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ONLINE ANALYTICS MBA (AMBA)

Designed for Data-Informed Leadership

Recognized as the #1 Online MBA in the state of New Jersey by U.S. News & World Report.

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ANALYTICS MBA OVERVIEW

The Analytics MBA at Stevens is a STEM program that will advance your technical skills and management capabilities. Go beyond the typical MBA and gain the competitive edge you need to progress in your career. Our part-time, online program is led by industry executives and academics who will prepare you for success as both an analyst and entrepreneur.

  • Identify and assess opportunities for creating value using data-driven decision making

  • Use statistical software like R and SAS to analyze multivariate data

  • Visualize, interpret and communicate data to peers

  • Leverage design thinking to spur innovation and solve complex problems

By the Numbers

Employment
100%
Three months after graduation, 100% of MBA graduates in the Class of 2020 accepted job offers.*
In the Nation
#21
Ranked among the top schools in the business analytics category by U.S. News & World Report (2020)
In N.J. Online MBA
#1
For six years in a row, Stevens’ Online MBA has been ranked #1 in New Jersey by U.S. News & World Report (2021)
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 business school 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 Analytics MBA at Stevens is structured around three areas of greatest need for the leaders of tomorrow's technology-driven organizations, who must be able to speak the language of business, understand how to apply innovations within business units and across the enterprise, and interpret data to identify trends and make strategic recommendations. Classes give you a broad set of skills that are applicable in any industry.

Prior to enrollment, you must have completed previous coursework in Financial Management, Financial and Managerial Accounting, and Economics for Managers. In the event you are missing any of the program prerequisites, designated online courses are available to satisfy this requirement. Please contact your enrollment advisor for more information.

Project success depends, largely, on the human side. Success in motivating project workers, organizing and leading project teams, communication and sharing information, and conflict resolution, are just a few areas that are critical for project success. However, being primarily technical people, many project managers tend to neglect these "soft" issues, assuming they are less important or that they should be addressed by direct functional managers. The purpose of this course is to increase awareness of project managers to the critical issues of managing people and to present some of the theories and practices of leading project workers and teams.

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.

The study of marketing principles from the conceptual, analytical, and managerial points of view. Topics include: strategic planning, market segmentation, product life-cycle, new product development, advertising and selling, pricing, distribution, governmental, and other environmental influences as these factors relate to markets and the business structure.

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.

An interdisciplinary course which examines the elements of, and the framework for, developing and implementing organizational strategy and policy in competitive environments. The course analyzes management problems both from a technical-economic perspective and from a behavioral perspective. Topics treated include: assessment of organizational strengths and weaknesses, threats, and opportunities; sources of competitive advantage; organizational structure and strategic planning; and leadership, organizational development, and total quality management. The case method of instruction is used extensively in this course.

This course serves as a second semester sequence in corporate finance. Students enrolling should have a mastery of the topics covered in Managerial Finance I (MGT 623) including time value of money, capital budgeting, risk adjusted hurdle rates, managerial accounting, and ratio analysis. Among the topics covered in MGT 638 are: leverage on the balance sheet and weighted average cost of capital; bankruptcy, turnarounds, and recapitalizations; international currency hedging; stock options; private equity valuation; mergers and acquisitions; and the issuance of public and private securities.

Innovative organizations are led by people who relentlessly nurture creative collaborations. These leaders stimulate imagination, teach others how to turn imagination into creativity, and build group structures and processes to enable people to turn creative ideas into innovations that drive business results. This course builds individual awareness of creativity and collaboration skills while increasing the student’s capacity for both. It teaches the science behind techniques, tools, interpersonal skills, leadership skills, organizational strategies, and environmental designs that increase group effectiveness. The overall goal is to strengthen the student’s ability to lead others to address meaningful problems and possibilities wherever they may be found.

This course introduces the most relevant algorithms of generative and discriminative estimation. Main topics include autoregressive and moving average models, seasonality, long memory ARMA and unit root test, volatility modeling, linear methods for classification, kernel methods, support vector machines, Bayesian and Markovian graphical models, EM algorithm, inference, sampling methods, latent variables, hidden Markov models, linear dynamical systems, reinforcement learning, and ensemble methods (boosting, bagging and random forests.) The course will also explore applications of the learning algorithms to finance, marketing, and operations.

