Treffer: Business Analytics Competition (BAC@MC): A Learning Experience

Over the past few years, academics have undertaken initiatives to bridge the gap between theory and practice in the ever-growing field of business analytics, including implementing real-life student projects in all shapes and forms. Every year since 2015, Manhattan College has invited student teams from across North America and elsewhere in the world to its campus in order to participate in an intercollegiate business analytics competition (BAC@MC). This well-received event and the objectives behind it are described in this article. The program is shown to serve as an effective experiential learning adventure for the undergraduate students as it hones their data analytic skills in the context of an engaging real-world business problem. The roles various stakeholders play in this high-impact practice are highlighted. Furthermore, an example of a recent competition question is presented (along with a summary of the analytical approaches attempted) by the student teams. Descriptive visualizations, regression, and cluster algorithms implemented using python, R, Excel, or Tableau are among the typical analyses utilized by participating students. As witnessed by the students, faculty advisors, and the industry practitioners who attended the event, competitions such as BAC@MC can be rewarding, community-building, and transformative experiences for undergraduate students who will soon become tomorrow's business analysts.

As Provided

AN0163236059;q1n01apr.23;2023Apr24.06:25;v2.2.500

Business Analytics Competition (BAC@MC): A learning experience 

Over the past few years, academics have undertaken initiatives to bridge the gap between theory and practice in the ever‐growing field of business analytics, including implementing real‐life student projects in all shapes and forms. Every year since 2015, Manhattan College has invited student teams from across North America and elsewhere in the world to its campus in order to participate in an intercollegiate business analytics competition (BAC@MC). This well‐received event and the objectives behind it are described in this article. The program is shown to serve as an effective experiential learning adventure for the undergraduate students as it hones their data analytic skills in the context of an engaging real‐world business problem. The roles various stakeholders play in this high‐impact practice are highlighted. Furthermore, an example of a recent competition question is presented (along with a summary of the analytical approaches attempted) by the student teams. Descriptive visualizations, regression, and cluster algorithms implemented using python, R, Excel, or Tableau are among the typical analyses utilized by participating students. As witnessed by the students, faculty advisors, and the industry practitioners who attended the event, competitions such as BAC@MC can be rewarding, community‐building, and transformative experiences for undergraduate students who will soon become tomorrow's business analysts.

Keywords: data analytics; experiental learning; competition

INTRODUCTION

Each year, Manhattan College hosts an exciting program and stimulating competition that gives undergraduate students in business analytics or related fields an opportunity to test their knowledge and hone their skills. Competing students engage in the "art and science" of decision‐making, but also practice their ability to draw business insights from a comprehensive analysis of relevant data. This intercollegiate competition is viewed by many as the first of its kind to engage undergraduate students in the interdisciplinary field of business analytics. The annually changing focus, covering a wide range of topics from varying industries, remains one of the event's distinctive features. The inaugural event took place during the academic year of 2014–2015, with the idea of building—at the national level and beyond—a community of academics, faculty, students, and professionals around the common interest of the field of business analytics. While the focus remains on undergraduate student teams competing to solve hard problems, the vision is to develop an intercollegial community returning to Manhattan College (MC) year after year. Since then, the event has been referred to fondly as BAC@MC.

The first round of the competition is a remote exercise that students complete at their home institutions over several months. The data and case questions are made available to the competing teams in early February, and the competition builds up to the day of the event when teams gather to share their findings for Round One and participate in the intensive follow‐up activities for Round Two. Business analytics experts from various industries serve as featured speakers and panelists at the event.

Adhering to the vision of building a community of learners around the academic field is key to sustaining and expanding interest in the competition. This article will discuss, in particular, the overarching objectives that help maintain consistent outcomes for the activity. The first objective is to introduce an approach to business analytics that leads to data‐driven decision‐making and problem solving. Thus, the design of the competition motivates decision‐making through questions leading to insights and recommendations. The second objective is to engage students in a high‐impact experiential exercise with real‐life implications. Thus, the design introduces complexity that requires deep understanding of the decision‐making situation, accompanied by the need to interpret and reflect on proposed recommendations.

This article is organized in seven sections; where appropriate, external links are provided. Section 2 summarizes background concepts that influenced the objectives of BAC@MC; it briefly discusses prior literature on teaching business analytics and the benefits of high‐impact practice in enhancing student learning. In this section, we suggest that BAC@MC as a form of high‐impact practice can offer innovative teaching and learning experiences in business analytics. It also includes descriptions of other similar collegial activities, highlighting similarities and differences in scope.

Section 3 describes BAC@MC as an effective experiential learning exercise that is grounded in the curriculum of business analytics, highlighting the promise of bridging the gap between theory and practice. Section 4 provides a detailed description of the format of the competition. It also describes the role of participants and stakeholders and how they contribute and benefit. In addition, readers will be guided through the process of preparing student teams. Section 5 uses the 2021 competition as an example. The competition research questions and results are outlined, along with a breakdown of the most popular analytic approaches, tools, and outcomes developed by student teams. Section 6 describes the various outcomes experienced through BAC@MC and how the process of designing the competition seeks continuous improvement. Multiple years of participants survey results are included. The form that students receive at the end of the competition summarizing judges feedback is also included as an appendix. The final section provides an overall conclusion of BAC@MC as an activity for high‐impact learning in business analytics.

BACKGROUND

Teaching business analytics

In line with the McKinsey Global Institute (MGI) call for professionals with analytical skills (Henke et al., [2]; Manyinka et al., [13]), educators have tried to offer business analytics education that meets industries' needs. Business analytics refers to "the process of methodological exploration of organizational data using statistical and mathematical analysis" (Johnson et al., [4], p. 91). Accordingly, as noted by the Institute for Operations Research and the Management Sciences (INFORMS), business analytics aims to help decision‐makers study and analyze organizations' problems (INFORMS, [3]). In this sense, it is necessary to educate students to analyze data from descriptive (i.e., what happened), predictive (i.e., what will happen), and prescriptive (i.e., how to make insightful decisions) perspectives (INFORMS, [3]; Paul & MacDonald, [17]). Importantly, Wilder and Ozqur ([25]) noted that business managers may not need to be experts when it comes to analytic approaches in decision‐making, rather "skilled enough to understand the value and recognize the opportunities created with analytics" (p. 182). In other words, business analytics education programs should train students to use data to make informed business decisions. It has been also highlighted that, since any business decision encompasses a variety of business functions, business analytics education should cover interdisciplinary aspects so that students have the capabilities to help businesses make better decisions in various domains (Paul & MacDonald, [17]; Sharma et al., [19]; Speier‐Pero & Schoenherr, [21]). In this regard, researchers encourage educators: (1) to focus on skill sets expected for business managers such as problem solving, teamwork, and communication and (2) to use various specific examples to let students experience the scope of business analytics applications (Cegielski & Jones‐Farmer, [1]; Konrad et al., [8]; Wilder & Ozqur, [25]).

