Home Computer Science
|
|
|||||
Navigating Universities – Where to StartUniversities 101Most of us are familiar with universities in some way. You likely attended one or perhaps more than one. You likely have also had the notion that a university could be helpful in filling your organizational needs for talent. If your organization is large enough, you may have Human Resource representatives or even talent specialists that visit job fairs, university recruiting days, engineering or programming hackathons, and business competitions. Your organization may have a portfolio of universities they target for hiring. But what you may not have fully considered are the other reasons and opportunities for partnering with universities well beyond hiring - specifically those around research, innovation, consulting, community engagement, and current employee training and continuing education. Universities’ historic core functions are to discover new knowledge, to train, and to educate. Some universities offer a more specialized curriculum while some provide a more generalized liberal arts curriculum (think Cal Tech vs. Wellesley College). But before we discuss this universe of differences and ultimately how to work with analytics and data science programs - which also have differences — some generalizations are necessary. Most people are not at all familiar with how the work of universities gets done. In fact, the very term “university” connotes a very different scope of work than does “college”, or “institute”. There are other classifications within the university structure that depend on the number and types of programs that are offered, the number of doctoral degrees conferred annually, the amount of research conducted by faculty, the size of the institution, and a host of other factors. You should consider different (multiple) types of universities for collaboration for different organizational objectives - no university can be all things to all organizations. Consider an automotive example - if your work requires hauling bricks, a % ton pickup truck might fit the bill. If your goal is to commute an hour a day as cheaply as possible, an electric vehicle might be the best fit. A sports car with a manual transmission might be fun to drive (and sadly a dying art), but a sedan with an automatic transmission might be a more practical (albeit less fun) alternative to accomplish the same basic transportation objectives. Like the array of options for vehicles, so too do universities provide options for engagement in analytics and data science, and like cars, they all come with different base options and upgrade potentials. And at the risk of extending the automotive analogy too far, the highest end of a luxury automobile brand may have the top rating from a publication like Consumer Reports, but that does not mean that a less expensive model from the same automaker will perform as reliably. Similarly, universities at the top of the rankings earn those rankings largely because of their external research funding (most coming from the federal government) and academic publications, which has very little to do with the typical undergraduate experience or the faculty’s ability (or interest) to engage with the private sector. More on that later. Beyond the walls of the university, faculty come in different flavors. Some professors exclusively teach and have no research responsibilities, while some exclusively do research and have little or no interaction with students. One of the authors recently had a conversation with a research faculty colleague at a large university that generates over $1 billion (with a “b”) a year in external research funding (primarily from the U.S. Department of Defense). The university in question has almost 30,000 students. The faculty member said, “/ hear that we have students, but I have never actually seen one on campus". Needless to say, that faculty does not spend much time in the classroom. Or go to football games. All of this is to suggest that the professor you email to start a collaboration in analytics and data science, has a specific set of incentives at work that may promote your organization’s intended engagement or prevent it. You may have attempted to work with a faculty member in the past who was less than accommodating - our colleague who never met a student has zero incentive to take your call or collaborate with you (he has no incentive to take our call). Perhaps you would like to explore teaching a course or guest lecture in a course as a starting point to engage students, but found it difficult to gain any traction with faculty. Tlie flip side of this, of course, are conversations most academics have had with corporate executives where the assumption is that after a full career, they want to semi-retire at the local university, imparting wisdom to young eager students. I often hear the common retort at dinner parties after learning of my profession: “'After I retire I am going to teach at a university". (Aside, this is not the best way to gain professor friends.) However, with some background information and context related to incentives and expectations for faculty and administrators across all of the different sizes and types of universities, there are rich opportunities to make integration between your company and a university partner mutually successful - and importantly can provide benefit to the students. We will discuss these perspectives in the coming sections. ![]() Illustrations have been created especially for this book by Charles Larson. |
<< | CONTENTS | >> |
---|
Related topics |