The MS in Business Intelligence and Analytics (BI&A) is 36 credit-degree, designed for part-time and full-time students who have completed undergraduate degrees in science, mathematics, computer science or engineering and are interested in pursuing careers in industry-specific analytical fields (e.g. financial, pharmaceutical, underwriting, manufacturing, information technology, telecommunications, energy and engineering, etc.). The Graduate Certificate in Business Intelligence and Analytics consists of four courses designed to help practitioners develop their knowledge and sharpen their analytical and management skills.
Define Your Future at Stevens As the market-demand for professionals with data management, analytical, and problem-solving skills increases, Stevens is one of only a dozen or so universities worldwide to offer this highly-desired degree. The U.S. alone needs as many as 190,000 additional people with "deep analytical skills," as well as, another 1.5 million "managers and analysts to analyze big data and make decisions based on their findings." – McKinsey Global Institute The Stevens MS BI&A program offers the most advanced curriculum available for leveraging quantitative methods and evidence-based decision making in support of business performance. It uses a cross-disciplinary perspective to teach the next generation of business analysts to be leaders in the management and interpretation of very large and complex dynamically-evolving data sets. Both theoretical and applied, the BI&A program blends courses in databases, data warehousing, data mining, social networking, and risk modeling. The program culminates in a “practicum” course that applies the concepts and techniques learned in prior courses to real-world problems in an industry of the student’s choice. Oral and written communications skills, analytical thinking, and ethical reasoning are emphasized throughout the curriculum.
The Stevens team led by Dr. Germán Creamer, including Financial Engineering Ph.D. students Yue Li, and Qiang Song won the Knight's Capital Prize for Best Use of Data in the Algorithmic Trading competition run by the University College London.
The design and execution of the BI&A program is guided by an advisory board of top executives with expertise in the financial services, life sciences, telecommunications, and retail industries.
About the Curriculum
The MS in Business Intelligence and Analytics is a 36 credit degree (12 courses), that is divided into six subject areas that conceptually comprise the field of BI&A. Each course combines relevant theories and techniques with applied exercises to illustrate practical industry applications of data analytics. Students also complete an industry-oriented capstone course where they apply the principles, and methods they have learned to real problems in the application domain of their choice.
Obtain the skills to collect, analyze, and interpret data in the following areas:
Strategic data planning and management
Data mining/Machine learning
Network analysis/Social networking
Risk, modeling, and optimization
BI&A by industry (e.g., pharmaceutical or financial)
Database and Data Warehousing
Optimization and Risk Analysis
Data Mining and Machine Learning
Social Network Analytics
Industry Practicum (select one)
Additional electives below are available for students who waive one or more of the required courses (e.g., MGT 615 or MIS 630). To waive courses students must have approval from a faculty advisor.
CS 506 Introduction to IT Security
CS 538 Visual Analytics
CS 559 Machine Learning
CS 578 Privacy in a Networked World
CS 581 Online Social Networks
CS 586 Machine Learning for Gaming
MGT 625 Investment and Capital Markets
Many Financial Engineering electives are available to BI&A students – See full list
MIS 760 IT Strategy
MIS 730 Integrating IT Architecture
MIS 661 Marketing Online
SOC 653 Introduction to Text Mining and Statistical Natural Language Processing
4 year undergraduate degree in science, mathematics, computer science, engineering or a related field required
Calculus (1 year)
A course in programming or programming experience
At least one course covering basic probability, hypothesis testing and estimation