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ONLINE MASTER OF SCIENCE IN BUSINESS INTELLIGENCE & ANALYTICS (MSBI&A)

Master the Engines of Enterprise

No GMAT/GRE Required At This Time

Ranked the #11 Business Analytics program by TFE Times (2020).

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BUSINESS INTELLIGENCE & ANALYTICS OVERVIEW

The M.S. in Business Intelligence & Analytics at Stevens is designed for a new kind of leader. Harness the power of data science to drive your organization’s competitive advantage and leverage tools like A.I., deep learning, and predictive analytics to challenge assumptions and make evidence-based decisions. As a Business Intelligence & Analytics student, you will hone the skills you need to:

  • Understand the basic methods underlying multivariate analysis using R

  • Use mathematical models to analyze risk phenomena and implement risk-aware solutions

  • Apply mathematical optimization models to improve processes

  • Design and manage data warehouse and business intelligence systems

  • Develop supply chain analytical skills to solve real-life problems

By the Numbers

Employment
93%
93% of 2020 MSBI&A graduates accepted job offers three months after graduation.*
In the Nation
#11
Named a top business analytics master's program by TFE Times (2021)
Average Compensation
$94,000
Average compensation for Stevens MSBI&A graduates (Class of 2020)
in Business Analytics
#16
Stevens is ranked among the top schools in Business Analytics online (U.S. News & World Report, 2021)
In N.J.
#2
Ranked among the top School of Business Graduate Programs by U.S. News & World Report (2022)
*Data reflects the on-ground program. Fall 2021 is the first time the program is offered online.

Coursework

The M.S. in Business Intelligence & Analytics program trains students to understand both the business implications of Big Data and the technology that makes that data useful. In doing so, it leans heavily on the high-tech infrastructure at Stevens, which gives students direct exposure to the kind of challenges they will engage in the workplace.

This course explores data-driven methods that are used to analyze and solve complex business problems. Students will acquire analytical skills in building, applying and evaluating various models with hands-on computer applications. Topics include descriptive statistics, time-series analysis, regression models, decision analysis, Monte Carlo simulation, and optimization models.

This course focuses on the design and management of data warehouse (DW) and business intelligence (BI) systems. The course is organized around the following general themes: business value of data, planning and business requirements, data architecture, data design, implementation, business intelligence, deployment, data integration and emerging issues. Practical examples and case studies are presented throughout the course. This course also includes hands-on application in various software packages.

This course covers basic concepts in optimization and heuristic search with an emphasis on process improvement and optimization. This course emphasizes the application of mathematical optimization models over the underlying mathematics of their algorithms. While the skills developed in this course can be applied to a very broad range of business problems, the practice examples and student exercises will focus on the following areas: healthcare, logistics and supply chain optimization, capital budgeting, asset management, portfolio analysis. Most of the student exercises will involve the use of Microsoft Excel’s “Solver” add-on package for mathematical optimization.

This course focuses on understanding the basic methods underlying multivariate analysis through computer applications using R. Multivariate analysis is concerned with datasets that have more than one response variable for each observational or experimental unit. Topics covered include principal components analysis, factor analysis, structural equation modeling, multidimensional scaling, correspondence analysis, cluster analysis, multivariate analysis of variance, discriminant function analysis, logistic regression, and other methods used for dimension reduction, pattern recognition, classification, and forecasting. Through class exercises and a project, students apply these methods to real data and learn to think critically about data analysis and research findings.

In this course, students will learn how to analyze social network data and apply the analyses to develop marketing strategies. The course focuses on network concepts, including graph-theoretic fundamentals, centrality, cohesion, affiliations, equivalence, and roles, as well as design issues, including data sampling and hypothesis testing. Theoretical areas covered include embeddedness, social capital, homophily, and network growth. Another focus of this course is on marketing applications of social network analysis, in particular the use of knowledge about network properties and behavior, such as hubs and paths, the robustness of the network, and information cascades, to better broadcast products and search targets After taking this course, students should be able to statistically analyze and describe large scale networks, model the evolution of networks, and apply the network analyses to marketing research.

This course will focus on Data Mining & Knowledge Discovery Algorithms and their applications in solving real world business and operation problems. We concentrate on demonstrating how discovering the hidden knowledge in corporate databases will help managers to make near-real time intelligent business and operation decisions. The course will begin with an introduction to Data Mining and Knowledge Discovery in Databases. Methodological and practical aspects of knowledge discovery algorithms including: Data Preprocessing, k-Nearest Neighborhood algorithm, Machine Learning and Decision Trees, Artificial Neural Networks, Clustering, and Algorithm Evaluation Techniques will be covered. Practical examples and case studies will be present throughout the course.

This course uses advanced technologies, such as IBM's Blue Mix and Google's TensorFlow, as building blocks, allowing student teams to exercise their ingenuity to develop applications that use AI and machine learning in entirely new business application areas. The products of cognitive computing are beginning to appear in the marketplace, while so-called "deep-learning" AI applications are finding their way into healthcare, energy management, security, marketing and financial services.

The field of Big Data is emerging as one of the transformative business processes of recent times. It utilizes classic techniques from Business Intelligence & Analysis, along with new tools and processes to deal with the volume, velocity, and variety associated with big data. As they enter the workforce, a significant percentage of BIA students will be directly involved with big data either as technologists, managers, or users. This course will build on their understanding of the basic concepts of BI&A to provide them with the background to succeed in the evolving data centric world, not only from the point of view of the technologies required, but in terms of management, governance, and organization. Tools will include Hadoop, Hbase, and related software.

