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Innovative Teaching

Economics Professor Daijiro Okada has students work in small groups in a digital classroom inside the new Rutgers Academic Building where innovative teaching is the norm. Screens for projecting notes and images; multiple white boards for students to show their work to each other; and 10 tables spread around the room with plenty of walking space make the perfect setting for Okada’s teaching style: mobile, interactive, and thought-provoking. “Being in a group creates a feeling of accountability,” he says. “I go from table to table to hear from students what they have learned, to pose new questions, and to help them master the material together.”

Professor Okada is just one example of the commitment and creativity that School of Arts and Sciences faculty bring to their role as teachers. Since its inception in 2007, the School has made excellence in teaching a fundamental part of its mission. Check out the stories below to see how innovation teaching is encouraging learning in the classroom across the academic spectrum from English to earth and planetary sciences and from genetics to mathematics.

eight bits make a byteAs today’s information technologies continue to generate vast and complex data sets, algorithmic analytical tools with solid foundations in computer science, statistical theory, and computer-human interaction have become indispensable to all aspects of life, from industry through healthcare. Rutgers University offers two unique Master’s of Science programs in Data Science, each focusing on a separate track within the discipline. Both tracks combine the knowledge and expertise of the Statistics and Computer Science departments, but they are distinguished by a difference in emphasis. We encourage applications to either of these tracks within the MSDS program. However, please note that prospective students may apply to one track only.


M.S. in Data Science-Statistics Track

The Statistics Track:

The M.S. in Data Science - Statistics Track is a 30-credit program that covers a broad range of topics, including probability, statistical methods, computational methods, data mining, and machine learning.

The program will allow students to:

  • Have a deep understanding of the nature of uncertainty, modeling, performance checking, and decision making.
  • Develop strong statistical and computational skills for data analysis.
  • Develop strong communication and leadership skills which are essential to a successful career as a data scientist.


M.S. in Data Science-Computer Science Track

The Computer Science Track:

The M.S. in Data Science – Computer Science Track is a 36-credit program that focuses on the latest systems, tools, and algorithms to store, retrieve, process, analyze, visualize, and synthesize large data sets. One of the program’s central goals is to create and integrate computerized tools that enhance decision making in social, scientific, and economical endeavors aiming to improve quality of life.

The program will allow students to:

  • Receive solid training in algorithms, probability, statistical learning, computing systems, data mining, machine learning, and visualization.
  • Acquire a deep understanding of the nature of uncertainty, modeling, performance checking, and decision making.
  • Implement interactive tools for data analysis and sense making.
  • Develop strong communication and leadership skills.

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