Get to Know St. Lawrence's New Data Science Major
St. Lawrence’s new data science major is interdisciplinary, hands-on, and full of opportunities for students to apply their coursework to real-world opportunities like internships and research.
Associate Professor of Statistics Ivan Ramler and Associate Professor of Computer Science Lisa Torrey shared what makes St. Lawrence’s program unique and how its innovative liberal arts curriculum will best prepare graduates to make an impact in the rapidly growing data science field.
What makes St. Lawrence’s data science major unique?
Ivan Ramler (IR): Many other data science programs are housed within a department with just a single field (either Mathematics or Computer Science or Statistics). Here, these three disciplines are housed together within the same department and all three contribute to the new data science major. This will give our students more opportunities to integrate what they’re learning. Further, the major is designed to encourage taking classes in an application field through a creative list of electives. Currently there are at least nine other disciplines with courses that count towards the Data Science major—including courses from popular programs such as Biology, Economics, Environmental Studies, and Psychology.
Lisa Torrey (LT): Another unique feature of the major is that its capstone requirement can be satisfied in a few unusual ways, such as a research fellowship or an industry internship. The typical Senior Year Experience options will still exist, but we expect that many students will be excited to be able to pursue a fellowship or internship as part of their major.
What are the benefits of majoring in data science at a liberal arts institution?
LT: We intend for majors to learn more than just the technical skills involved in data science. Liberal arts institutions are known for producing graduates with strong communication skills, which are important in any career. Also, our students will be asked to consider tough questions about the societal impacts of their work, both within and beyond their courses in our department.
IR: In my opinion, good data scientists have the ability to connect with subject matter from a wide array of fields. Many data scientists will need to apply their knowledge to other subjects—in the physical or biological sciences, social sciences, humanities, or arts. St. Lawrence’s well-rounded liberal arts education offers students a jumpstart on being able to make these different connections.
How will this program prepare students for their careers? What kind of careers can data science majors expect to pursue?
IR: Core requirements that include computer science, mathematics, and statistics courses give students the foundation to branch out in many ways for a career. For example, some data scientists approach their work from a computer science angle by pursuing careers in data architecture or engineering, while others approach it from the statistics side and may work as data analysts. Also, the capstone requirement allows majors to gain experience with real-life examples and applications.
LT: Data is everywhere, so data scientists are in high demand. Entry-level job postings for data scientists and data analysts number in the thousands. There are roughly three times as many job postings for data science as there are job searches, suggesting a shortage in the market. Our majors will be qualified for jobs in diverse sectors: business, health, sports, public service, and many more. They will also be well prepared to pursue graduate studies if they want to take that path.
What tools, technology, or software will data science majors be able to take advantage of to support their learning?
LT: Our students take many of their classes in computer labs, which allows for a hands-on learning style. Thanks to an anonymous donor, we will soon have a newly renovated classroom designed specifically for data science pedagogy. And thanks to a National Science Foundation grant, St. Lawrence owns a high-performance computing cluster that makes it possible to work with big data and tackle computationally-intensive tasks.
IR: Students in data science courses will gain experience in several of the most popular programming languages in data science—Python, R, and SQL. They will also encounter several professional-level programming environments such as Pycharm, Jupyter, and RStudio.
How will faculty in this program support their advisees’ career exploration?
IR: We work with our Center for Career Excellence to identify potential internships and careers for our students. For example, we’ve had members of the Center give presentations to students in our department about finding and applying for internships. We also use social media to advertise job postings that alumni contact us about.
LT: The department is also developing an Alumni Advisory Board to help us provide students with opportunities for mentoring. We have already begun to reach out to alumni in these fields, and we’d like to welcome interested alumni to reach out to us.
Tell us a little bit about faculty expertise in the department—who will students of this program be learning from?
IR and LT: Students will end up meeting many of the faculty in mathematics, computer science, and statistics, but we’ll introduce the computer Science and statistics faculty here, since they teach the majority of the courses in the data science major.
Computer Scientists:
- Charles A. Dana Professor of Computer Science Ed Harcourt helps design fingerprint recognition technology.
- Associate Professor of Math, Computer Science, and Statistics Lisa Torrey develops virtual agents that can teach themselves to do complex tasks.
- Associate Professor of Math, Computer Science, and Statistics Choong-Soo Lee specializes in esports analytics and network traffic analytics.
- Visiting Assistant Professor of Math, Computer Science, and Statistics Kevin Angstadt designs hardware to make big data analysis faster and invents ways to keep drones safe from cyberattacks during flight.
Statisticians:
- Professor of Mathematics, Computer Science, and Statistics Robin Lock is an author on a leading textbook for introductory statistics.
- Rutherford Professor of Mathematics and Professor of Math, Computer Science, and Statistics Michael Schuckers is a leader in sports analytics and biometrics.
- Professor of Math, Computer Science, and Statistics Jessica Chapman develops multiple-criterion optimization techniques and measures climate change by studying tree rings in driftwood.
- Associate Professor of Math, Computer Science, and Statistics Ivan Ramler leads data science projects applied to esports or other forms of entertainment.
- Assistant Professor of Math, Computer Science, and Statistics Matt Higham studies spatial statistics with ecological applications and writes statistical software packages.
What excites you most about the future of this program?
IR: It is a toss-up between having students who are interested in data science from the moment they start at St. Lawrence and the variety of new classes that we are already planning to offer (e.g. Machine Learning and Data Visualization).
LT: Data science projects can do a lot of good for society, but they come with risks: privacy violations, unintended biases, and exploitation of disempowered groups, to name a few. To address the societal impacts of data science, we are working on positioning ethical questions alongside technical ones in the upper-level courses of the major. That’s a new and exciting challenge for our technically-trained faculty.
