used. Some of the concepts covered include large‐scale data collection and management using the Hadoop ecosystem, stream mining, visual analytics, and social network analytics. Besides Hadoop, the course also introduced various IBM and Teradata big data tools. We show how the course modules align with the intended learning goals and course objectives. A post‐course survey indicated that the structure and organization of the course helped students clearly and concisely assimilate the course content.     

Learning analytics initiatives in Latin America: Implications for educational researchers,practitioners and decision makers

This study presents several Latin American research initiatives in the field of learning analytics (LA). The study’s purpose is to enhance awareness and understanding of LA among researchers, practitioners and decision makers, and to highlight the importance of supporting research on LA. We analyzed case studies of LA research conducted at four levels of the educational system (the national, institutional, classroom and student levels), which were implemented in four countries (Brazil, Ecuador, Mexico and Uruguay). Diversified cases were selected to demonstrate the use of LA in primary, secondary and higher education, and to allow the inclusion of different types of datasets. These cases also showed the development of legal frameworks for handling ethical issues, and they met the requirements for data privacy protection in Latin America. The study concludes with a discussion of the findings and their implications for further research and practice in the field of LA for teaching and learning.     

Why do English Universities really Franchise Degrees to Overseas Providers?

Franchising degrees to overseas providers, normally for‐profit private companies, has become big business for English universities. The latest data from the Higher Education Statistics Agency reveal that there are now more international students registered for the awards of English higher education institutions that are studying wholly offshore than are on campus. There is an extensive economic literature exploring the role of franchising (or licensing) in the internationalisation of multinational companies. There are, however, few studies that have attempted to understand the reasons why so many English universities have moved beyond exporting (educating foreign students on campus) to franchising their degrees to overseas partners. This study uses an exploratory research methodology to get ‘inside the black box’. It investigates the motivations of decision‐makers entering and maintaining franchising operations at four English universities, revealing that financial considerations are less dominant than widely believed within the sector and are overshadowed by other, non‐commercial considerations.     

Coordinating learning analytics policymaking and implementation at scale

Many Latin-American institutions recognise the potential of learning analytics (LA). However, the number of actual LA implementations at scale remains limited, notwithstanding considerable effort made to formulate guidelines and frameworks to support the LA policy development. Guidance on how to coordinate the interaction between the LA policymaking and implementation is mostly missing, leaving a difficult challenge up to practitioners. In this study we propose a coordination model to support future LA initiatives at scale. We explore the problem by comparing two cases in Belgium and Ecuador. Following up we use the LA implementation timeline as a driver for planning the interaction between the policymaking and implementation. We continue by testing an application of the model with LA experts predominantly from Latin-American institutions, asking them to map low-level items of the SHEILA policy framework to four implementation phases. The results of this mapping support that LA policy building can be spread over time, that it can coincide with LA implementation at scale, and that both efforts can be coordinated. It is hoped that this study will provide additional guidance for future Latin-American and other LA initiatives.     

A Demonstration of Regression False Positive Selection in Data Mining

Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection (type I errors) is nearly certain to occur. This article describes an in‐class demonstration that shows the frequency and impact of false positives on data mining regression‐based predictive modeling. In this demonstration, 500 randomly generated independent (X) variables are individually regressed against a single, randomly generated (Y) variable, and the resulting 500 p‐values are sorted and examined. This experiment is repeated and the distribution of the number of variables significant at the 5% level resulting from this simulation is presented and discussed. The demonstration provides a tangible example in which students see the reality and risks of incorrectly inferring statistical significance of independent regression variables. Students have expressed a deeper understanding and appreciation of the risks of type I errors through this demonstration. This demonstration is innovative because the scale of the simulation allows the students to experience the near certainty that the correlations shown in the results are truly random.     

Improving Human Learning and Performance at Indiana University

The Indiana University Instructional Systems Technology statement of purpose is, “We improve human learning and performance in diverse contexts.” Our focus on learning and performance improvement includes K‐12 schools, higher education, business, industry, the government, military, and non‐profit organizations. This article gives an overview of the IST's background and history, key program elements (academic degree programs and leadership development programs), faculty's research and development activities, diverse students demographics and esteemed alumni (academics and practitioners), and future directions. We have led the instructional technology field through our commitment to remain a quality and forward‐thinking program.