PhD in Big Data Analytics


The PhD in Big Data Analytics is an interdisciplinary STEM PhD program focusing on systems and technologies for processing data and information. Unlike pure data science programs, this program includes the human and social implications of information and technology, bringing in critical components of cognition, ethics, biases and storytelling into a strong, big data analytics curriculum.

This program will graduate advanced big data practitioners, researchers and scientists who can work with large data, write code, develop models and build systems, and do so while acutely aware of potential biases and ethical uses issues. Students will develop theoretical and applied skills, including how to design, implement and evaluate information-focused big data technologies that support decision-making across social and organizational contexts.

Why the focus on an interdisciplinary program? Many existing PhD programs offer training in all of the stand-alone scientific fields such as statistics, mathematics, computer science, or information systems, but they do not unify the salient ideas from these fields.

In that sense, graduates become experts in a relatively narrow area in, for example, statistical modeling of data, but are inexperienced and unaware of how to parse and store data or how to code “apps” and develop solutions that automatically make decisions based on the data models or how to evaluate the human and societal impact of the developed data solutions and systems.

This PhD program combines human and technical skills with analytical abilities required to support decision-making by today’s leaders and innovators. A big data analytics PhD will introduce students to a truly interdisciplinary program and diverse perspective to important problems and opportunities for society that are driven by the availability of big data.

Why now? An increasing number of companies are looking for professionals with experience in big data analytics, and they are hard to find, especially in the educational spectrum, i.e. at the expert/PhD level. In addition, there is an increasing demand for faculty with PhDs in this field. This program is timely as we are seeing deep problems in society  (polarization of society, fake news, algorithmic bias) where analytics-driven solutions alone struggle to be sufficient — problems where the broader perspective of building intelligent systems by being aware of broader issues and human aspects becomes increasingly important as well.

Programs that bring together curriculum and faculty expertise from multiple areas (such as information systems, mathematics, psychology, computer science, etc.) will play a critical role in this broader context.