Business Analytics MS

Tuition

• This program consists of ten 3-credit courses, or 30 credits.
• Online tuition is $575 per credit.
• On campus tuition is:
  • $812.49 per credit for Massachusetts residents
  • $1,402.91 per credit for New England residents
  • $1,561.87 for out-of-state/international students
• Associated fees include a $950 College of Management fee, assessed in the fall and spring semesters.
• Total estimated tuition cost* for this program starts at:
  • $21,524.82 for students from Massachusetts
  • $32,152.38 for students from New England
  • $35,013.66 for out-of-state/international students
• Other fees may apply. Request Info to connect with a program representative for further details.

*Cost varies depending on amount of online and on-campus credits taken, and which concentration — Big Data or Supply Chain — a student is enrolled in.

Deadlines

• Application deadline is June 1 for fall admission
• Application deadline is October 1 (priority deadline) or December 1 (final deadline) for spring admission

Application Checklist

• Online application with application fee.
• Current professional résumé highlighting professional and academic contributions/qualifications.
• Official transcripts from all post-secondary institutions attended, both undergraduate and graduate. Applicants must have the equivalent of a United States bachelor’s degree to be eligible to apply. All transcripts are required, regardless of whether credits were transferred.
• Exam Scores Required:
  • Official GMAT or GRE scores. You can apply for exam waiver consideration.
  • International applications — Official TOEFL or IELTS scores. Learn more here.
• Two (2) letters of recommendation from professional or academic references.
  • Applicants must provide the name and email address for (2) references as part of the online application. Those references will receive an email notification directing them to a secure site to submit their recommendation. If references prefer to type a traditional letter, it should be sent as a PDF attachment referencing the applicant’s name, student ID number, and program of interest to GADocs@umb.edu.
• Essay: Use either the standard essay prompt in the online application or the one below.
  • Part 1 (up to 300 words): Why are you interested in attending graduate school at UMass Boston?
  • Part 2 (approx. 1,200 words): State a current issue, problem, or topic from your intended field of study (This can be specific to your country, state, or local community) and discuss your strategic plan as to how you would address the issue and how attending graduate school at UMass Boston will aid you in your pursuits.

International Students

• International graduate applicants must submit supplemental documentation.
• All non-US transcripts must be evaluated by Center for Educational Documentation (CED) located at www.cedevaluations.com. We require a course-by-course evaluation report. Applicants must submit the evaluation package as well as a separate copy of their official transcript.
• All applicants, regardless of current citizenship, who have completed their undergraduate studies outside of the United States must take the TOEFL or IELTS scores. Learn more here.
•  View UMass Boston's minimum required TOEFL or IELTS scores
• Applicants may be exempt from submitting these test scores if they have received at least four years of education (including their undergraduate program) at an English-speaking college or university in the United States. Applicants may be exempt from submitting these test scores if they have received at least four years of education (including their undergraduate program) at an English-speaking college or university.

We encourage applicants to submit their complete application, including supplemental materials, early to allow sufficient time for application processing. For additional information or specific questions about the online application, or submitting application material, please contact the College of Management Graduate Programs Office directly at graduate.admissions@umb.edu or by calling 617.287.6400.

Please mail all application materials to:

UMass Boston Graduate Admissions
100 Morrissey Boulevard
Boston, MA 02125

*NOTE: Materials should not be sent directly to the College of Management Graduate Programs Office. This will delay application processing.

Required Courses:

• Enterprise Business Intelligence (MSIS 670) (online)
  Explore business intelligence — what it can offer organizations and how it’s used in the real world — and develop an action plan to identify and act on business intelligence opportunities. Learn about business intelligence’s role in the effective management of an organization. Study the business, technical, and human components of business intelligence and see how real organizations applied these components to their organization. Finally, you’ll explore a framework and processes for identifying, evaluating, and acting on specific business intelligence opportunities. 
• Multivariate Statistics and Regression Analysis (MSIS 642) (on campus)
  In this course, you’ll develop statistical data analysis skills in business analytics applications. You’ll cover multivariate statistics, which analyzes problems in which multiple variables are simultaneously present, and various regression applications for business, such as simple linear regression, multiple regression, and logistic regression, etc. You’ll also learn how to solve various issues that you might face during those applications. This course will be the foundation for applied quantitative research for business analysts and business researchers. The course’s goal is to identify the signal or key features of the data. You’ll cover the major techniques in this field with the focus on practical issues such as selecting the appropriate approach and how to prepare the data.
• Enterprise Data Mining and Predictive Analytics (MSIS 672) (online)
  Learn the theory behind the analytical concepts of data mining and how data mining techniques are used in strategic business decision making. Get a hands-on understanding of the key methods of data visualization, exploration, association, classification, prediction, time series forecasting, clustering, induction techniques, neural networks, and others. You’ll work as part of a team to solve a business problem of your choice, using data mining tools and applying them to data. Explore concepts used for building data mining frameworks needed in analyzing useful patterns in databases through the application of practical methods.
• Management Decision Models (MBA MS 638) (on campus)
  Using the framework of data, models, and decisions, you’ll explore the systematic use of data and models in decision-making. You’ll acquire an appreciation of management science approaches to solving problems in business or government, public or private, and profit or not-for-profit sectors. You’ll discuss examples of problems from various sectors and from various functional areas. You’ll gather data about these problems, develop models, and explore solutions using computer-based analysis and managerial judgment. In addition, “what if” analyses are used to determine the sensitivity of model solutions to uncertainties in data inputs. The course is computer-based, using many of the advanced features on Excel and/or other software packages.
• Project and Change Management (MSIS 630) (on campus)
  Learn how to manage projects in the context of change. Study concepts and techniques in project management such as planning, scheduling, and implementation. Get an understanding of change management as relevant to project management in a dynamic organizational environment. Develop an understanding of the software tools employed for project management and apply the concepts and software to hypothetical and real-world cases.