Though such recommendations for business analytics education have been proposed, it is hard to find standardized guidelines for designing business analytics education programs. Accordingly, researchers have examined existing curricula to provide a roadmap for teaching business analytics in accordance with what the industry requires for business analytics professionals (Paul & MacDonald, [17]; Stanton & Stanton, [22]). For instance, Wilder and Ozqur ([25]) propose that project life cycle, data management, analytical techniques, deployment, and a functional area should be covered in business analytics education, as those skills are commonly required by practitioners. Furthermore, researchers have studied the focus of currently offered business analytics education programs at various levels and found that the undergraduate programs tend to focus on basic lectures for mathematical/statistical knowledge and individual learning, whereas the graduate programs concentrate on problem solving and group collaboration (Paul & MacDonald, [17]). Such differences, in turn, make undergraduate students less likely to have experiences in problem solving and group collaboration, which are commonly expected for analytics professionals (Johnson et al., [4]; Paul & MacDonald, [17]). Furthermore, it has been identified that most entry‐level positions in the analytics field require job candidates to have prior analytics‐related experiences (Cegielski & Jones‐Farmer, [1]; Wixom et al., [26]). As a result, compared to graduate counterparts, undergraduate students are less competitive in job markets due to the lack of desired skill sets and experiences. Hence, educators should find ways to fill this gap in undergraduate analytics programs for future career preparation.

Analytics competitions as a high‐impact practice

To bridge the gap between industries' needs for business analytics professionals and the usual skills developed in undergraduate business analytics programs, researchers have recommended several complementary approaches such as internships, partnerships with companies for class projects, and participation in analytics competitions (Stanton & Stanton, [22]). These approaches can be considered as high‐impact practices (i.e., HIPs). HIPs are theoretically grounded in experiential learning literature; specifically, researchers suggest that HIPs can further motivate students to engage in learning experiences by bridging the gap between educators and practitioners, thereby better preparing students for future careers (Kolb, [7]; Shavers & Mitchell, [20]). The Association of American Colleges and Universities identifies several types of HIPs (e.g., service learning and internships) and strongly recommends their adoption in higher education for their benefits to learning (Kuh, [9]). Prior research finds that HIPs are highly effective in educating students as well as preparing them for careers because they receive significant feedback from faculty, outside‐of‐classroom experiences, and interactions with faculty and peers (Kuh, [9]; Kuh & O'Donnell et al., [10]; Kuh et al., [11]). Furthermore, prior research finds that HIPs are particularly effective at encouraging students to engage in problem solving, collaborate with others, and apply knowledge in a real‐world setting (Kilgo et al., [6]; Johnson & Stage, [5]; Shavers & Mitchell, [20]). As noted earlier, undergraduate business analytics programs tend to lack offerings that develop those capabilities (Johnson et al., [4]; Paul & MacDonald, [17]). Hence, adopting an HIP approach in the undergraduate curriculum would complement the existing business analytics curricula while better preparing students for their future careers.

This article is particularly interested in how participating in analytics competitions benefits undergraduate students. When students participate in analytics competitions, they are asked to analyze data with their team members under time pressure, then present solutions to the provided scenarios (Lynch et al., [12]; Sachau & Nass, [18]). Thus, through the competition process, students can effectively learn desired skill sets for analytics professionals such as problem solving, teamwork, and communication skills. Furthermore, it is common to have external judges (specifically, industry professionals) in analytics competitions, and thus students can enjoy a networking experience while competing (Lynch et al., [12]). In sum, participation in analytics competitions can promote students' learning, especially for undergraduates, by offering additional educational benefits rarely experienced in typical undergraduate business analytics programs.

The field of analytics competitions

BAC@MC was developed based on the consensus about the benefits of analytics competitions. It is worth noting that, since BAC@MC was first implemented, a few similar business analytics competitions have been established; the majority are interuniversity competitions that follow similar formats. These include Clark University's All Things Business Analytics Competition (https://www.clarku.edu/schools/school‐of‐management/2022/05/04/2022‐business‐analytics‐competition/) and Rochester Institute of Technology's Saunders Business Analytics Competition (https://saunders.rit.edu/faculty‐research/academic‐departments/mis‐marketing‐analytics/business‐analytics‐student‐competition). These competitions, although still quite impactful, are limited to student teams enrolled by the same university; though they likely require less planning and outreach resources, they limit the diversity of the impacted learning community. Other business analytics competitions are designed for graduate students and might require higher levels of technical skills. An example of this is the University of Iowa event, Iowa Business Analytics Case Competition (https://tippie.uiowa.edu/masters‐business‐analytics/full‐time/business‐analytics‐case). A few focus on the use of business analytics in a single industry, often partnering with a particular company who might provide research questions, data, and help with judging the competition. An example of that is the event run by the University of Pittsburgh, Pitt Business Analytics Case Competition (https://cba.pitt.edu/2021‐pitt‐business‐analytics‐case‐competition/).

A newly formed competition that most closely resembles the format of BAC@MC, the Champion® Analytics Case Competition was first offered in 2020 by Elon University (https://www.elon.edu/u/academics/business/organizational‐analytics‐center/cacc/). The major difference between the two competitions is that Champion allows mixed student teams (graduate and undergraduate). Also, they limit the number of participating teams to twelve. Before the start of their competition, Elon University participated in BAC@MC for several years and was backed by BAC@MC "Champions" in 2019.

In sum, among the currently existing analytics competitions, BAC@MC offers compelling features (e.g., a national‐level competition for undergraduates covering various topics) that can further contribute to enriching student learning experiences.

COMPETITION CURRICULUM

The early literature on teaching and learning describes experiential learning as a sequential process of doing then stopping (i.e., not doing; Kolb, [7]). These theories form the foundation on which high‐impact practices are developed in the literature. The iterative method provides the opportunity to experience, then reflect, and allows learners to conceptualize in the course of experimentation.

