Masters in Accounting Analytics
Learn to not only analyze data, but to translate and communicate it throughout your entire organization.
- Courses from five departments (Accountancy, Computer Information Systems, Finance, Information Process Management, Mathematics) sharing a common core with other MS in Analytics programs.
- Choose from three specialized tracks: accounting analytics, database analytics and forensic analytics.
- Straightforward, 10-course degree that can be completed in 12 months.
- AACSB accredited, and the first graduate program of its kind.
- STEM designation, increasing employability of international students due to three years of OPT (optional practical training).
The Bentley MSA prepares you for a wide range of careers, no matter the industry you are interested in. See below for a partial list of employers and job titles our alumni have achieved over the past year.
|Company Name||Alumni Title|
|Deloitte||Audit Associate, Advisory Consultant, Tax Associate|
|EY||Assurance Associate, Diversified Staff Group - Tax Services, Transfer Pricing Associate|
|PwC||Assurance Associate, Tax Associate, Advisory Associate, Technology Consulting|
|KPMG||Audit Associate, Deal Advisory Associate, Advisory Associate|
|Grant Thornton||Audit Associate, Tax Associate|
|RSM||Assurance Associate, Tax Associate|
|BDO||Assurance Associate, Tax Associate, IS Assurance Associate|
|State Street||Emerging Technology Consultant, Tax Analyst|
|Thermo Fisher Scientific||Associate Internal Auditor|
|Analog Devices||Cost Accountant|
The Howard A. Winer Learning Lab for Economics, Accounting, and Finance (LEAF) is a welcoming and inclusive learning environment where students are encouraged to seek academic support for their accounting, economics and finance courses. Students utilizing the LEAF will find peer tutors knowledgeable in accounting, economics, and finance, ready to help and prepare students to thrive in the Bentley business curriculum.
The LEAF is designed to allow students hands-on experience by integrating technology into their areas of study and work together on group projects and case studies. Additionally, students will find peer led tutoring for the subject areas covered by the lab.
Average age of students in this program
Domestic Placement Rate
Part-Time Students Enrolled
International Students Enrolled
QS Top MBA Discusses the Launch of the Bentley MS Accounting Analytics Program
Top Employers Hiring Our Graduates
Pre Program Requirements
All applicants will be evaluated for foundation waivers.
This course covers basic statistical techniques in a managerial setting, and features case studies and conceptual exercises. Statistical topics include effective use of numerical and graphical summaries, estimation and confidence intervals, hypothesis testing and regression. More advanced topics such as data mining, the Bayesian paradigm and principles of model building, may be encountered during projects.
First in a two-course sequence of financial accounting courses at the professional level. Examines the principles and practices of external financial reporting, with particular emphasis on balance sheet valuations and their relationship to income determination. Reviews basic accounting concepts and the essentials of the accounting process. Covers the application of present value techniques to accounting valuations. Studies in depth the measurement and disclosure problems associated with cash, receivables, inventories, fixed assets and intangibles. Alternative accounting procedures and their impact on financial statements are also examined.
This course continues the two-course sequence begun in AC 611 by exploring accounting theory and concepts which form background for external financial reporting. It examines the Generally Accepted Accounting Principles (GAAP) related to the preparation of financial statements, with particular emphasis on the equity side of the balance sheet. Topics covered include current liabilities, long-term debt, leases, pensions, stockholders’ equity, earnings per share, accounting for income taxes, accounting changes, and the statement of cash flows.
This course examines typical organizational business processes and the information technology that enables those processes. Reviews qualities of information, including those established by authoritative bodies, to assess the ability of information systems to support the business processes and an organization's management. Focuses on financial and accounting information systems (AIS) and explores several typical AIS application areas. Issues addressed include the effect of emerging technologies on business processes and their related information systems; control issues pertaining to these systems; and the implications of technology-enabled organizational changes on systems design, implementation and management. Students will be introduced to state-of-the-art tools and techniques for examining business processes and information systems and will engage in a project at a company site.
This course is designed to provide a foundation in financial statement auditing. Class sessions cover the economic and social justifications for auditing; the connections between enterprise strategy, business processes, business risks, financial measures, and the audit; the role of internal control in auditing; the technical details of audit planning, testing and reporting; and the social responsibility of the auditor. Investors, analysts and the public face a significant problem in assessing the quality of the financial information that an enterprise reports as it goes about its activities. Arguably, these parties can make better decisions if they can trust the executives and management of the enterprise and if they are reasonably sure that the information they encounter is of high quality. One way to gain both that trust and that assurance is by examining the quality of the information through the process of financial statement auditing.
This course approaches effective communication both as an essential professional skill and as an important function of management. It discusses the elements of communication (argumentation, structure, style, tone and visual appeal) and presents techniques for increasing one's effectiveness in each area. Students read, discuss and write about cases based on tasks that managers commonly face, such as explaining changes in policy, writing performance evaluations, analyzing survey results or other numerical data, and communicating with employees, shareholders, the press and the public. Methods include group work, oral presentations, several writing assignments and role playing. Drafting and revising and computerized word processing are stressed.