Big Data Analytics Electives (Pick Five):

• Database Management (MSIS 618) (on campus)
  Learn the fundamental concepts necessary for the design, use, and implementation of database systems. Explore the fundamentals of database modeling and design, the languages and facilities provided by database management systems, and the techniques for implementing relational database systems. Upon completion of the course, you’ll be able to use Entity-Relationship Diagrams as a tool to assist in logical database design, be able to design logical databases in third normal form, be able to identify current issues in the uses of database management systems, be able to identify issues in physical database implementation, and gain familiarity with industrial-strength database management systems. This is a required elective, if you have not previously taken this course.
• Enterprise Data Warehousing for Business Intelligence (MSIS 671) (online)
  Gain a comprehensive overview of data warehousing, including planning, design, deployment, and ongoing maintenance issues. Develop a clear understanding a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and information delivery. Learn about data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architectures, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. You’ll get experience through hands-on exercises in commercial data warehousing modeling and implementation tools, and you’ll perform case analysis.
• Data Mining and Predictive Modeling (MSIS 680) (on campus)
  As organizations have become more and more readily able to collect massive quantities of data, they are increasingly recognizing data as one of their most valuable assets. Many organizations consider their ability to acquire data, utilize data mining, and build predictive models as key core competencies and many are realizing benefits from fact-based decision-making. For those ends, data mining is used to find patterns and relationships that lie within data and to build predictive models for fact-based decision-making. In this course, you’ll learn data mining algorithms in depth, including techniques for classification, association, and clustering. You’ll also learn techniques for mining text data, such as Latent Semantic Analysis and Latent Dirichlet Allocation. This course focuses on real-world applications to develop the understanding of appropriate approaches for gathering data and use data mining algorithms to build effective predictive models.
• Information Storage Management (MSIS 656) (online)
  Gain a comprehensive overview of network-based storage technology and information storage infrastructure. Learn about storage architectures, service features, benefits of Intelligent Storage Systems, and Storage Virtualization. Explore Networked storage technologies, including fiber channel (FC), based Storage Area Network (SAN), Network Attached Storage (NAS), and IP-SAN. You’ll also discuss advanced storage technologies on Content Addressed Storage (CAS), information security, and networked storage virtualization.
• Introduction to Big Data Analytics (MSIS 685) (on campus)
  In this course, you’ll learn a new and increasingly popular method of managing data using large scale data analysis. The advent of the internet, social media, and subsequently machine generated data has enabled social scientists to have access to massive datasets about the behavior of millions (or billions) of people or objects. However, collecting, storing, and analyzing this data isn't straightforward and requires specific skills. The goal of this course is to help you gain the skills required for this type of research while exposing you to tools and big data research streams. The course will help you understand both the challenges and the opportunities and assist you to appreciate Big Data applications.
• Business Programming (MSIS 615) (on campus)
  Learn essential programming skills in the current business and analytical world. Explore modern programming topics such as object-oriented programming (OOP), functional programming (FP), database integration, web APIs, and mobile/location-based system programming. Learn how to program to solve analytical business problems.
• MSIS 682 Linear Programming (MSIS 682)
  In this course, you’ll learn linear optimization techniques that are powerful and important tools in analytics area. Linear optimization can be used for mining and analytics of complex systems in the business world, which can greatly impact the decision-making process in this area. You’ll focus on linear programming techniques with an emphasis on their applications in solving real-world practical problems. You’ll explore effective formulation techniques, basic mathematical and algorithmic concepts, and software solution of large-scale problems arising in business analytics applications.
• Decision and Risk Analysis (MSIS 643) (on campus)
  This course combines elements of probability, economics, logic, psychology, and domain knowledge to characterize and analyze complex decision problems. You’ll gain familiarity with the basic theory and methods from classic and recent texts and examine some real-world applications from recent journal publications. You’ll focus on connections between the approaches covered and developments in information systems and in analytics.
• Business Analytics Project (MSIS 683)
  Demonstrate skills and integration of knowledge by working on a semester-long project, particular to your specialization. Particularly, you’ll synthesize and integrate knowledge acquired in coursework and other learning experiences and apply theory and principles in a situation that approximates some aspect of professional practice. It will be used as one means by which faculty judge whether you have mastered the body of business analytics and can demonstrate proficiency in the required competencies.