Business analytics programs hold the promise of bridging the gap between theory and practice to prepare students for the challenges of helping organizations make evidence‐based decisions in the real world. The Business Analytics Competition at Manhattan College (BAC@MC) builds on this promise by introducing an experiential exercise that encourages students to analyze data, then discern meaning from their analysis, leading to further analysis and deeper understanding. Accordingly, the organizing committee of BAC@MC has tried to design the competition curriculum focused on the following aspects.

Theme selection

The organizing committee of BAC@MC selects a theme for the competition and designs research questions around that topic. This initial step presents several challenges. The first is to choose a topic that would be relevant as well as familiar to the majority of undergraduate students. It must be a topic that they can readily explain, not only during the course of the competition but also as part of their overall educational and career development. Ideally, it would be a topic that they are likely to be passionate about and willing to invest the time and energy to research and analyze. Finally, we find it imperative that the themes fit within the social justice mission of Manhattan College.

For example, competition themes have tackled challenges around climate, education and housing. During the last 2 years, issues related to COVID‐19 were central to the selected themes. Table 1 shows a complete list of the themes by year for all BAC@MC competitions held to date. Additionally, Section 5 includes a detailed description of the 2021 competition that combined intersecting issues of housing and pandemic outbreaks in New York City.

1 TABLE Research questions and data themes for the previous BAC@MC competitions by year

<table><thead><tr><th><p><bold>Competition year</bold></p></th><th><p><bold>Main data set source</bold></p></th><th><p><bold>Type of data sets</bold></p></th><th><p><bold>Competition question theme</bold></p></th></tr></thead><tbody><tr><td>2015</td><td>An amusement park (anonymous)</td><td>Private</td><td>Employee satisfaction&#8212;covering various aspects of an employee's experience with their job</td></tr><tr><td>2016</td><td>Gilt.com</td><td>Private</td><td>Customer manipulation&#8212;analysis of online shopping data for products including items for sale</td></tr><tr><td>2017</td><td>Federal Funds Information for States (FFIS)</td><td>Public</td><td>Educational justice&#8212;analysis of the distribution of grants to states using the Catalog of Federal Domestic Assistance (CFDA)</td></tr><tr><td>2018</td><td>Checkbook New York City</td><td>Public</td><td>Budgetary accountability&#8212;analysis of the contract and spending data for the City of New York and Boston</td></tr><tr><td>2019</td><td>New York Independent System Operator</td><td>Public</td><td>Renewable energy feasibility&#8212;analysis of pricing and load for the electric power zones in the New York State</td></tr><tr><td>2020</td><td>New York City's Housing and Neighborhood's Data Hub</td><td>Public</td><td>Housing inequality&#8212;a multivariate analysis of the housing crisis in New York City at a subborough level</td></tr><tr><td>2021</td><td>NYC Department of Health and Mental Hygiene and New York City's Housing and Neighborhood's Data Hub</td><td>Public</td><td>Pandemic and housing&#8212;analysis of the impact of COVID&#8208;19 on the housing crisis in New York City</td></tr><tr><td>2022</td><td>Bloomberg Covid Resilience Ranking and Oxford COVID&#8208;19 government response tracker project</td><td>Public</td><td>Global resilience against the pandemic&#8212;comparison of the government responses to COVID&#8208;19 and their resilience ranking</td></tr></tbody></table>

Curriculum design: data and question

The second step is to secure relevant and usable data. We have explored options of acquiring proprietary data sets that are targeted toward clear business questions, since these types of data sets are typically clean and ready to use. However, they are often costly, and sometimes scrubbed to a level of obscurity that could interfere with the clarity of the question and make it harder to discern meaning from its analysis. On the other hand, publicly available real data sets are easy to grab, but such data sets are typically maintained for the sake of reporting rather than exploring research questions. We have also explored the use of publicly available data, using multiple sources and formats. These types of data sets typically have missing data and merging of different formats can be challenging; hence, this approach introduces an additional layer to the data analysis. We have used both types of data sets in our competition successfully and concluded that each type may provide its own valuable experience for the students. Table 1 includes the list of data sets provided to the teams in all BAC@MC competitions in previous years, including the type and source of the main data sets.

When the competition data sets have been selected, we can more critically examine the level of complexity of the research questions. The organizing committee attempts to answer an important question: How much of the analysis should be dictated and how much should be left for the students' interpretation? Achieving the perfect balance is an art we are developing with each competition and getting better at every year. Our aim is to address three objectives:

The first is to go beyond descriptive analysis of the data to examine how the data can predict or prescribe a decision‐making situation.

The second is to highlight business/social relevance in the analysis questions.

The third is to enable a performance of an analysis that is both simple at one extreme and more complex at another.

Designing the scope for the competition is a recursive process that involves going back and forth between question and data. The committee works on this difficult and vital part of the preparation during the fall semester. The data sets and the story narrative (containing the research question) are shared with the competing teams as Round One material at the start of the spring semester. That day marks a day of celebration for the committee.

Elements of effective experiential learning

Like any activity that engages smart and motivated students by blending teaching with practice, BAC@MC has all the important elements for a high‐impact learning experience. Traditional teaching and learning literature names four such main elements: the concrete experience, the reflection, the conceptualization, and the experimentation. Below we describe briefly how BAC@MC accommodates each of these components.

The Concrete Experience: The competition is an exercise in applying academic knowledge to a real‐life situation. It is well grounded in the topics of business analytics, requiring some level of expertise in data analysis. Students need to know or learn, at some level, how to retrieve, organize, and clean large data sets; how to design data‐visualization schemes; how to interpret statistical analyses; how to employ data‐mining techniques; how to develop decision models; and how to articulate conclusions and recommendations. Once student teams develop and apply their academic knowledge, they have an opportunity to reflect on their experience.

The Reflection (examining knowledge versus results): We design the curriculum of the competition with the hope that each student, at any level of academic proficiency, will experience an "ah‐ha" moment (or two) through reflective observations. Of course, reflections are impacted by predetermined knowledge, but they often spark the need to adjust one's knowledge in order to meet new challenges. Students are forced to critically assess their findings and struggle with how new information might contradict their previous knowledge. They then develop questions and observations that determine the need to further research and understand newly acquired information.

The Conceptualization (reinforcing conceptual understanding): The next element in experiential learning is channeling reflective experiences into the conceptualization of new knowledge. The competition provides enough motivation to foster this reinforcement of knowledge. It is clear to students that they need to learn more about the target industry's ins and outs, whether it's electric energy or online fashion marketing. The more they understand the complexities of the data, the more they seek new tools and techniques, and the more they hone their business analytics knowledge.

