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. 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, algorithms, high-performance computing, data management and analysis, visualization, software, and
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.
There are two required
courses (6 credits) and two elective courses (6 credits).
Required courses (take two courses from the following):
16:332:566 (S) Introduction to Parallel and Distributed Computing (3)
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
16:332:563 and 564.
16:332:572 (S) Parallel and Distributed Computing (3)
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.
16:137:602 Cloud Computing and Big Data (3)
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.16:137:603 Python for
Data Science (3)
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.
At least two elective courses must be taken for the
Electives can be taken from any of the following fields of specialization
(two courses, 6 credits)
- Analytics and data science
- Computational methods in engineering
(biomedical, chemical, civil, electrical, industrial, mechanical, etc.)
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 at http://mbs.rutgers.edu. See also the entry in this catalog for Business and Science 137.
Students must attend at least six colloquia or workshops in CDS&E for the
certificate. Colloquia in CDS&E-related topics will be listed on the
Rutgers Discovery Informatics Institute website or the mbs.rutgers.edu website.