Supply Chain Analytics Electives (Pick Five):

• Health Information Analytics (MSIS 635) (online)
  Study ways to aid decision-making in health care by applying data-driven, computer-based tools to complex problems. Explore emerging information technologies for management support through data analysis and business intelligence systems. Learn the importance of proper formatting of data to obtain high quality results and identify the appropriate tools and techniques to implement business intelligence systems applied to the health care industry. This course is required, if not taken previously.
• Lean & Six Sigma Management (MBAMS 652) (on campus)
  In this course, you’ll study how to use new leading-edge methods, including High performance, Six Sigma, and Lean management, and IS management methods, for creating required competitive advantages. You’ll focus on how a company can create and deliver high value and quality to end-users, how it can design products and services so these will start their lives as growth products when launched, how the companies can manage its value chains so they become capable of operating with a productivity advantage at competitively low costs, and how the two — the value and the productivity advantages — drive sustained growth of the “bottom line”, a company's net income. You’ll study how, in this very competitive world, these new high-performance methods will achieve stellar ''bottom line'' business results in an accelerated time frame under dynamically changing circumstances.
• Operational Risk Management (MSIS 631) (on campus)
  Explore the role of ''operational risk management'' in different aspects of business. You’ll start with a session of definitions and preliminary discussions to show the big picture of the risk management discipline. Then you’ll study how different risks that an organization faces can be categorized according to their natures, probabilities, and impacts. You’ll learn how the probability of these risks can be reduced and how the impacts can be mitigated. Finally, you’ll focus on how an organization can recover faster and more efficiently from a realized risk. Throughout this course, the emphasis is to show the importance of managing the inevitable risks as sources of opportunity for organizations. 
• Management of the Supply Chain (MSIS 617) (on campus)
  Learn how to create opportunities for revolutionizing manufacturing and logistics, with increased efficiencies in designing, operating, and managing supply chains. Study state-of-the-art models and practical tools for supply chain management and multi-plant coordination. Explore effective logistics strategies for companies operating in several countries and on the integration of supply chain components and their associated information workflows into a coordinated system to increase service levels and to reduce costs. Use Internet and developments in information systems and communication technologies with real-world case studies that illustrate and analyze important concepts, such as strategic partnering and outsourcing.
• Data Mining and Predictive Modeling (MSIS 680) (on campus)
  As organizations have become more and more readily able to collect massive quantities of data, they are increasingly recognizing data as one of their most valuable assets. Many organizations consider their ability to acquire data, utilize data mining, and build predictive models as key core competencies and many are realizing benefits from fact-based decision-making. For those ends, data mining is used to find patterns and relationships that lie within data and to build predictive models for fact-based decision-making. In this course, you’ll learn data mining algorithms in depth, including techniques for classification, association, and clustering. You’ll also learn techniques for mining text data, such as Latent Semantic Analysis and Latent Dirichlet Allocation. This course focuses on real-world applications to develop an understanding of appropriate approaches for gathering data and use data mining algorithms to build effective predictive models.
• MSIS 682 Linear Programming (MSIS 682)
  In this course, you’ll learn linear optimization techniques that are powerful and important tools in analytics area. Linear optimization can be used for mining and analytics of complex systems in the business world, which can greatly impact the decision-making process in this area. You’ll focus on linear programming techniques with an emphasis on their applications in solving real-world practical problems. You’ll explore effective formulation techniques, basic mathematical and algorithmic concepts, and software solution of large-scale problems arising in business analytics applications.
• Decision and Risk Analysis (MSIS 643) (on campus)
  This course combines elements of probability, economics, logic, psychology, and domain knowledge to characterize and analyze complex decision problems. You’ll gain familiarity with the basic theory and methods from classic and recent texts and examine some real-world applications from recent journal publications. You’ll focus on connections between the approaches covered and developments in information systems and in analytics.
• Business Analytics Project (MSIS 683)
  Demonstrate skills and integration of knowledge by working on a semester-long project, particular to your specialization. Particularly, you’ll synthesize and integrate knowledge acquired in coursework and other learning experiences and apply theory and principles in a situation that approximates some aspect of professional practice. It will be used as one means by which faculty judge whether you have mastered the body of business analytics and can demonstrate proficiency in the required competencies.

At the end of this two-year program, you’ll be awarded a Master of Science in Business Analytics. The degree will demonstrate your expertise in the field on your résumé, as well as in interviews and workplace evaluations. With this degree, you’ll use cutting-edge skills in data collection, storage, and analysis to help businesses in any industry achieve their strategic goals. 

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