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M.S. in Business Intelligence & Analytics and MBA Dual Degree Master's Program

Program Details

Degree

Master of Science or Dual-Degree MBA

Department

School of Business Graduate Program

Available

On campus

Contact

Office of Graduate Admissions1.888.511.1306[email protected]
Apply Now

Stevens School of Business and SKVM's NMIMS Deemed-to-be University offer a highly-coordinated dual degree program in Master of Business Intelligence & Analytics (BIA) and Master of Business Administration (MBA) providing a broad spectrum of skills ranging from deep and practical knowledge of quantitative skills to a comprehensive understanding of management principles with a global perspective. At the end of the program, you will get a Master of Business Administration degree from NMIMS and a Master of Business Intelligence & Analytics degree from the Stevens School of Business.

In the first year, you will enroll full-time at NMIMS, taking courses within the MBA program (12 credits). Then, you will enroll full-time at the Stevens School of Business, taking courses within the Business Intelligence & Analytics program (24 credits). You will receive a diploma from Stevens and a diploma from NMIMS after completion (approximately 24 months).

Program Benefits:

Global Vision: Get global management perspectives with exposure to international business practices.

Specialized Expertise: Become highly valuable in today's data-driven business environment where understanding both the business side and the analytical side is crucial.

Strategic Decision-Making: Gain the knowledge to make informed, data-driven decisions which is highly valued as businesses increasingly rely on data for strategic planning and decision-making.

Careers:

  • Business Analyst

  • Business Intelligence Manager

  • Financial Analyst

  • Marketing Analyst

  • Risk Analyst

  • Management Consultant

Stevens Institute of Technology logoStevens Institute of Technology

Stevens Institute of Technology is a premier, private research university in Hoboken, New Jersey, overlooking the Manhattan skyline. Since its founding in 1870, technological innovation and entrepreneurship have been the hallmarks of Stevens’ education and research. Within the university’s three schools, Stevens prepares its more than 8,000 undergraduate and graduate students for an increasingly complex and technology-centric world. Our exceptional students collaborate closely with world-class faculty in an interdisciplinary, student-centric, entrepreneurial environment, readying them to fuel the innovation economy. Academic and research programs spanning finance, computing, engineering and the arts expand the frontiers of science and leverage technology to confront the most challenging problems of our time. Stevens is consistently ranked among the nation’s leaders in ROI and career services and is in the top 1% nationally of colleges with the highest-paid graduates.

About The MS In Business Intelligence and Analytics Program

Data science has become the ultimate driver of competitive advantage. But few leaders understand the potential of Business Intelligence and Analytics (BI&A), deep learning, and predictive analytics as engines of the enterprise.

The Business Intelligence and Analytics (BI&A) master's program provides a blend of analytical and professional skills to help you become the kind of manager who challenges assumptions and uses data to make evidence-based decisions. At Stevens, you'll master new tools that will help you refine products, services, and strategies while setting the pace for your company in markets undergoing constant, technology-driven change.

The Business Intelligence and Analytics curriculum covers the concepts at the forefront of the data revolution — machine learning, language processing, web mining, optimization, and risk. Classes explore key business concepts while going beyond basics in R, SAS, Hadoop, Python and Spark. The program culminates in a capstone experience in which you'll work on a project, using real data, under the guidance of an industry mentor.

Core Courses

Select 5-6 Courses

MIS 636 Data Integration for Business Intelligence & Analytics - 3 Credits

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: Knowledge Discovery in Databases, Planning and Business Requirements, Architecture, Data Design, Implementation, Business Intelligence, Deployment, Maintenance and Growth, and Emerging Issues. Practical examples and case studies are presented throughout the course. This course also includes hands-on application in various software packages.

MIS 637 Data Analytics and Machine Learning - 3 Credits

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.

BIA 650 Optimization and Process Analytics - 3 Credits

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.

BIA 652 Multivariate Data Analysis - 3 Credits

This course introduces basic methods underlying multivariate analysis through computer applications using R, which is used by many data scientists and is an attractive environment for learning multivariate analysis. Students will master multivariate analysis techniques, including principal components analysis, factor analysis, structural equation modeling, multidimensional scaling, correspondence analysis, cluster analysis, multivariate analysis of variance, discriminant function analysis, logistic regression, as well as other methods used for dimension reduction, pattern recognition, classification, and forecasting. Students will build expertise in applying these techniques to real data through class exercises and a project, and learn how to visualize data and present results. This proficiency will enable students to become sophisticated data analysts, and to help make more informed design, marketing, and business decisions.

BIA 654 Experimental Design - 3 Credits

This course covers fundamental topics in experimentation, including hypothesis development, operational definitions, reliability and validity, measurement, and variables, as well as design methods, such as sampling, randomization and counterbalancing. The course also introduces the analysis associated with various experiments. At the end of the course, students present a project, which consists of designing an experiment, collecting data and trying to answer a research question.

BIA 658 Social Network Analytics & Visualization - 3 Credits

Given a data matrix of cases-by-variables, a common analytical strategy involves ignoring the cases to focus on relations among the variables. In this course, we examine situations in which the main interest is in dependent relations among cases. Examples of “cases” include individuals, groups, organizations, etc.; examples of “relations” linking the cases include communication, advice, trust, alliance, collaboration etc. Application areas include social media analytics, information and technology diffusion, organization dynamics. We will learn techniques to describe, visualize and analyze social networks.

