Analytics: discovery informatics and data sciences is offered as a
concentration within the professional science master's program described under
Business and Science 137. The objective of the master of business and science
degree with a concentration in analytics: discovery informatics and data sciences is to prepare students for data-driven decision making, and its
applications to a broad range of domains including science, engineering, and
business. It brings together fields of data management, statistics, machine
learning, and computation. Students will obtain a variety of skills including
the ability to manage and analyze very large data sets, to develop modeling
solutions to support decision making, and develop a good understanding of how
data analysis drives decision making. This curriculum also targets those
interested in data-enabled computational science and engineering (escience).
All students in the analytics: discovery informatics and data sciences
concentration must take four core courses from the following list:
Required:
16:137:550 Fundamentals of Analytics and Discovery Informatics (3)
Decision trees, including algorithms, association mining, statistical modeling, linear models, and instance-based learning. Case studies; class project.
Statistics (one course):
16:960:563 Regression
Analyses (3)
Analytics and Data Mining (Select one course, others can be
taken as electives):
16:137:550 Fundamental
of Analytics
(3)
16:960:588 Data Mining (3)
22:960:575 Data Analysis
and Decision Models (3)
26:198:644 Data Mining (3)
Database Systems (Select one course, others can be taken as
electives):
16:198:541 (S) Database
Systems (3)
16:332:569 (F) Database
System Engineering (3)
17:610:557 Database Design
and Management (3)
26:198:641 Advanced
Database Systems (3)Programming (Select one course, others can be taken as
electives):
16:332:503 Programming
Methodologies for Numerical Computing and Computational Finance (3)
16:332:566 Introduction
to Parallel and Distributed Computing (3)
16:332:572 Parallel and
Distributed Computing (3) (data-intensive computing, cloud computing, scalable data-analytics, accelerators, etc.)
Course Descriptions
Full course descriptions can be found under respective departments/graduate programs.
Electives are available at http://psm.rutgers.edu.
Concentration Coordinators:
Professor Manish Parashar
parashar@rutgers.edu
Professor Shanrenu Jha
shantenu.jha@rutgers.edu
Professor Deborah Silver
silver@jove.rutgers.edu
