BU Today: Let’s talk about the new CDS building. Is it still scheduled to open in fall 2022?

Yes, as far as I can tell, it is on schedule and everything is moving along. A few weeks ago, we went through the exercise of narrowing down the options for the furniture that BU will be taking bids on. That’s a good sign, and we could not be more excited. The next time you walk along Comm Ave this summer be sure to look it up—I think we are up to the fifth floor so far.

BU Today: Until that building is completed, where is CDS located? Where will the classes be taught?

Our current home base for CDS is a newly renovated suite of offices and collaboration spaces on the first floor of the Math & Computer Science (MCS) Building at 111 Cummington Mall. While we expect that some of our classes will be taught there, others will be taught in classrooms assigned by the registrar’s office around campus. Once the CDS building is completed, students can expect to take most, if not all, CDS courses in that building.

BU Today: What about students who are intimidated by mathematics? Should they consider this program anyway?

They certainly should consider this program. The whole pedagogical framework for the undergraduate program in data science is meant to be accessible to students with minimum or no prior exposure to college-level mathematics, or students who did not connect with abstract mathematics in high school. This is not to say that mathematical foundations are not an important part of the data science major. Quite the contrary; they are important, but we have designed the curriculum in an integrative fashion so that students learn the math they need when they need it.

BU Today: What do you mean by learning the math they need when they need it? How is that different from the current practices in STEM disciplines?

Rather than thinking of the mathematical and computational foundations of data science, such as calculus, probability, statistics, linear algebra, and programming, as stand-alone prerequisites that students must take before they are ready to get their hands on exciting real-world problems, we have designed the ramps to the major in a way that provides students with “immediate gratification” by having students iterate over increasingly deeper mathematical and computational concepts. Each iteration shows students how these deeper concepts translate to better solutions to real-world data analysis. We believe that this spiral pedagogy is not only the antidote to what has been called “math anxiety” and “math phobia,” but also just a better way to learn the foundations of data science.
 
Interestingly, starting with a “minimum viable product” and then iterating over it to improve it is what is considered a best practice in software innovation. We are basically using that agile approach to teach data science too, and we believe that by doing so we prepare students to follow this approach as they continue to develop their data science skills and competencies long after they graduate. Data science is and will continue to be a fast-evolving field, and the best training for data scientists is one that puts a priority on this agile approach to continuous integration of new methods.

BU Today: You’ve said that the data science degree is different from most degree majors. Could you talk about that?

Yes. It’s important to understand that the data science degree has characteristics that are different from other degrees. Most importantly, there is no one-size-fits-all pathway to completing the major.
 
While the data science major draws from, and builds on, traditional STEM disciplines, it is a union of these disciplines rather than an extension of just one. This is why the spiral pedagogy I mentioned before makes a lot of sense.
 
Data science is also highly interdisciplinary. As such, students will have the opportunity to take electives in other departments related to a particular domain of interest—for example, in environmental sustainability, health sciences, journalism, or ethics. Finally, data science is also highly applied—one learns by doing. As such, the program emphasizes experiential learning and has a practicum requirement, which can be completed through curricular and cocurricular pathways such as those offered through BU Spark! that provide students the skills to apply data science to domain-specific questions through projects for internal and external partners.

BU Today: It sounds like this degree is well suited for students looking for a second major.

Yes. The data science major is very friendly to a minor and those who are hoping to pursue a dual degree. Eventually, we hope that students will be able to complete double majors across colleges; the DS major will lend itself well to that in the future. It leaves a lot of room for students to explore different fields and interests. In fact, with only 64 required credits, students can do as many as half of the courses required for a BU degree in other departments. It’s also important to say that this is a highly flexible major. Once done with the foundational and core requirements for the major, students can get the same data science degree by following one of two different paths. One path is more technology-focused, getting students deeper into statistics, machine learning, software and data engineering, and AI. The other path is what we call Data Science in the Field, which is more about connecting data science with a particular discipline or set of disciplines.

BU Today: How will students decide which path to take?

Due to the flexible nature of the program, we are putting a huge emphasis on advising. Some students may know which path to take, but others will need more direction. We really want students to talk to us and let us help them develop their plans.

BU Today: What kind of job titles and careers should students in this major expect to have when they graduate?

That’s a good question, and it’s also hard to answer, because the careers and job titles are all over the place, given how prevalent data science is in many industries and in many sectors of the economy. But let me try to answer it this way: data science job titles are usually a combination of two terms—the first is the word “data” or any associated technology such as “information security,” “machine learning,” or “AI,” and the second term is a word that is more aligned with the type of work, such as “scientist,” “researcher,” “engineer,” “architect,” “developer,” and “analyst.” So, job titles such as “data scientist,” “information security analyst,” “machine learning engineer,” and “AI developer” fall under that.
 
I should also say that there is quite an overlap with titles typically associated with related degrees in computer science, computer engineering, information systems, and statistics, such as “software engineer,” “product manager,” “solution architect,” “business analyst,” and “SAS programmer.”

BU Today: What can you tell students about why they should consider this new major?

Students should think about majoring in data science not only because they are excited about data science as a technical subject with amazing career opportunities, but also because they are passionate about a particular discipline or cause and want to explore the ways data-driven approaches can make a difference. The program puts students in front of real data and big questions. They will learn the tools of data science and the foundations of data science by being immersed in curricular and cocurricular experiential learning opportunities leading them to complete a practicum in collaboration with industry or with a nonprofit.

BU Today: Bottom line: how would you describe the goal of this program?

The major is really about putting students in a position where they can make a bigger impact in whatever career they choose to pursue.