C-Level View | Feature
Right from the Start: Data Analytics in Introductory Courses at Bentley University
A Q&A with Mark Frydenberg
Course designers and curriculum planners at colleges and universities are, of course, aware of the technologies that are reshaping our world most: AI, machine learning, block chain, and data analytics among them. Such technologies are often reflected in upper-level courses so that students can graduate with market-relevant knowledge — but this is mostly based on the advanced electives they select.
At Bentley University in Waltham MA, however, students in Computer Information Systems experience some of these highly impactful technologies as fundamental elements of introductory courses, preparing them for much more in-depth study in advanced electives.
Here, Mark Frydenberg, senior lecturer in computer information systems and director of the CIS Sandbox (a technology/social learning space at Bentley University), takes the example of data analytics and explains how the topic is now a key part of the university's foundational, introductory coursework in computer information systems.
Thanks to new and redesigned introductory courses, Bentley students now tackle data analytics and work with large, real-world data sets early in their college careers.
"It became clear to me that we needed to change the Introduction to Programming with Python course to give students a real taste of data analytics." —Mark Frydenberg
Mary Grush: What were Bentley's first steps to include data analytics in introductory courses?
Mark Frydenberg: At first the CIS department — this was about four years ago — started teaching a course called Introduction to Programming with Python. Python was becoming popular because it is the programming language that is used most commonly in the data analytics field. So, students were taking the Python course in preparation for their data analytics courses down the road.
We first taught Introduction to Programming with Python as a traditional programming course; from a computer science perspective. There weren't many textbooks that taught Python from a data-driven programming perspective. I realized that most students were taking the course because it was a prerequisite to the upper-level data analytics courses — but we weren't even introducing the topic [of data analytics] in our course. It became clear to me that we needed to change the Introduction to Programming with Python course to give students a real taste of data analytics.
Grush: How did you make that change?
Frydenberg: We changed the structure of the course to be project-based. So, the first two thirds of the course remained relatively unchanged: programming fundamentals; data structures; file processing… But the last third introduced basic capabilities of Python modules like Matplotlib, NumPy, and pandas, which are used for data analytics. We replaced the final exam with a project to create an interactive application to explore real-world data.
Grush: What data do the students have to work with?
Frydenberg: Students work with a subset of a large, complex data set (maybe 8,000 rows). For example, we took a real data set from Airbnb that has listings of rentals, location information, amenities, availability, ratings, pricing… everything you'd expect an Airbnb data set to include. So, using Python and a module called Streamlit, which makes it possible to create interactive web applications using Python, students are challenged to create an interactive interface that allows users of their applications to customize charts and maps to visualize the data.
Students are challenged to create an interactive interface that allows users of their applications to customize charts and maps to visualize the data.
Grush: How have the students reacted to the project-based course?
Frydenberg: The course, still called Introduction to Programming with Python, has become extremely popular here, and students can see the benefits for themselves easily. I think teaching the course from a front-line business perspective, instead of from an engineering and computer science perspective, has made this offering unique.
Grush: And will it become less unique as you offer more courses based on the same principles?
Frydenberg: Of course! This approach is becoming more common as interest in careers as data analysts and data scientists increases.
Grush: Less unique but still more useful…
Frydenberg: Certainly.
Grush: Is there another introductory course at Bentley modeled on similar principles?
Frydenberg: Yes, there is a course called Data-Driven Decision Making that our department introduced last year. It uses SQL and a tool called Tableau, among other data visualization tools, to expose students to the use of data and databases in problem solving. This is a new course, added to the curriculum to prepare students to analyze complex problems using a variety of data sources and present their ideas effectively to others. Students need to understand how data is stored and organized so they can clean, manage, and analyze it to solve problems well.
Data-Driven Decision Making is a new course, added to the curriculum to prepare students to analyze complex problems using a variety of data sources and present their ideas effectively to others.
Grush: How have students picked up on the world of data analytics? Is this something that you are marketing to them, or are they coming to Bentley with their own expectations about the data analytics field? Is Bentley driving the market, or is the market driving Bentley?
Frydenberg: Bentley is responding to market needs by preparing our graduates with the skills they need to thrive in a world driven by data. Whether it's creating Python applications or using industry-standard tools to analyze data, one thing that sets the Bentley experience apart is the way we have chosen to integrate data analytics into introductory courses, and to teach them from that perspective.
Bentley is responding to market needs by preparing our graduates with the skills they need to thrive in a world driven by data.
And the students coming here are more aware of the analytics field and its potential for growth in the job market. That's why we offer both a major and a minor in data analytics, and one reason students are attracted here.
Grush: Are you starting to see the first cadre of students graduating, since you introduced data analytics into the Python course? Are you seeing how data analytics is affecting their careers?
Frydenberg: Absolutely. We have current statistics showing Bentley graduates at the top in the country for job placements across all our majors. While that alone doesn't provide a correlation with our introductory course strategies, we do have wonderful anecdotal evidence that data analytics courses were helpful for students securing and being successful in their new jobs.
One of my students recently mentioned that she demonstrated her course project during a job interview, and it helped her land the job. She was able to show her potential future employer a pretty impressive application she had built, and she was able to talk about how it works.
Grush: Do you have any industry partnerships in the CIS Sandbox based on data analytics?
Frydenberg: This past fall the CIS Sandbox started an industry engagement program, in which companies sponsor the cost of a CIS Sandbox tutor for a semester, in exchange for the benefit to the company of a student intern for 40 hours working on a company project — often overseen by a Bentley alum. Three of the four such partnerships we engaged in during A/Y 2021-2022 involved analyzing, visualizing, and storing data. Those company projects went a long way towards providing Bentley University with insights into the significance of data analytics and the importance it should hold for the experiences we provide for our students.
[Editor's note: Image courtesy Bentley University; from a data analytics project by Revanth Munnangi, a student in the Introduction to Programming with Python course.]