BIA 660 Web Mining - 3 Credits

In this course, students will learn through hands-on experience how to extract data from the web and analyze web-scale data using distributed computing. Students will learn different analysis methods that are widely used across the range of internet companies, from start-ups to online giants like Amazon or Google. At the end of the course, students will apply these methods to answer real scientific question or to create a useful web application.

BIA 686 Practicum in Analytics - 3 Credits

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, methods associated with four areas of analytics (texts, descriptive, predictive, and prescriptive) to real problems in an application domain associated with their area of interest.

Elective Courses

Choose 2-3 courses

BIA 656 Advanced Data Analytics and Machine Learning - 3 Credits

The significant amount of corporate information available requires a systematic and analytical approach to select the most important information and anticipate major events. Statistical learning algorithms facilitate this process understanding, modeling and forecasting the behavior of major corporate variables. This course introduces time series and statistical and graphical models used for inference and prediction. The emphasis of the course is in the learning capability of the algorithms and their application to finance, direct marketing, operations, and biomedicine. Students should have a basic knowledge of probability theory, and linear algebra.

BIA 662 Augmented Intelligence and Generative AI - 3 Credits

This course explores the area of augmented intelligence and its implications for today’s world of big data analytics and evidence-based decision making. Topics covered as part of this course include: augmented intelligence design principles, natural language processing, knowledge representation, advanced analytics, as well as generative AI and deep learning architectures. Students will have an opportunity to build business applications, as well as explore how knowledge-based artificial intelligence, generative AI, and deep learning are impacting the field of data science.

BIA 670 Risk Management and Simulation - 3 Credits

Theoretical and practical aspects of risk assessment and management will be covered. Major topics include: Importance of innovation and technological changes in current competitive environment, risk and uncertainty, decision trees, binomial methods and derivation of Black-Scholes option pricing formula, extension of option methodology to non-financial (real) options, VAR (value at risk), a framework of risk assessment, and several real-world case studies. The course is designed for all students in the School of Technology Management.

BIA 672 Marketing Analytics - 3 Credits

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

BIA 674 Supply Chain Analytics - 3 Credits

Supply chain analytics is one of the fastest growing business intelligence application areas. Important element in Supply Chain Management is to have timely access to trends and metrics across key performance indicators, while recent advances in information and communication technologies have contributed to the rapid increase of data-driven decision making. The topics covered will be divided into strategic and supply chain design and operations, including -among others- supplier analytics, capacity planning, demand-supply matching, sales and operations planning, location analysis and network management, inventory management and sourcing. The primary goal of the course is to familiarize the students with tactical and strategic issues surrounding the design and operation of supply chains, to develop supply chain analytical skills for solving real life problems, and to teach students a wide range of methods and tools -in the areas of predictive, descriptive and prescriptive analytics- to efficiently manage demand and supply networks.

BIA 676 Data Stream Analytics - 3 Credits

In recent years, the progress in sensor technologies, RFID (Radio Frequency Identification) tags, smart phones and other smart devices has made it possible to measure, record, and report large streams of transactional data in real time. Such data sets, which continuously and rapidly grow over time, are referred to as Big Data Streams. Analysis of streaming data poses a number of unique challenges which are not easily solved through direct applications of well-known data mining methods and algorithms developed for traditional static data. This course will serve as a first course on the emerging field of "Data Streams Analytics". It will provide an introduction to IoT, sensors & devices, the architecture and environment in which these devices generate data streams, the data quality & data cleaning, data acquisition, and emerging methodologies and algorithms for knowledge discovery from data streams. Topics include: synopsis & sampling techniques, sliding windows, computing the entropy in streams, data streams correlations, change detection, outliers & anomaly detection.

BIA 678 Big Data Technologies - 3 Credits

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 (BI&A), along with a new tools and processes to deal with the volume, velocity, and variety associate with big data. As they enter the workforce, a significant percentage of BIA students will be directly involved with big data 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 also in terms of management, governance, and organization. Students taking the course will be expected to have some background in areas such as multivariate statistics, data mining, data management, and programming.

The following courses can be transferred from NMIMS to Stevens (Choose 4):

NMIMS Course

Stevens School of Business Course

Financial Accounting

FIN 615: Financial Decision Making

Business Analytics

BIA 610: Applied Analytics

Operations Management

BIA 600: Data, Models, and Decisions

Quantitative Methods I

BIA 652: Multivariate Data Analytics

Analytic Tools and Techniques for Decision Making

BIA 654: Experimental Design

Machine Learning Theory and Application

MIS 637: Analytics and Machine Learning

Marketing Analytics

BIA 672: Marketing Analytics

Operations and Supply Chain Analytics

BIA 674: Supply Chain Analytics

SVKM's NMIMS Deemed-to-be UniversitySVKM's NMIMS logo

Since 1981, NMIMS has today emerged as a globally reputed university. Always socially conscious, the Shri Vile Parle Kelavani Mandal (SVKM) made the decision to cater to the rising demand of management institutes in India which led to the birth of the Narsee Monjee Institute of Management Studies (NMIMS). NMIMS currently has 17 specialized schools with more than 17,000 students and about 750 full-time faculty members, 10 faculty members with Fulbright and Humboldt International Scholarships.

NMIMS MBA Courses

  • Financial Accounting

  • Business Analytics

  • Operations Management

  • Quantitative Methods I

  • Analytic Tools and Techniques for Decision Making

  • Machine Learning Theory and Application

  • Marketing Analytics

  • Operations and Supply Chain Analytics

M.S. in Business Information & Analytics: Facts & Figures

$102,875
Average Salary
$26,000
Average Signing Bonus
98%
Employed 6 Months After Graduation
#8
Best Online Masters in Data Science Degrees 2025