Gregory Page

• Machine Learning
• Data Analysis
• E-Commerce Software Applications
• Web Development

MET AD 648 – Ecommerce

MET AD 654 – Marketing Analytics

MET AD 699 – Data Mining for Business Analytics

“Syntax is Temporary, Concepts are Forever.” Paper presented at the 15th Annual Computer Science and Education in Computer Science (CSECS) Conference, Fulda, Germany, June 28–July 1, 2019.

What is your area of expertise?
I teach Data Mining for Business Analytics (MET AD 699) and Marketing Analytics (MET AD 654), which are both part of the Applied Business Analytics program. I’m a generalist when it comes to data science. In my courses, we cover several types of supervised and unsupervised machine learning models. At the root of it all, though, is exploratory data analysis—the fundamentals of examining data, generating summary stats, and producing meaningful visualizations are things that I emphasize with my students, from the start of the semester to the finish.

Please tell us about your work. Can you share any current research or recent publications?
Yes—a couple of years ago, I co-wrote a textbook for my Marketing Analytics (MET AD 654) class. The book is freely-available online (www.lobsterland.net). I am looking forward to doing some revisions and touch-ups this summer, and perhaps finding a way to offer it in a print version, too.

How does the subject you work in apply in practice? What is its application?
I work to focus my research on areas that are connected strongly to policy and practice. Evaluation of firearms laws directly informs the choices of justice policymakers. Working on the impact, as well as the implementation issues, of innovative police reforms, like the co-response model, helps to guide police agencies in the field. The lessons we learn about “how to do it” are as important as evaluating “what to do” for our students in the field.

What are you currently working on?
I am always working on new ways to incorporate hands-on activities into the class sessions. This semester, I’ve been emphasizing ways to bring generative AI into those activities. For instance, I might ask the students to generate a formula or a graph in a very specific way, using any AI tool of their choice. Getting this right often requires more than just a quick copy-paste of the instructions into GeminiAI or ChatGPT—it often means that the students need to adjust the way that they’re prompting the AI to get it to deliver exactly what’s needed. After the students solve the problem, we review it together as a group, and we assess the different solutions that students have found.

How does the subject you work in apply in practice? What is its application?
Data analytics is all around us. Something I emphasize to my students is that we often need to distinguish between fundamental business problems and data problems. Data analysis—even good data analysis—is not a cure-all. It’s not going to fix something that’s fundamentally broken regarding a product or service.

How do your past experiences help to inform your teaching?
Before getting into teaching, most of my work experience comes from being a military officer. This might sound surprising at first, but military experience translates very directly into the classroom. So many of the important elements of teaching come down to clear communication and organization. These things are heavily emphasized in the military, too.

Please highlight a particular project within one of your courses that most interests your students. What “real-life” exercises do you bring to class?
At the end of the semester, my Data Mining students complete a final project based on Airbnb data. I change the city each time, and I change a few elements of the prompt, but I always use Airbnb datasets. The datasets contain a nice mix of variable types (categorical, numeric, boolean, etc.) and are “messy” enough to offer a lot of learning opportunities for data cleaning tasks. Because Airbnb is so well-known, students are able to understand the data right away, and they can often use the findings in job interviews. Interviewers like to ask about it when they see it on a resume—it’s much more relatable and interesting than, say, the migratory mating habits of the Arctic fox.

Is there anything else you would like to add?
Generative AI is here to stay. It’s part of our lives, it’s part of our students’ lives, and it will only become even more ubiquitous with time. We need to be sure that we are designing our curricula with this in mind. Instead of telling our students not to use it for assignments and class exercises, we should embrace it. We should teach them how to use it in a way that keeps critical thinking at the forefront. If we want to be sure that students actually understand the key concepts that we’re teaching, then we should design assessments that are completed in class, with pen and paper.