Experimentation (more analysis): Armed with the new concepts, students are ready to test their expanded knowledge. Exercises with defined conclusions, even those requiring complex solutions, may imply an end to the experimentation process. The questions of our competition are designed to be open‐ended, even when they are well‐guided. Our objective is to encourage seemingly endless exploration, and the competitive environment helps with this objective. We are always impressed with the depth of the analysis performed by the student teams, but what is equally gratifying is their realization that more could be done to fully answer the questions at hand.

The learning process that students go through during the competition clearly follows this loop: experiences become concrete knowledge, spark deeper reflections, help develop new understandings, leading to more focused experimentation, etc.

Overall, a successful competition takes into account all three aspects described in this section. The theme helps frame the "big" purpose of engaging in the activity, the link to the curriculum adds relevance to the learning process, and recognition of the elements of experiential learning leads to high‐impact practice.

RUNNING THE EVENT

Undertaking this community‐building event is multifaceted. Hence, as they say, it takes a village. Many types of participants and contributors come together to enable a meaningful learning experience. This section includes a brief description of the stakeholders and format of the competitions.

Stakeholders' role

Organizing committee

The efforts to plan BAC@MC start at the beginning of each school year, roughly 9 months before the main competition takes place. Business school faculty of the hosting institution constitutes the majority of the organizing committee. The initial meeting is convened at the request of the Dean and a committee chair is selected. The idea of involving faculty from other institutions in the organizing committee has also been explored.

The organizing committee members are then divided into subcommittees, taking on various tasks including the event logistics planning, inviting keynote speakers and judges, as well as a "data subcommittee." Selecting a theme for the competition, finding related data sets to be shared with the teams, then designing analytical research questions around that theme has proved to be the most critical task of the preparation.

An important member of the organizing committee is a carefully selected graduate assistant who has good technical and communication skills. The graduate assistant maintains the competition website and acts as the first contact person on the committee, thus managing the communications with teams before, during, and after the event.

Team advisors

Once certain details of the event (e.g., dates, format, and a tentative schedule) are finalized and the flyer of the event is created by the organizing committee, an announcement is disseminated through various collegial analytics forums in the United States and elsewhere in the world. The faculty teaching undergraduate business analytics (or related courses) in AACSB‐accredited institutions are invited to register a team in the upcoming competition. Faculty advisors do not need to have selected their team or include student names at the time of registering for the event. In the past few years, a number of participating faculty advisors included the competition questions in their spring‐semester syllabi. These instructors encourage all students to tackle the competition question as part of their course assignments, then select the best performers in the class to represent the institution at the actual competition. Also, BAC@MC has hosted schools who registered several student teams under the supervision of various advisors. In many cases the same faculty advisors have returned to the event year after year, while other returning schools have rotated the participating advisors among their faculty. The job of advising a team of undergraduates in such a highly competitive event can be quite demanding; it is often a semester‐long commitment described by many as both a hard and a rewarding experience.

Students

Every team consists of two to four undergraduate students who have a passion for data analytics, majoring in business analytics, data science, mathematics, or other related fields in business, science, or engineering. When the competition was held as an in‐person event between 2015 and 2019, 17 teams (on average; min = 13, max = 20) participated each year. Furthermore, during the same period, we had more teams from private (vs. public) institutions (i.e., 77% vs. 23%). However, when the competitions were hosted virtually between 2021 and 2022, the average number of participating teams substantially increased (i.e., 32 teams). Furthermore, compared to the previous years, more teams participated from public institutions (49%) during these years. The demographics of past teams are summarized in Table 2. A significant percentage of students in the past competitions have been graduating seniors who have built up an arsenal of data analytics skills over the course of their college years, and are ready to put those skills to good use before they step into their careers as junior data analysts.

2 TABLE A summary table of BAC@MC past participants

<table><thead><tr><th align="left">Year</th><th>2015</th><th>2016</th><th>2017</th><th>2018</th><th>2019</th><th>2020</th><th>2021</th><th>2022</th></tr></thead><tbody><tr><td align="left">Number of participating teams</td><td>13</td><td>20</td><td>15</td><td>18</td><td>17</td><td>18</td><td>30</td><td>33</td></tr><tr><td align="left">Number of participating institutions</td><td align="left">Total</td><td>13</td><td>19</td><td>14</td><td>17</td><td>16</td><td>18</td><td>23</td><td>24</td></tr><tr><td align="left">Public schools</td><td>N</td><td>3</td><td>6</td><td>2</td><td>4</td><td>4</td><td>6</td><td>13</td><td>10</td></tr><tr><td>(%)</td><td>23</td><td>32</td><td>14</td><td>24</td><td>25</td><td>33</td><td>57</td><td>42</td></tr><tr><td align="left">Private schools</td><td>N</td><td>10</td><td>13</td><td>12</td><td>13</td><td>12</td><td>12</td><td>10</td><td>14</td></tr><tr><td>(%)</td><td>77</td><td>68</td><td>86</td><td>76</td><td>75</td><td>67</td><td>43</td><td>58</td></tr></tbody></table>

1 Note. (1) The numbers reported for the year of 2020 indicate the registered teams/institutions. The competition was canceled due to the COVID‐19 pandemic shutdown. (2) The competitions in 2021 and 2022 were hosted virtually. (3) Multiple teams from the same institution can participate in each year's BAC@MC competition.

Over the 4 months of the competition (i.e., from the time the data/questions are released to the registered teams until the teams present their findings at the competition), students hone their analytical skills by working on a real‐world problem represented by one or multiple related large data sets. The many opportunities for students to learn and practice under the guidance of their faculty advisors include understanding the theme or subject matter, cleaning and restructuring the provided large data sets, interpreting statistical analyses, employing data‐mining techniques, designing data‐visualization schemes, articulating conclusions and recommendations, as well as engaging in the art of presenting the highlights of their findings in different formats and to different target audiences.

Like all competitions, not all participants are content with the outcome once the winners are announced. Nevertheless, according to a survey of participants, the majority of students find BAC@MC a unique experience of learning, networking, and resume‐building that is also an enjoyable learning environment (see Section 6 for a summary of survey results).

Judges

Other than providing guidance to the students on their teams, faculty advisors are also in charge of judging the poster/oral presentation of the other student teams during Round One, which takes place—either on‐ground or virtually—during the first meeting day of the competition.

