Certificate in Computational and Data-Enabled Science and Engineering (CDS&E)

Computational and Data-Enabled Science and Engineering 199

Program Offered: Certificate in Computational and Data-Enabled Science and Engineering

Co-director of certificate program, representing the program in business and science: Professor Deborah Silver, Executive Director, Professional Science Master’s Program, 848-445-5117.

Co-director of certificate program, representing the Rutgers Discovery Informatics Institute: Professors Manish Parashar

The certificate program is a program of the Graduate School–New Brunswick and is jointly supported by the Rutgers Discovery Informatics Institute (rdi2.rutgers.edu) and the Master of Business and Science program (mbs.rutgers.edu).

Certificate Program

The graduate certificate in computational and data-enabled science and engineering (CDS&E) is a cross-disciplinary graduate program based in the Rutgers Discovery Informatics Institute (RDI2) and the professional science master's program(Master of Business and Science degree). The goal of the program is to provide the necessary structures, learning opportunities, and experiences, beyond the more traditional university curriculum, that are necessary to drive science, engineering, and business using advances in cyberinfrastructure (CI). The program will provide an overlay on the existing curricular structures at Rutgers to give students a multidisciplinary experience, and will include foundational and applied courses spanning modeling and computation, analytics, algorithms, high-performance computing, data management and analysis, visualization, software, and multidisciplinary collaborations.

The certificate in CDS&E is open to all graduate students in the sciences and engineering. To receive the certificate, students must complete all the course requirements listed below.

Please choose 2 courses from this list (6 credits):

16:137:602 Cloud Computing and Big Data (3cr)
This course introduces fundamental concepts, technologies and innovative applications of Cloud and Big Data systems like distributed systems, map reduce programming model, distributed file systems virtualization and cloud models, etc. Engineering aspects like bridging the gap between analytics and data-driven platforms, performance evaluation and benchmarking. Explore recent technological solutions and research in Cloud and Big data. Hands on experience with in Hadoop, HDFS and big data databases, SQL, noSQL and newSQL.

16:332:566 (S) Introduction to Parallel and Distributed Computing (3cr) Systems, architectures, algorithms, programming models, languages, and software tools. Topics covered include parallelization and distribution models; parallel architectures; cluster and networked metacomputing systems; parallel/distributed programming; parallel/distributed algorithms, data-structures and programming methodologies; applications; and performance analysis. Programming assignments and a final project. Prerequisites: 16:332:563 and 564.

16:332:572 (S) Parallel and Distributed Computing (3cr)
Advanced topics in parallel computing including current and emerging architectures, programming models application development frameworks, runtime management, load balancing, and scheduling, as well as emerging areas such as autonomic computing, grid computing, pervasive computing, and sensor-based systems. Prerequisite: 16:332:566.

16:137:603 Python for Data Science (3cr)
This course covers the basics of Python and how it can be used for data science and computationally enabled science and engineering. A major project involving data is part of the course.

Please choose two elective courses (6 credits).
Electives can be taken from a wide variety of scientific disciplines, such as:

  • Analytics
  • Computational methods in engineering (biomedical, chemical, civil, electrical, industrial, mechanical, etc.) - For example, computational solid and fluid mechanics, finite element analysis, computational aerodynamics, computer simulation of materials, etc.
  • Computational methods in sciences (physics, chemistry, biology, etc.)
  • Computational methods in finance
  • Computational methods in social science
  • Medical/health informatics
  • Computer visualization

The full list of elective courses is available here. See also the entry in this catalog for Business and Science 137.

Students must attend at least six colloquia in CDS&E for the certificate. Colloquia in CDS&E-related topics will be listed on the Rutgers Discovery Informatics Institute website.