2021-2022 Caspersen School of Graduate Studies
Data Analytics
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About the Program
The digitization of information, including archives and library collections, business transactions, healthcare records, and social networks, just to name a few, has transformed the world. The exponential growth of digital information leading to big data sets necessitates a new set of digital skills and technologies in order to extract and make meaning from these data. Data management, extraction, analysis, communication, visualization, computer simulation, and modeling are increasingly used for research and inquiry across all academic disciplines and are used in an array of industries. In many areas the change has been revolutionary, transforming the nature of knowledge itself. For example, without computing technology, we simply could not know what we do today about genomics, neuroscience, or geography. Further from traditional science disciplines, data, supported by tools that access, process, summarize, and visualize it, have given us Google Translate, GPS, instant access to centuries’ worth of music and art, and much more. Data science has arguably democratized knowledge and information (if sometimes imperfectly). With this digital transformation of academia and the workforce, it is not surprising that data science and data analytics jobs are projected to grow at some of the fastest rates in the near future.
The Data Analytics program is an applied program that teaches students how to draw information from data. The curriculum involves courses in statistics, data science, and programming, as well as applied data analytics projects and internship opportunities across many different disciplines and industries. Students complete the program with a portfolio of data analytics projects highlighting the application of their skills to internship and case study projects.
Director
Sarah Abramowitz, PhD (sabramow@drew.edu), Mathematics
Advanced Research
Data Analytics students focus on the intersection of statistics and computer science, with content knowledge from another discipline or industry and an emphasis on applying skills and technologies in case studies courses, internships, and capstones aligned with a student’s interests. Experiential learning is a critical component of this curriculum, in line with Drew University’s mission across all three schools.
Curricular Components
Courses emphasizing Big Data explore how to obtain, prepare, and manage data from a wide variety of sources. Students work with big data scraped from the web and from social media.
Courses emphasizing Data Analysis explore how to master various data analytical techniques. Students apply data analytics to numerous disciplines, through data analysis, visualization, computer simulation, and computer modeling. Students learn the uses, potential, and limitations of the tools of computing technology as a foundation for research and knowledge acquisition in disciplines and in society.
Courses emphasizing Communication of data-driven conclusions explore the results based on an analysis of real-world data. Students work collaboratively. Applied data science is multidisciplinary involving the interplay between statistics, computer science, and different disciplines. The focus of all classwork is on practical applications and the communication of results. Students leave the program with a portfolio of project work and with work experience from their internships.
Programs
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