Welcome and thanks for visiting! As my colleagues will attest, I have been meaning to start this blog for some time because I think that we’re doing some pretty exciting stuff here in Data Analytics at Denison (#DAatDU) that is worth sharing. We hope to convey some of this excitement and chronicle the development of a truly unique interdisciplinary program that is growing faster than we could have imagined. Posts will be periodically written by any number of faculty and students associated with the program on any number of topics. We hope you find them interesting, and always welcome your comments and questions.
We are officially in the second year of the program, but it really started brewing in response to a call for proposals for new majors during the 2014-2015 academic year. In response, a group of about a dozen faculty representing seven disciplines in the quantitative, natural, and social sciences joined forces to design a program that would empower students to solve problems in a data-rich world. The major was designed to build on our strengths at Denison, but also fill gaps in our existing curriculum. We also wanted it to be attractive to a diverse group of students with a variety of interests (not just math and computer science students) and to fully embrace the liberal arts.
The major, which was approved the Denison faculty in Spring 2016, consists of four parts:
Computer Science and Mathematics foundation (4 courses)
In the CS courses, students learn the basics of problem solving and programming, data storage, databases, data wrangling, and client/server architectures. The introductory course uses my book and a novel new Data Systems course was designed last summer by my colleague Tom Bressoud. In the Mathematics courses, students learn Calculus, probability, experimental design, statistical inference, hypothesis testing, and regression. The programming languages used are Python, R, and SQL. Students who wish to pursue more technical depth have plenty to choose from among our Mathematics & Computer Science courses.
Project-oriented Data Analytics courses (4 courses)
DA 101 has no prerequisites and introduces students to the data analytics cycle; types of data; wrangling, summarizing, and visualizing data; and communicating results orally and in writing. They also begin to confront ethical and social dimensions of DA. In DA 301, the “Denison Practicum,” students engage in semester-long projects for a real client on campus. My colleague Sarah Supp will likely blog about the course later this semester. DA 350, Advanced Methods in Data Analytics, is being taught for the first time this semester by my colleague Anthony Bonifonte, who will also probably have more to say about it here later. Finally, in DA 401, the Senior Capstone, students will undertake individually-designed projects in their individual interest areas. This course will be taught for the first time next year. All of these courses are project-oriented and hands-on using a variety of different data sets from a variety of sources.
Choice of disciplinary focus (3 or 4 courses)
Because no data exists in a vacuum, we want our students to understand how data is used in at least one specific discipline. There are currently seven different disciplinary foci that students can choose from: biology, economics, philosophy, physics, political science, psychology, and sociology. In the future, we will undoubtedly add more, and several are already under discussion.
Summer internship
Finally, we require that every major undertake a DA-oriented internship or research experience in the summer after their junior year. Our first juniors will engage in their internships this summer, and we are currently working with the Knowlton Center to support these students’ application processes.
This curriculum follows pretty closely the recommendations from the PCMI summer workshop in 2016, which three of my colleagues attended. I think the main differences are twofold: we may be a little lighter on the minimum mathematics requirements in favor of a much stronger focus on interdisciplinary perspectives. As I said above, we wanted our major to be attractive to a diverse group of students, including students who would not otherwise choose to take a CS or Statistics class.
Also for this reason, Data Analytics at Denison is an interdisciplinary program that is administratively independent of the Math & CS department. The program is administered by a committee of nine faculty: seven from the departments mentioned above, plus two fabulous new colleagues – Anthony Bonifonte, an applied mathematician, and Sarah Supp, a macro-ecologist, – whose homes are the DA program.
We are currently in the second year of the program and already have about 75 majors among the classes of 2019, 2020, and 2021. So things are humming along. More to come!