This course deals with the theory and methods associated with design thinking, a problem-solving protocol that spurs innovation and solves complex problems. Design thinking involves a unique form of inquiry which goes well beyond product and service design. Students will develop an appreciation for design and develop skills for studying design systems. These concepts and methods have wide applicability as they can be used to design organizations of people, information structures, compensation systems as well as the entire consumer experience. Applying these approaches can often create entirely new systems that are more useful and usable. The logic of this approach can sometimes solve “wicked problems” which have defied previous solutions.

This course is intended to integrate all previously taken courses in the program by presenting a set of increasingly complex business problems. These problems can be solved through analytic skills taught in this and previous courses. In particular, the course is intended to reinforce the understanding of analysis as a way to build models that can focus attention on parts of the system that can be improved through intervention. The early part of the course uses synthetic data and empirical data readily available for analysis. The second part of the course encourages students to state and solve their own problem, gathering their own data as a part of the analytic process.

This course leads students through the identification, analysis, definition, and deployment of service opportunities within public and private organizations. Each of these phases is analyzed in detail to encompass the principal activities, methods, tools and techniques applied in the respective phase. Students will learn how to identify appropriate supporting techniques and information technologies for the different phases of the service life cycle, assess the role of technology, and gauge the organizational impact of service-focused operations. The objective of the course is to enable students to identify, implement and evaluate innovative service offerings in their organization.

In this course, students will evaluate and create their own prospective business strategies. They will develop an understanding of entrepreneurship and innovation in starting and growing a business venture. Students will be given an opportunity to actually start their own business or create a business in their company by learning how to take advantage of the new order of business opportunities of the information age. This course’s main objective is to show students how to identify these opportunities, be able to formulate and evaluate both qualitatively and quantitatively whether the opportunity is worth pursuing, and, of course, how it may be pursued. Actual case studies and experiences will be intertwined with the course content.

This course deals with the basic problems of managing a project, defined as a temporary organization built for the purpose of achieving a specific objective. Both operational and conceptual issues will be considered. Operational issues include definition, planning, implementation, control, and evaluation of the project. Conceptual issues include project management vs. hierarchical management, matrix organization, project authority, motivation, and morale. Cases will be used to illustrate problems in project management and how to resolve them.

Career Outlook

Earning an Analytics MBA prepares you for success in a technical management role.

JOB TITLE

EMPLOYED

AVERAGE INCOME

Marketing Managers

285,000

$132,000

Market Research Analysts

735,000

$64,000

Financial Managers

707,000

$129,000

Management Analysts

912,000

$86,000

General and Operational Management

2,469,000

$100,000

Financial Analyst

496,000

$81,000

Source: Emsi Labor Market Data, 2021

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

  • Deloitte
  • Prudential
  • EY
  • PwC
  • ICIMS
  • Verisk Analytics

PROGRAM ADMISSION REQUIREMENTS

  • PROGRAM PREREQUISITES

    Students are required to have taken courses in financial management, accounting, and economics. Specific non-credit online courses will also satisfy these requirements. Reach out to your enrollment advisor for further details.

  • ACADEMIC TRANSCRIPTS

    Your application must include official transcripts from all universities you have attended, or in which you are currently enrolled. These records must show your name, the name of the university attended, enrollment dates, coursework completed and grades assigned. Your bachelor's degree must come from an accredited institution, and you must have attained a B average, to be considered.

  • PROFESSIONAL RÉSUMÉ

    Work experience is not a requirement for the Analytics MBA 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 (39 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

Our faculty includes five National Science Foundation (NSF) CAREER winners as well as researchers who consult with companies such as Microsoft, IBM, Google, Bell Labs and other top industry firms.

Pranav Garg

PRANAV GARG

ASSISTANT PROFESSOR

Gary Lynn, Ph.D.

GARY LYNN, PH.D.

ASSOCIATE PROFESSOR

Dr. Peter Dominick

DR. PETER DOMINICK

TEACHING ASSOCIATE PROFESSOR

Bei Yan

BEI YAN

ASSISTANT PROFESSOR

Michael zur Muehlen

MICHAEL ZUR MUEHLEN

ASSOCIATE PROFESSOR

PAM BURKE

AFFILIATE FACULTY

German Creamer

GERMAN CREAMER

ASSOCIATE PROFESSOR

Peter Koen

PETER KOEN

ASSOCIATE PROFESSOR

Ionut Florescu

IONUT FLORESCU

RESEARCH PROFESSOR

Stefano Bonini

STEFANO BONINI

ASSISTANT PROFESSOR

VICTOR MOYA

ADJUNCT

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GRE/GMAT Not Required