Covers marketing analytics techniques such as segmentation, positioning, and forecasting, which form the cornerstone of marketing strategy in industry. Students will work on cases and data from real companies, analyze the data, and learn to present their conclusions and make strategic recommendations.

Introduces the tactical and strategic issues surrounding the design and operation of supply chains, to develop supply chain analytical skills for solving real-life problems. Topics covered include: supplier analytics, capacity planning, demand-supply matching, sales and operations planning, location analysis and network management, inventory management and sourcing.

Business intelligence and analytics is key to enabling successful competition in today’s world of “big data”. This course focuses on helping students to not only understand how best to leverage business intelligence and analytics to become more effective decision makers, making smarter decisions and generating better results for their organizations. Students have an opportunity to apply the concepts, principles, and methods associated with four areas of analytics (text, descriptive, predictive, and prescriptive) to real problems in an application domain associated with their area of interest.

This course surveys applications of artificial intelligence to business and technology in the digital era, including autonomous transportation, fraud detection, machine translation, meeting scheduling and facial recognition. In each application area, the course focuses on issues related to management of A.I. projects, including fairness, accountability, transparency, ethics and the law.

Career Outlook

Graduates of the MSBI&A program bring a blend of business and data science skills to a variety of industries — technology, finance, telecommunications and beyond.

PROSPECTIVE OCCUPATIONS FOR BUSINESS INTELLIGENCE & ANALYTICS GRADUATES

JOB TITLE

EMPLOYED

AVERAGE INCOME

Business or Data Analyst

905,000

$87,000

Data Scientist

33,200

$126,800

Operations Research Analyst

99,000

$86,000

Financial Analyst

397,000

$83,000

Source: Emsi Labor Market Data, 2021

OUR SCHOOL OF BUSINESS ALUMNI HAVE GONE ON TO WORK WITH THE FOLLOWING ORGANIZATIONS:

  • BMW
  • IBM
  • Exxon
  • Lockheed Martin
  • Goldman Sachs
  • UPS

PROGRAM ADMISSION REQUIREMENTS

  • PROGRAM PREREQUISITES

    Given the highly technical nature of this degree, students are required to have a background in calculus & statistics, and familiarity with a programming language. Non-credit online courses may satisfy these requirements. Reach out to your enrollment advisor for further details.

  • ACADEMIC TRANSCRIPTS

    Applicants must submit transcripts from all undergraduate and graduate institutions where credit was earned. You may submit unofficial transcripts during the application process. After admission, you will be required to submit official transcripts.

  • PROFESSIONAL RÉSUMÉ

    Work experience is not a requirement for the Business Intelligence & Analytics program, but the admissions committee does value applicants with professional experience. You must include a résumé with your application that highlights:

    • Academic Record

    • Work and internship experience

    • Leadership abilities

    • Professional aspirations

  • LETTERS OF RECOMMENDATION

    Your application must include two letters of recommendation. The strongest applications will include one letter from a current supervisor, and one from a former supervisor or previous employer who can speak to your leadership potential and discuss your professional performance.

TEST SCORE ACCOMMODATIONS DURING THE NOVEL CORONAVIRUS OUTBREAK

Due to the impacts of the coronavirus (COVID-19) on testing centers around the world, Stevens has made the following accommodations available to all students for the fall 2021 admissions cycle:

  • GRE/GMAT: Test scores are temporarily waived.

  • TOEFL/IELTS/DUOLINGO: Affected applicants may submit Duolingo English Test (DET) results in lieu of TOEFL/IELTS exam results.

Tuition & Cost

Per Credit (36 credits)
$1,776*
Application Fee
$60
Fee waivers available
Enrollment Deposit
$250
*Tuition rates based on Fall 2022 tuition rate effective September 2022. Tuition and fees are subject to change annually.

Key Dates & Deadlines

Term

Early Submit

Priority Submit

Final Submit

Start of Classes

$250 Deposit Waiver* and Application Fee Waiver Available.

Application Fee Waiver Available and Early Application Review.

Summer 2022

February 15, 2022

March 15, 2022

May 5, 2022

May 23, 2022

Fall 2022

May 23, 2022

June 27, 2022

July 25, 2022

September 12, 2022

* Applicants who apply by the early submit deadline and are admitted may be eligible for a $250 deposit waiver. Applicants who receive education assistance from employers or other tuition discounts are not eligible. Other eligibility conditions may apply.

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Faculty

Jordan Suchow

JORDAN SUCHOW

ASSISTANT PROFESSOR

Dr. David Belanger

DR. DAVID BELANGER

SENIOR RESEARCH FELLOW

Alkis Vazacopoulos

DR. ALKIS VAZACOPOULOS

TEACHING ASSOCIATE PROFESSOR

Bei Yan

BEI YAN

ASSISTANT PROFESSOR

Khasha Dehnad

KHASHA DEHNAD

ADJUNCT PROFESSOR

Feng Mai

FENG MAI

ASSISTANT PROFESSOR

Christopher Asakiewicz

CHRISTOPHER ASAKIEWICZ

INDUSTRY ASSOCIATE PROFESSOR & DIRECTOR BUSINESS INTELLIGENCE & ANALYTICS PROGRAM

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GRE/GMAT Not Required