During Round Two of the competition, in order for the student presentations to be evaluated from a more practical perspective, student teams present their work to a panel of judges composed of industry practitioners. A majority of these practitioners are data scientists representing such high‐tech giants as Google, IBM, Microsoft, and others. Depending on the theme of the competition, a number of domain experts from a variety of other financial, educational, and health care institutions as well as government officers have served on the judging panel.

Constructing a comprehensive, multiperspective judging panel is another delicate, time‐consuming task, which keeps the organizing committee occupied until a few weeks before the start of the event. Among the several concerns of choosing judging panelists for the second round is making sure the invitees are not directly affiliated with the hosting team (alumni, for example), thus ensuring a fair judging process for the visitors.

The presence of the industry‐based judges enhances the judging process through a diversification of perspectives. Their participation also helps their own portfolio by syncing up their analytical skills with the latest trends taught in academia. Additionally, they benefit their respective companies by representing them in the event, as well as potentially assisting the companies with recruiting future data scientists.

Partners

The other category of stakeholders of BAC@MC consists of sponsors, donors, and industry partners.

Securing the funds for hosting 20‐plus student teams from across the United States and the world has always been a challenge for the hosting institution. The on‐ground version of the competition necessitates securing additional financial resources, on top of the registration fees charged to the teams. To avoid a heavy financial burden on the O'Malley School of Business, the organizing committee as well as the Dean constantly seek potential sponsors and donors for the event. Donors are typically committed alumni of Manhattan College with a strong passion for business analytics.

Finding companies to directly fund the event as sponsors has been an ongoing challenge, although certain companies have made their own contributions to BAC@MC by providing proprietary data sets in a number of competitions as well as supplying data scientists or domain experts to serve as judges.

Keynote speakers

Another key component of BAC@MC, be it in‐person or virtual, is provided by the keynote speakers of the event. For a multiday event gathering students, faculty, and industry partners, as well as the administration of the hosting institution, inclusion of renowned speakers not only contributes to the prestige of the event but can also serve as a tool to attract a large audience.

The process of inviting such high‐profile yet relevant speakers is another time‐consuming task which keeps multiple members of the organizing committee involved for several months. Certain speakers have arrangements with PR agencies which makes the speaker invitation process smoother yet costlier. For the competitions held to date, BAC@MC has hosted prominent data scientists, book authors, movie directors, as well as other respected figures in the analytics society, many of whom are experts in the theme for that year's competition, and all of whom are fantastic storytellers. The idea of having a panel of experts has also been explored as a supplement or replacement to the keynote talks in a number of competitions.

Format

On‐ground competitions

The BAC@MC was designed to be a multiday, in‐person event from its inception in 2015. The main event takes place over 2–3 days in May on the campus of Manhattan College in Riverdale, New York City, following the end‐of‐year graduation ceremonies. The timing allows the organizing committee to utilize campus residence halls to provide relatively affordable accommodations in New York City for the participating students.

In the on‐ground version of the competition, teams first present their posters and answer questions from the faculty judges (i.e., Round One). Following these presentations, the teams are provided with additional data sets and questions building on the theme of the initial data. For the Round Two of the competition, students have to analyze the new data sets in a hackathon‐like format in assigned study spaces in the Manhattan College library, and present their findings orally to panels of practitioner judges less than 24 h after the beginning of this round.

Five competitions were successfully held during the 2015–2019 period, bringing hundreds of students, faculty advisors, judges, and speakers to the Manhattan College campus each spring. The 2020 event was off to a great start based on the number of registered teams. The main event, though, had to be canceled due to the emergence of the COVID‐19 pandemic in the United States.

Virtual competitions

For the two subsequent annual events, the organizing committee decided to host the event virtually. The two remote competitions held in 2021 and 2022 brought unique challenges and opportunities which had not been of much relevance to the on‐ground version. In this section, we compare such unique aspects of running a virtual analytics competition with those of a conventional in‐person, on‐ground event.

While the idea of a virtual competition was rather imposed on the organizing committee during the 2020–2022 period, it opened new opportunities for the hosting institution as well as the participating teams. First and foremost, a virtual competition has a broader outreach to institutions across the country and the world. The virtual format saves significantly in travel costs for the students and their advisors as well as visa‐related fees, where required. The hosting institution also enjoys a significant reduction in operating costs by foregoing the two highest cost items: dorm accommodations and meals. A virtual competition also provides greater scheduling flexibility, as the event date was not constrained by the availability of the residence halls. Consequently, the event could be held prior to graduation, allowing more graduating seniors to participate. Lastly, a virtual competition allows greater availability of keynote speakers and judges, who were able to join the event as remote guests from the convenience of their home offices.

Similar to other remote events, a virtual competition comes with its own challenges and drawbacks. First of all, a virtual competition where participants are represented by squares on a computer screen, although remaining highly competitive, might limit the urgent excitement in participating when compared to the atmosphere of a large, bustling auditorium. Secondly, a virtual competition provides little to no opportunity for the attendees to network with the speaker, the judges, or students and advisors from other universities. Additionally, the New York City excursion, a fun community‐building part of the competition, which typically precedes the events of the main competition, is lost to the remote format. This part is particularly of interest to the students from other parts of the United States or elsewhere in the world who have not had a chance to visit New York City prior to BAC@MC.

The challenge of hosting a virtual competition is not limited to the inherent issues mentioned above. Managing the time difference between the Eastern Time Zone and other parts of the world could inconvenience remote students and advisors, as well as speakers and judges. Secondly, providing a quality presentation and Q&A environment for the keynote speakers has proven to be difficult at times. Most importantly, ensuring a fair competing platform for students and their advisors in a virtual format remains a top priority for the organizing committee from the beginning of the organizing efforts to the very last minute of the competition.

EXAMPLE: BAC@MC 2021

In 2021, teams were tasked with analyzing the housing landscape of New York City and the cascading effects the COVID‐19 pandemic had on its residents.

The story

The competition research question is presented to the competing team in the form of a story, often describing a meeting or a conversation. The 2021 story addresses aspects of the relationship between NYC housing challenges and the COVID‐19 impact in various city neighborhoods. In this case competing teams had to understand what had been happening over the previous 10 years: Is homeownership increasing or decreasing? What is happening to home prices and rental costs? Are incomes keeping pace with increased housing costs? What are the barriers to home ownership? Is it the down payment? Is it simply qualifying for a loan? If a family could afford the down payment, how would the mortgage payment compare to the rent in the same neighborhood? Perhaps rent vouchers could become mortgage vouchers—at least for the initial years of the loan. The COVID‐19 pandemic had a significant impact on the questions above and the previous year's trends. Teams had an opportunity to analyze complex data, explore options, and propose actions in the context of the articulated questions. The complete original story distributed to all teams is included in Appendix A.

