How computer science takes data science to the next level

Computer science allows us to actualize data science’s vast potential

How computer science takes data science to the next level

We live in a world that’s been shaped by computer science to a remarkable degree. One need only consider the likelihood that most people are reading these words on a device small enough to fit in their pockets — a device packed with more tools than a Swiss army knife: a camera, a cinema screen, a myriad of communication channels, and dedicated apps for booking transport, accommodation, food delivery, and more.

At Rice University, we’ve been at the forefront of this computer science revolution for longer than most. In fact, in 2019 we celebrated our 35th anniversary as a computer science department. We did so with an alumni gathering that reflected the Rice computer science diaspora that exists across the globe, from tech giants like Google and Facebook to rising companies like DoorDash and Two Sigma, and in the fertile start-up environment of Silicon Valley.

In educating the computer scientists of tomorrow, we consider both the timeless principles of engineering and problem solving as well as areas of specialization that graduate-level computer scientists consider relevant to their professional development. This brings us to data science — the field of study that focuses on extracting meaningful insights from data.

Anyone setting out to earn a master’s degree would be wise to look at the bigger picture of the investment they’re making in themselves and their career. In the 21st century, “data science” is an important part of that picture. In an increasingly complex and technologically advanced world, computer scientists may need to be able to extract meaningful insights from data. A prospective student might ask, “Will a computer science master’s provide me with the most relevant skill set for the careers of today and tomorrow? And how does computer science leverage the strengths of data science to set me up for success?”

These are good questions to ask. Data science is a key part of our Master of Computer Science curriculum — a curriculum that was designed with both the interests of our students and the needs of today’s employers in mind. For a master’s student, that means an appropriate level of focus on databases, machine learning, data visualization and statistics. But it also involves a theoretical fluency with software construction and the principles of algorithms as well as a broader understanding of computer systems.

Together, computational and data science skills don’t simply add up to bigger picture thinking. They’re the building blocks of bigger-picture doing. A masters-level graduate seeks not only to navigate the current tools and systems in order to derive useful and productive insights, but to build their own tools and systems for problem solving of the scope and scale that drive technological and business category disruption.

Our interdisciplinary approach to data and computation is reflected in the presence of the Ken Kennedy Institute at Rice. There, our Rice faculty are engaged in a mission to “collaboratively solve critical global challenges by fostering innovations in computing and harnessing the transformative power of data.” This exciting and expansive mission has led to many achievements, including award-winning work led by Rice computer scientists for AI-driven COVID -19 research.

At Rice, we empower CS graduate students to not only crunch big data in search of insights, but to develop the systematic thinking required to wrestle with big problems and engineer innovative solutions. Though data science is foundational to the graduate computer science curriculum, computer science is the discipline that allows us to actualize data science’s vast potential in transformative ways.