This course is designed to examine the practice of information technology (IT) auditing, including professional standards, application of IT and internal control frameworks, and recognition of current and emerging technology-related risks. Class sessions cover topics such as IT general controls, systems development and implementation, the auditor’s role related to information security, and data extraction and transformation activities. Through readings, case studies, exercises, and discussion, students will learn to plan, conduct, and report on IT audit activities. Additional topics may include introduction to advanced audit software, business continuity planning, and the role of the IT auditor as a management adviser. Provides a foundation for the Certified Information Systems Auditor exam.
This course is designed to provide a culminating experience combining accounting, technology and analytical methods. Students will apply skills acquired in prior courses to large, complex financial data sets resulting in a significant semester-long project. Student teams will address real world accounting. audit and/or tax problems using sophisticated analysis tools to collect, clean and analyze large sets of data, and present project results using appropriate visualization tools and reports.
This course teaches foundational data management,retrieval, and manipulation skills with an emphasis on enabling the students to form a strong foundation for analytical processes. It builds a foundation for understanding various domains of practice with conceptual data modeling and demonstrates how the same conceptual needs can be served with different data management technologies. The course covers relational technologies for both operational databases and data warehouses and non-relational data management infrastructures for analytics. The course will help the students develop strong skills in the use of the SQL language for database definition and data manipulation.
Financial Modeling is focused on applying sophisticated Excel techniques to the most common modeling problems in finance. The skill set is expanded to include advanced features of Excel including TVM and statistical functions, array manipulation, text and date usage, regression, conditionals, Boolean operators, data tables and random number generation. Subsequently the course will cover macro recording as well as custom subroutine and function construction in the Visual Basic for Applications (VBA) development environment. All techniques learned will be applied to the most common financial modeling problems of the day including financial statement forecasting, present value, cost of capital, and valuation.
The course will provide a business-focused perspective on analytics in organizations, with emphasis on business fundamentals for analytics professionals (including how businesses are structured, functional areas, core business processes and associated performance metrics, and types of business decisions), the value of analytics in organizations (including organizational-level perspectives on value, managing with analytics, and constraints and consequences of analytics processes such as information security, privacy and ethics), and the practice of analytics (understanding and framing ill-defined business problems in various functional business areas, exploring and visualizing problem-related data, identifying actionable insights, and communicating the results at different organizational levels). The course will feature hands-on exercises with real-world data and analytics applications.
Working with and finding value in data has become essential to many enterprises, and individuals with the skills to do so are in great demand in industry. The required skill set includes the technical programming skills to access, process and analyze a large variety of data sets, including very large (big data) data sets, and the ability to interpret and communicate these results to others. Anyone with these abilities will provide benefit to their organization regardless of their position. This course presents the essentials of this skill set.
Provides students with an in depth coverage of simple and multiple linear regression methods and, as time permits, an introduction to the analysis of time series data. Simple and multiple linear regression techniques are covered including the use of transformations such as squares and logarithms, the modeling of interactions, and how to handle problems resulting from heteroscedasticy and multicollinearity . Issues surrounding outlying and influential observations are also covered. The art and science of model building are demonstrated with the help of cases. Autocorrelation is then considered, and an introduction to the ARIMA modeling of times series is provided. Makes use of statistical packages such as SAS, JMP, R or SPSS.
This course focuses on statistical modeling situations dependent on multiple variables, as commonly found in many business applications. Typical topics covered are logistic regression, cluster analysis, factor analysis, decision trees, and other multivariate topics as time permits. Applications of these methodologies range from market analytics (e.g., direct mail response and customer segmentation) to finance and health informatics. A central objective of the course is for participants to be able to determine the appropriate multivariate methodology based on the research objectives and available data, carry out the analysis and interpret the results. This course makes use of statistical packages such as SAS, JMP, R or SPSS, along with more specialized software.
Select two courses from the following electives.
Affords students the opportunity to enhance self-realization and direction by integrating prior classroom study with experience in professional employment. Each student is required to prepare a research paper addressing a contemporary accounting issue and a paper on the work experience, under the supervision of a faculty adviser.
This course, designed for students who will be accountants and information systems professionals, shows how they can help management use information technology to effectively control the execution of business activities, while capturing accurate and complete data about those activities in real time. Students will model, analyze and evaluate accounting information systems that support intra- and inter-organizational business processes as well as management control and decision-making. Students will learn to determine and document user requirements, communicate results, and support decision-making. By analyzing and discussing case studies, students will develop the ability to identify key issues, wrestle with conflicting information, and formulate appropriate and feasible recommendations. The course incorporates large-scale projects to enrich the student's experience with an appreciation for the accounting challenges and opportunities posed by information technology.
The course exposes students to the environment of financial fraud, with a focus on asset misappropriation and fraud perpetrated against the organization. It explores the prevailing theories of criminal behavior related to white collar crime, as well as the basics of the regulatory, criminal justice and civil justice systems, relevant federal and state statutes and regulations, and common law related to fraud. The course covers fraud prevention, and detection and investigation tools related to asset misappropriation. It also introduces the digital environment of fraud, including identity theft, cyber crimes and Internet forensics.