Data: description and sources

The NYC COVID‐19 data comes from the NYC Department of Mental Health and Hygiene, downloaded from Github (NYC Department of Health & Mental Hygiene, [14]). It includes NYC's COVID‐19 cases, deaths, rates, and tests by Zip Code Tabulation Area (ZCTA). The housing data come from the NYC Core Data (NYU Furman Center's CoreData.nyc, [16]). This includes housing prices, home sales, renter income, rates of crowding, among others. Other demographic data comes from the NYC Data Portal (NYC Open Data Portal, [15]).

The provided data sets gave information about New York City (NYC) in the form of administrative subunits, Public Use Microdata Areas (PUMAs). The supplemental Excel file describes each table and variable in detail, and the following is a summary of the data contained and used in the quantitative analysis:

<bold>NYC‐housing‐data.xlsx</bold> contains NYC housing data. It lists information on single family home prices, price single family home, price condominium, single family sale volume, condominium sale volume, home ownership rate, FHA‐ or VA‐backed loans, LMI borrowers, housing units, crowding (>1 person/room), homeowner income, renter income, rent burden, choice vouchers, and percentage of public housing.

<bold>NYC‐COVID‐data.xlsx</bold> contains NYC COVID‐19 data. It lists NYC's daily cases, hospitalizations and deaths, virus (diagnostic) testing, and percent positive for each PUMA.

<bold>NYC‐demographic‐other‐data.xlsx</bold> contains NYC demographic data and other supporting data. It detailed characteristics for each PUMA including population, poverty rate, serious crime, English scores, Math scores, subway access, park access, racial breakdown (in percent), adults with independent living difficulty, school absenteeism, unhealthy food access, jail incarceration, high school graduation, cancer in children, lung and bronchus cancer, breast cancer in females, brain and other nervous system cancer, bicycle injury hospitalizations, pedestrian injury hospitalizations, nonfatal assault hospitalizations, and chronic obstructive pulmonary disease hospitalization.

There are a total of 42 tables with sizes varying from 55 to 302 rows and 6 to 22 columns. The supplemental Excel file lists the characteristics for each table, and Figure 1 provides a sample of selected data tables. All data sets are available for download. (https://drive.google.com/drive/folders/19BFfYJtQhfpghpmDHHF78laFJUbHdArr?usp=sharing)

The collection of data sets was utilized in various forms for each of the analysis steps described in this article. In certain instances, the data were aggregated or summarized as required. Teams were allowed to augment their analyses by introducing additional data from external sources; some of them introduced population mobility, housing sales, and pricing information, as these fluctuated greatly during the pandemic.

Types of analysis used

Many of New York City's structural problems were aggravated by the onset of the COVID‐19 pandemic. Much research identifies crowding, housing, and other demographic factors that caused the COVID‐19 pandemic to impact some regions of the city more than others. Using the provided data sets and other public sources (e.g., Google's Mobility data), student teams developed models of COVID‐19 rates based on the factors listed above, and also discussed the financial hardships that constrain housing availability for the average New Yorker.

Since analyzing housing and COVID implications is a multifaceted discussion that may have emotional and political implications, the best way to discern fact from fiction is to look at the data.

Teams have a lot of freedom in how they organize and deliver their presentations, and some prefer more technical information while others focus on the big picture. Overall, we observed that descriptive visualizations, regression, and cluster analysis were the most commonly utilized forms of analysis. In 2021, all teams developed visualizations of different difficulties, with 60% applying regression and 30% using clustering. In terms of tools, 80% used Tableau or Power BI, 56% Python or R, and 100% Excel. There were significant variations in the direction teams chose for their data investigations, but below are a few selected instances of some of the most prevalent insights discovered by the students.

Descriptive analysis

Many of the teams' analyses included visualizations such as those shown in Figures 2 and 3.

Figure 2a shows that NYC's income distribution is highly skewed, as the city's average household income in 2019 was $105,304 while the median was $72,108. This is because the percentage of very high incomes (over $190,000) is higher in NYC compared to the rest of the country. NYC also has an aging population, and the percentage of old people is also increasing, with spikes of two to 3% over the past few years (Figure 2c). Both income and population health play a prominent role affecting COVID rates.

Crowding also plays a role in the relevant analysis (Figure 3). A housing unit is considered crowded if the total number of people living in the unit exceeds the number of rooms. As rent prices increased throughout all five boroughs, renters faced a steepening tradeoff between space and price. Many renters had to find roommates or move family members into shared rooms (e.g., siblings sharing a room), thus contributing to crowding. New units in the city are growing at a much slower rate than the population, contributing to high demand. Increased crowding is especially seen in the Bronx, Brooklyn, and Manhattan; it occurs more often in low‐income neighborhoods like Sunset Park (26%) and University Heights (19%). Over the past 20 years, the population in NYC has increased 1.5 times faster than the construction of new buildings, e.g., the Bronx has 120,000 more people than 20 years ago, but only 35,000 new homes.

In addition, the change in availability of houses is important to note. According to the Furman Center, the New York City Department of Buildings authorized 28,787 new housing units in 2019, but only 20,590 in 2020: a decrease of 28.5%. According to the same report, the number of units that received a certificate of occupancy decreased by 24.8% between 2019 and 2020. Note that the report suggests the peak in 2015 may be due to a rush for permits before the 421—a tax exemption expired.

Regression

Regression analysis is a very popular statistical modeling technique used throughout different years of the competition. For the 2021 competition, many teams found that COVID rates could be predicted by home ownership rate, renter income, and crowding (Figure 4). Areas with low income and communities with high crowding were particularly affected by the COVID‐19 pandemic. This relationship is supported by the significant p‐values for renter income and crowding percentage and the high adjusted R<sups>2</sups> which was 0.78. A slightly more surprising result was that home ownership rate is a significant predictor that positively correlates with COVID rates.

Cluster analysis

Another popular type of analysis is clustering. Several teams clustered PUMAs based on factors like poverty rate, adults with independent living, crowding percentage, home ownership rate, LMI borrowers, homeowner income, renter income, and rent burden. The variables were standardized and principal components were calculated. The first two components explained over 80% of the variance observed within the data. The loadings for both components are broken down in Table 3.

