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Collaboration with Doctoral Programs
Differences Between Doctoral and Master’s-Level Education
Any discussion of why or how to work with doctoral students probably needs to begin with an understanding of the differences between master’s-level education and doctoral-level education.
Simply put, in a master's program, students learn how to "do stuff", while in a doctoral program students learn how to develop "new stuff to do".
There are broadly two types of doctoral programs that you will likely encounter - the Professional Doctorate (e.g., Doctorate of Business Administration or DBA, EngD or Doctorate of Engineering) and the Doctorate of Philosophy (PhD). While universities offer other kinds of doctorates (e.g., the Educational Doctorate or EdD, Juris Doctorate or JD), these degrees are less likely to be aligned with the analytical needs of your organization1. In Chapter 2, Table 2.1 provides the classifications of universities and colleges in the United States. Almost all PhD programs will be offered through universities classified as “High” or “Very High” Research Activity. Doctorates are primarily offered by “High”, “Very High”, or “Professional Doctorate” universities (see Table 2.1). Both Professional Doctorates and the PhD
Table 5.1 Distinctions Between the DBA and the PhD Degrees
are considered research degrees, both qualify the graduate to teach at the university level, and both require a dissertation. However, there are important distinctions. For example, consider the distinctions between a DBA and a PhD presented in Table 5-1.
Most master’s degrees require 1—2 years of study, while most doctoral programs require 4+ years of study. However, the difference is not just additional years of study - doctoral students must engage in independent, scholarly research that will make a meaningful contribution to their discipline.
Tlie requirement that doctoral students, both PhDs and Professional, must engage in independent research, where master’s-level students typically do not (although some do), cannot be overstated. At our university, we see student applicants as well as corporate partners who do not appreciate the difference between the doctoral requirement to engage in “research” versus the master’s-level requirement to engage in “projects” - master’s students are more typically “doers”, while doctoral students are (should be) “independent researchers”. In fact, one of the interview questions that we pose for our doctoral program is “What do you see as the main differences between a master’s-level degree and a doctoral degree!'’ Students who cannot articulate a difference are not offered a second-round interview slot.
I tell many of our applicants that in many ways a doctoral student in data science has more in common with a doctoral student in sociology than with a master’s-level student in data science.
The point is that there are common skills doctoral students develop2 which most master’s-level students do not (e.g., conducting a literature review, formulating a novel research question, developing a proposal, producing peer-reviewed scholarship, and - the ultimate doctoral experience - defending a dissertation). These experiences and developed skills are consistent to all doctoral programs from chemistry to theology, mathematics to business.
Between the two of us, we have almost 50 years of experience in academia (Bob is older) - where many of those decades have included connecting the classroom experience with organizations seeking analytical talent. Over that time, we have worked with hundreds of analytical hiring managers — and almost all of them ask about some combination of five critical skills. While these skills can be found at the undergraduate and master’s level, they are actually foundational to completing a PhD:
For some reason, doctoral programs seem to facilitate fertility; in our PhD program, almost every married student had a baby during their program. As a rule, this event is typically not integrated into a doctoral timeline and is not part of the expected deliverables - research or otherwise.
5. Collaboration. While some doctoral programs may expect students to work completely independently, interdisciplinary collaboration is increasingly becoming more common; most students work with multiple faculty members, other students, and research sponsors on concept development, experiments, simulations, papers, and presentations. This is particularly true in analytics and data science where students are working across multiple disciplines like computer science, mathematics, business, healthcare, and statistics to solve problems and engage in research teams. As the data science community moves away from the concept of “unicorns” - single individuals who are trained in every aspect of data science - to teams of people who specialize in the different roles across the analytical continuum, the need for collaborative skills and the ability to work productively in an interdisciplinary team is frequently flagged as a “critical” skill.
Figure 5.1 Areas of study for practicing data scientists.
In a 2018 study3, practicing data scientists were asked what they studied in school. The results in Figure 5-1 indicate the relevance of interdisciplinarity and collaboration.
It is important to understand too that the term “data scientist” increasingly encompasses a whole category of positions that require varying degrees of specialization: data analyst, data architect, data engineer, business analyst, marking analyst, and business intelligence expert. In the context of larger analytical projects, all of these roles will be required - and required to work together.