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Actuarial and Statistical Analysis
African Studies 016
Analytics: Discovery Informatics and Data Sciences
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Applied Computing
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Required Courses in Business and Management
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Actuarial and Statistical Analysis Concentration
Analytics: Discovery Informatics and Data Sciences Concentration
Applied Computing Concentration
Biomedical Engineering Concentration
Biotechnology and Genomics Concentration
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Statistics and Biostatistics Concentration
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User Experience Design (UXD) Concentration
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Divisions of the University

Analytics: Discovery Informatics and Data Sciences Concentration

Camden Newark New Brunswick/Piscataway
  Graduate School-New Brunswick 2017 Programs, Faculty, and Courses Business and Science 137 Analytics: Discovery Informatics and Data Sciences Concentration  
Graduate School-New Brunswick

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 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:



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.

16:137:551 Advanced Analytics and Practicum(3)
This course involves a large project in analytics.

16:137:552 Python Methodologies (3)
An introduction to computer programming with the Python programming language: conditionals, iteration, functions, data handling, object-oriented programming, and simple data structures. Emphasis on data handling and processing.


16:960:563 Regression Analysis (3)

Database Systems:

17:610:557 Database Design and Management (3)

Big Data Computing:

16:137:602 Introduction to Cloud Computing and Big Data (3)

Please note: Basic statistics and computing courses can be taken as part of the M.B.S. degree program and are needed for the analytics and regression course above.

Basic Statistics Courses:
01:960:401 Basic Statistics for Research (Fall/Spring/Summer) (3)
01:960:484 Basic Applied Statistics (Fall/Spring/Summer) (3)

Basic Computing Courses:
16:137:603 Python for Data Science (Python) (Fall/Spring/Summer) (3)
56:137:500 Essentials of Computational Science (Python) (Fall) (3)
16:332:503 Programming Methodologies for Numerical Computing and Computational Finance (C++) (3)

Course Descriptions

Full course descriptions can be found under respective departments/graduate programs.
Electives are available at

Concentration Coordinators:

Rutgers University-New Brunswick

Professor Manish Parashar

Professor Shantenu Jha

Professor Deborah Silver

Rutgers University-Camden

Professor Debashis Kushary
For additional information, contact RU-info at 732-932-info (4636) or
Comments and corrections to: Campus Information Services.

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