3 TABLE Cluster analysis: Principal components loadings

<table><tbody><tr><td><graphic href="" /></td></tr></tbody></table>

PC1 may be interpreted as selecting for high‐income, upper‐class neighborhoods. Homeowner and renter income as well as the home ownership rate are positive, while undesirable factors such as poverty and crowding are strongly negative. PC2 selected for bordering neighborhoods. Poverty as well as homeowner and renter income are positive, while home ownership and rent burden are negative. These factors seem to conflict; however, certain neighborhoods in NYC display this relationship.

It is interesting to note that socioeconomic similarities cause the clustering of neighborhoods in East Harlem with some areas in lower Brooklyn, whereas neighborhoods in lower Manhattan share more characteristics in common with West Brooklyn than they do with neighborhoods in upper Manhattan. A few teams also found significant differences between COVID rates for different clusters, further supporting the notion that the economic components were highly correlated with COVID rates (see Figure 5).

Results

The analysis process provided students with hands‐on experience with real data in a competitive setting. Each team had to examine massive data sets, some as large as hundreds of thousands of records and tens of columns. They gained knowledge in utilizing popular tools like Excel, Tableau, RStudio, MS Power BI, and MS SQL Server, as well as programming languages like R and Python. Appendix B provides an example of a poster that student teams produced for the 2021 competition.

The results raised housing and health care issues that concern all of us. Through this experience many students learned that adopting the correct technological techniques to the relevant data sets regarding individuals, institutions, communities, and systems is not sufficient to address systemic inequity and minimize human diversity in judgment. Collaborations between data scientists and domain specialists from other disciplines, especially the social sciences, are vital for designing comprehensive, human‐centered, and ethical solutions while preventing or mitigating biased, unsuitable, or unexpected effects. Interdisciplinary approaches to social justice alter the questions we ask and the way we evaluate the outcomes.

The benefit of the competition often goes beyond the event itself. In 2019 and 2020, Manhattan College data analytics faculty and students worked on a collaborative effort continuing the analysis developed for the BAC@MC 2019. A data analytics student, already instrumental during the 2019 competition, continued providing valuable input for a paper published in 2020 in the International Journal of Energy Sector Management titled "Case study on renewable energy in New York: bridging the gap between promise and reality" (Tomoiaga et al., [24]). In 2022, another data analytics student presented his results, "Housing Comorbidities of NYC," at the Mathematical Association of America (MAA) Metro New York section (Tunnock et al., [23]).

OUTCOMES

Judges' feedback

Competition participation as a high‐impact practice is best appreciated if feedback from judges is shared with students, thereby offering students opportunities to reflect on their performance. Hence, the organizing committee is committed to sharing the judges' feedback for every team after the competition has concluded.

Teams are evaluated based on a rubric developed by the organizing committee. The competition rubric is designed to evaluate each team's performance in addressing the questions based on the given scenario, analyzing data, drawing conclusions, and communicating results with others (the competition rubric can be found in Appendix C). Furthermore, depending on the phase of the competition, teams are judged by separate panels: faculty advisors from other competing institutions during Round One and, if they advance, professional judges during Round Two. The judging rubric is provided to judges beforehand so they have sufficient time to become acquainted with the assessment criteria before the competition starts. Additionally, judges are welcome to provide extra remarks or ideas for competing teams.

As seen by the sample feedback form (Appendix D), each team receives the average scores of evaluating categories together with any comments/suggestions made by judges. If a team was qualified to participate in Round Two, its ranking is also included. In addition, as remarked by a faculty advisor ("Thank you for sharing the feedback from the judges. This is very helpful for us in future competitions!"), sharing comments with teams provides opportunities for improvements.

Participants' satisfaction survey

The organizing committee of BAC@MC strives to offer satisfactory (or better) experiences to every participant. To successfully provide positive experiences, the organizing committee seeks continuous improvement by identifying the areas that need the committee's attention. To this end, the organizing committee has conducted a postcompetition satisfaction survey each year since 2016.

The purpose of conducting the postcompetition satisfaction survey is twofold. First, the survey aims to understand how satisfied participating faculty advisors and students were, focusing on the following three major areas: curriculum (e.g., competition themes and data sets), format (e.g., keynote speakers and judging processes), and overall learning experiences (e.g., overall satisfaction and likelihood to reattend). These satisfaction levels are measured using a 5‐point Likert scale (e.g., 1: extremely unsatisfied, 5: extremely satisfied). Second, the survey asks participants to provide any further comments related to the aforementioned areas or suggestions for the upcoming years' competitions (e.g., suggestions for themes or keynote speakers). Open‐ended formats are used to allow participants to share their comments or suggestions.

The organizing committee developed the initial postcompetition satisfaction survey in 2016 and has been using it as a basic survey template since then. However, because the committee actively considers participants' feedback in designing the following year's competition, the details of each year's survey are modified to reflect the nature of the given year's competition. For the ease of data collection, the postcompetition satisfaction survey is made using the Qualtrics survey platform and sent to participating faculty advisors and students via email. Typically, the online Qualtrics survey link is sent to participants one week after the competition, with several email reminders sent in the following weeks. The numbers of satisfaction survey participants in the past years are summarized in Table 4.

4 TABLE Numbers of satisfaction survey participants

<table><thead><tr><th>Year</th><th>2017</th><th>2018</th><th>2019</th><th>2021</th><th>2022</th></tr></thead><tbody><tr><td>Faculty</td><td>9</td><td>9</td><td>11</td><td>10</td><td>10</td></tr><tr><td>Student</td><td>&#8211;</td><td>22</td><td>9</td><td>16</td><td>49</td></tr><tr><td>Total</td><td>9</td><td>31</td><td>20</td><td>26</td><td>59</td></tr></tbody></table>

After the survey responses are collected, results are shared with all organizing committee members and discussed as a priority when the regular organizing committee meeting resumes every academic year. The organizing committee is particularly interested in identifying issues and concerns raised by participants, then finding ways to solve them for the upcoming competition. For instance, in 2019, the overall satisfaction level of competition experiences among faculty seemed lower than previous years (Figure 6). To respond to these concerns, the organizing committee closely reviewed the comments in open‐ended questions and found that faculty advisors felt the competition's research questions were not articulated to the best extent. Hence, the committee was devoted to developing the research questions for the next year's competition more succinctly. Note that the overall satisfaction level was not measured in 2016 and only measured with faculty members in 2017.