This course focuses on complex frauds (including financial statement fraud, tax fraud and money laundering), and on non-fraud forensic accounting engagements (including cases of patent infringement, commercial damages and anti-trust). It covers related investigation methods and legal issues, valuation models, reporting and communicating findings, testifying as an expert witness, and other litigation advisory services.
This course introduces graduate students to professional accounting research. It focuses on how research can help address measurement, uniformity and disclosure issues that regularly arise in business. It reviews and critiques research works and their implications for the practice of accounting. Investigates ethical perspectives and emerging professional issues. The course evaluates policy formulation of professional accounting standards and their impact on business reporting. Students research, analyze, develop and present proposed solutions to accounting and related business cases found in practice using modern information technology resources.
Python is an easy to learn, widely versatile programming language whose extensive collection of external libraries makes it a popular choice for business analytics and visualization, data science, artificial intelligence, scientific and numeric computing, and many other applications. Its compatibility with leading analytics tools that are widely used in enterprises also places it in high demand. Students in this course will first learn the fundamentals of programming that are common to all programming languages. They will then work with Python libraries to perform common analytics tasks. No prior programming experience is required.
This course teaches programming using the Java language, which is widely used in business. By focusing on algorithm development, data structures, logical reasoning skills, and sound programming practices, students learn to analyze and tackle business programs with software solutions. Emphasis is placed on the importance of writing and thoroughly testing code that is well structured and runs efficiently. Students first gain a solid understanding of programming fundamentals before delving into higher-order concepts, including abstract data types. Practical hands-on exercises and assignments using a well-known, integrated development environment reinforce algorithmic thinking, programming, and debugging skills. No prior programming experience is required.
The architecture of modern database systems for data analytics with big data are examined. This course provides a hands-on introduction to several architectures and approaches for data for analytics, including data from operational transactions, sensor data, web logs and social media sites. It explores the different types of data that make up the big data space, and applies capture and storage technologies appropriate for relational and non-relational models, such as clickstreams and user navigation of web sites. Data will be explored using Python-based tools for analytics and visualization. Students enrolled in this course are expected to have basic proficiency in the Python programming language and relational databases.
This course introduces students to the foundations of artificial intelligence (AI) and its use in automation. Fundamental concepts and techniques behind software agents, automated reasoning, machine learning and robotics are introduced and illustrated with applications in various domains. Students will learn how these techniques can be integrated into business operations and functions to increase productivity and to support strategic decision making in organizations. Students will have opportunities to explore AI-based software and tools and discuss the ethical issues related to the development and use of AI.
This course extends students' knowledge and skills gained in database management courses and looks further at business intelligence and data science concepts and techniques. The course explores the data management and analytics architecture and technologies required for solving complex problems facing modern enterprises and organizations. Case studies of organizations using these technologies to support business intelligence gathering and decision making are examined. This course also provides hands-on experience with state-of-the-art data warehousing, analysis, mining, and visualization methods and tools.
This course presents an overview of information security issues that must be addressed by organizations in today's ubiquitously networked environments. Specific coverage will include information security risks and related protection of data, networks and application software. While the primary focus is on how to protect organizational information assets, other topics will include strategic uses of security in business, the impact of security risk on various industries, as well as the security and privacy rights and responsibilities of end users and home computer operators. The course is designed to help students think critically about the local, national and global information security issues in our highly networked society.
An enterprise system forms the backbone of a company. Business information is collected, shared and reported using an enterprise system, which needs to be tailored to support a company's business processes. In this course, students gain hands-on experience planning for and configuring enterprise systems, using the world's leading enterprise software product from SAP. Students will experience the Request for Proposal process, translate business process needs into module-based design requirements, and design test plans for the processes they configure. They will gain a deep understanding of how business processes are instituted in a company setting, and how carefully configured software can lead to efficiency and effectiveness gains and support competitive strategy. This course prepares students to participate in enterprise system implementation and evaluation processes as a consultant, business systems analyst, subject matter expert or auditor.
This course provides analytics students an introduction to machine learning field. Students will be introduced the mathematics and statistics ideas behind the foundation of the machine learning. Particularly, students will be involved in hand on experience to practice the machine learning methods through advanced tools, and work on real-world business questions to look for business solutions. Advanced analytics topics, such as resampling methods, support vector machines (SVM), Bayesian inference, Kernel methods, and simulations, deep learning will be covered in this class.
Find out More
Meet Your Program Director
Len Pepe is an accounting lecturer and the program director of the MS in Accountancy and Accounting Analytics at Bentley University. Len was a partner in the audit department of Grant Thornton, LLP Boston and has experience as a managing partner of the CCR LLP, Boston Office and as a partner at BDO Seidman, Boston. His research interest is in professional practice and has been honored with Bentley’s Adamian Award for Teaching Excellence — Part Time in 2017.
Contact Len to schedule a time to discuss your background and career goals and how these align with Bentley's MSA and MSAA programs.