In 2021, the competition format was changed to virtual due to the COVID‐19 pandemic. The committee designed the competition similar to previous years (i.e., a 2‐day competition schedule), but in a virtual format, hoping to deliver a similar experience to participants. However, from the participants' shared comments, the organizing committee learned that a 2‐day virtual competition may not be ideal due to the so‐called Zoom fatigue (e.g., "Having a two‐day conference combined with the competition is tough. It is different if it was in person but for a virtual component—it was just too long to be on the computer.") After mulling over various options, the organizing committee decided to host a 1‐day virtual competition in 2022 to minimize participants' Zoom fatigue while reenacting the previous years' competition experiences. Accordingly, the organizing committee had to modify how the competition takes place in a day; for instance, participating students in 2022 were asked to do slide presentations both in Round One and Round Two (if advanced), rather than making a poster presentation for Round One and a slide presentation in Round Two as done previously. As a result, the overall satisfaction level among faculty advisors bounced back in 2022. Though there is still room to improve participants' satisfaction with their competition experiences, specifically to catch up to the in‐person competition experiences, this anecdotal evidence describes how the organizing committee uses the postcompetition survey as a tool to improve the overall quality of the competition. A copy of the satisfaction survey is provided in Appendix E.

Similarly, the organizing committee has continuously tried to improve participants' competition experiences in a detail‐oriented manner. In the earlier years, for example, the organizing committee learned that the competition's curriculum (e.g., themes and data sets) did not meet participants' expectations (e.g., "Educational funding was challenging and disheartening to students," "The first phase of data was extremely large and it was difficult to manipulate.") As described in the previous sections, the competition curriculum is critical to assuring participants' learning experiences. Hence, the committee explored various business‐related and timely issues for each year's competition curriculum and spent a significant amount of time to evaluate and design each year's competition theme as well as data set. Reflecting such efforts, the satisfaction levels of the competition's themes and data sets have improved over the past years (see Figures 7 and 8).

The organizing committee also appreciates participants' positive feedback since it serves as a great motivation for "taking the rough with the smooth" in preparing for each year's competition. Here are some examples of positive feedback we have received:

<bold>Students</bold>: (anonymously shared through the post‐competition satisfaction surveys)

"Thank you for giving us students the opportunity to compete in a competition with other schools from around the country."

"It was an honor to be part of this wonderful experience and so hope this event continues to grow. It is truly a fantastic opportunity for professional and self‐growth."

<bold>Faculty Advisors</bold>:

"We thank you for creating, organizing and hosting this competition. We can see how much work and effort goes into this on your part. We definitely want to return next year's competition. Our team had great fun and were fully engaged in this year's competition."

"BAC@MC is a great competition that assesses student skills, from managing a large amount of data to visually presenting information to a wide variety of stakeholders to lastly, problem solving under time constraints with new and important information and communicating that information to a panel of industry and subject matter experts. It is a competition that we will be returning to year after year."

<bold>Judges</bold>:

"BAC@MC is definitely one of the better Business Analytics competitions in the country. The quality of participants, the organization of the competition and the involvement of the industry is impressive and very relevant to the requirements of the industry. A good recruiting source for analytics leaders and a very good avenue for displaying talent for the universities."

"Beyond the surprise of the analytical expertise they demonstrated I left the event feeling a sense of inspiration and awe. These undergraduates—children in many ways—delved into a very challenging data, balanced that with the economic and social consequences, and made recommendations with poise and confidence that most companies can only dream of."

CONCLUDING REMARKS

This article explored building a community of learners around the academic field of business analytics using an intercollegiate competition called BAC@MC. It examined two main objectives: (1) introducing undergraduate students to the experiences of data analysis that lead to decision‐making and (2) engaging students in a high‐impact learning practice around a real‐life problem. Designing and implementing this yearly competition has proved a rewarding and effective endeavor, one that is assessed and improved year after year.

The innovation in this activity centers around the deliberate approach to its design. As mentioned in the background section of this article, high‐impact practices are well accepted and encouraged in undergraduate business education. Intercollegiate competitions have been available for undergraduate finance students through investment portfolios competitions. Other forms of data analysis competitions have focused primarily on technical expertise through hackathons and similar activities. BAC@MC introduced a new approach to student competition that is centered around data analysis as a tool for decision‐making. The exercise is designed to highlight the need for data‐driven processes in decision‐making. Year after year, participants, faculty, students, and judges appreciate the careful consideration and articulation of an evidence‐based, real‐world decision situation. If participating in BAC@MC has inspired other universities to start their own events, it would provide a flattering punctuation on the innovation and effectiveness of Manhattan College's Business Analytics Competition.

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By Salwa Ammar; Min Jung Kim; Amir H. Masoumi and Alin Tomoiaga

Reported by Author; Author; Author; Author

Salwa Ammar is Professor Emeritus of Business Analytics at the O'Malley School of Business in Manhattan College, New York. She received her PhD in Management Science from the Warrington College of Business Administration, University of Florida. Her research interests include the development of methodologies in systems that can be used to evaluate performance in business, industry, and government. Her scholarly contributions also include pedagogical developments and teaching innovations in business. She served as the dean of the business school where she spearheaded the development of the business analytics undergraduate major and the BAC@MC intercollegiate competition.

Min Jung Kim is Assistant Professor of Marketing at the O'Malley School of Business in Manhattan College, New York. She received her PhD in Marketing from Texas A&M University. Her research interests include personal finance decisions, logo design/redesign, and cultural influences in ways which those impact consumers' responses to marketing strategy. She has been affiliated with the organizing committee for the BAC@MC intercollegiate competition in various capacities since 2016, and is currently serving as a co‐chair.

Amir H. Masoumi is Associate Professor of Operations and Supply Chain Management at the O'Malley School of Business in Manhattan College, New York. He received his PhD in Management Science from the Isenberg School of Management, University of Massachusetts Amherst. His main research interests focus on the analysis of health care supply chain networks, including blood banking systems, pharmaceutical supply chains, medical device sales industry, human milk banks, as well as disaster relief operations. He has been affiliated with the BAC@MC competition in various capacities since its inception to the date.

Alin Tomoiaga is Associate Professor of Data Analytics at the O'Malley School of Business in Manhattan College, New York. He received his PhD in Business Statistics from Texas Tech University. His primary scientific interests are on genetics and the links between epigenetics and various diseases. For the past 3 years, he has served as the Manhattan College Data Analytics team coach for the BAC@MC competition. His collaboration with undergraduate students led to joint peer‐reviewed publications and conference presentations.