Master of Science in Accounting and Analytics

This course introduces concepts and applications of data analytics to solve problems in accounting. It focuses on applying data science techniques and providing hands-on experience to develop the skills to use these tools and think critically. Topics covered include data preparation and cleaning, data visualization, audit data analytics, managerial analytics, tax analytics, and financial statement analytics.

Research skills are important for success in accounting, regardless of specific career path. This course focuses primarily on applied research done in practice, often in conjunction with the preparation or review of financial statements or tax returns. Applied accounting research typically involves qualitative skills, but the course also introduces academic accounting research. Students will acquire an understanding of different research skills in applying those skills to conduct research projects in accounting, auditing, and taxation. The course emphasizes communication and problem-solving while also extending the students’ technical accounting knowledge and ability to apply accounting rules and regulations.

This course introduces students to the principles of retirement and estate planning as well as current issues in these areas. The course is designed to enable students to understand and be conversant with the basic language of retirement and estate planning, and to understand the pertinent provisions of the US Internal Revenue Code related to these topics. The course focuses on training an individuals ability to use this information for making both short-term and long-term planning decisions. The course progresses at a rapid pace and requires students to prepare regularly for each class session instead of waiting until the exams. Topics include retirement planning tools, techniques and plans, estate and gift tax calculation and compliance, estate planning tools and techniques (both pre and post death), probate and non-testamentary disposition of assets, the use and purpose of trusts, family gifting strategies, estate liquidity, business succession planning, Social Security, Medicare and Medicaid and retirement plan distributions.

This course introduces the principles and concepts related to forensic accounting, including professional ethics and responsibilities issues related to the field. It covers civil and criminal procedures including evidence and discovery. The course introduces litigation services for forensic accountants and engagement and practice management. It also concludes with a survey of specific forensic accounting topics including fraud, bankruptcy, digital forensics, matrimonial disputes, financial statement misrepresentation, damages and valuation.

This course deals with (1) interpretation of financial statements, (2) evaluation of the alignment between business strategies and financial performance, (3) identification of potential business risks, and (4) comparison of performance of different companies. The course introduces business analysis and valuation techniques and utilizes real world data to help students comprehend financial statement analysis tools. Topics covers financial statement information, tools of financial statement analysis, and forecasting and valuation techniques.

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 will focus on data mining and knowledge discovery algorithms and their applications in solving business and operation problems. We concentrate on demonstrating how discovering the hidden knowledge in corporate databases helps managers make near-real time decisions. Methodological and practical aspects of knowledge discovery algorithms will be covered, including data preprocessing, k-nearest neighborhood algorithms, machine learning and decision trees, artificial neural networks, clustering, and algorithm evaluation techniques. Practical examples and case studies will be present throughout.

This course introduces basic theory and methods underlying multivariate analysis. Students will study techniques used for regression, classification, dimension reduction, and clustering. They will build expertise in applying these techniques to real data through class exercises and a project, and learn how to present results. This proficiency will enable students to become sophisticated data analysts, and to help make more informed design, marketing, and business decisions. Python will be the default programming language used for the course.

This course prepares students to employ essential ideas and reasoning of applied statistics. The course provides students with a solid foundation for solving empirical problems with the ability to summarize observed uni- and multivariate data, and to calibrate statistical models. While financial applications are emphasized, the course may also serve areas of science and engineering where statistical concepts are needed. The course will familiarize students with the use of R for statistical data analysis.

BIA 500 Business Analytics: Data, Models & Decisions (3)

*If not taken as core

Many managerial decisions - regardless of their functional orientation - are increasingly based on analysis using quantitative models from the discipline of management science. Management science tools, techniques and concepts (e.g., data, models, and software programs) have dramatically changed the way businesses operate in manufacturing, service operations, marketing, transportation, and finance. Business Analytics 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.

BIA 610 Applied Analytics (3)

Applied Analytics is a capstone course for the analytic-focused MBA program. It is intended to integrate all previously taken coursed in the program by presenting a set of increasingly complex business problems. These problems can be solved through analytic skill taught in this and previous courses. In particular, the course is intended to reinforce the understanding of analysis as 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.

FA 550 Data Visualization Applications (3)

Effective visualization of complex data allows for useful insights, more effective communication, and making decisions. This course investigates methods for visualizing financial datasets from a variety of perspectives in order to best identify the right tool for a given task. Students will use a number of tools to refine their data and create visualizations, including: R and associated visualization libraries, Ruby on Rails visualization tools, ManyEyes, HTML5 & CSS 3, D3.js and related javascript libraries, Google Chart Tools, Google Refine, and image-editing programs.

MIS 631 Data Management (2)

This 2-credit course focuses on data and database management, with an emphasis on modeling and design, and their application to business decision making. The course provides a conceptual understanding of both organizational and technical issues associated with data. The central theme concerns data modeling and databases. We examine organizational approaches to managing and integrating data. Among the topics included are normalization, entity-relationship modeling, relational database design, SQL, and data definition language (DDL). Discussed are specific applications such as strategic data management, master data management, and physical database design. The course concludes with a brief overview of Decision Support Systems, data warehousing and business intelligence, NoSQL databases (e.g., MongoDB) and cloud computing. The course includes a number of cases studies and modeling and design projects. Students in MIS 631 must also enroll in the associated 1-credit lab course MIS 632 Managing Data Lab.

MIS 632 Data Management Lab (1)

This 1-credit lab course provides an experiential learning component for MIS 631 Data Management for which it is a co-requisite. MIS 632 provides hands-on experience in designing, implementing, and querying data bases. The relevant software is introduced using demonstrations, in-class exercises and homework exercises that are closely tied to and executed in synch with the conceptual and theoretical material covered in MIS 631. Specifically, students will gain hands-on experience in: (i) ERWIN - a widely used commercial tool for representing conceptual (e.g., E-R diagrams) and logical data models (e.g., relational DBMS), (ii) PostgreSQL (relational database software), (iii) SQL Structured Query Language) and (iv) MongoDB a NoSQL document data store. Students in MIS 632 must also be enrolled in the associated 2-credit lecture course MIS 631 Managing Data course.

MIS 633 Business Intelligence and Data Integration (2)

This 2-credit 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, architecture, data design, implementation, business intelligence, deployment, data integration and emerging issues. Practical examples and case studies are presented throughout the course. Students in MIS 633 must also enroll in the associated 1-credit lab course MIS 634 Business Intelligence & Data Integration Lab.

MIS 634 Business Intelligence and Data Integration Lab (1)

This 1-credit lab course provides an experiential learning component for MIS 633 ML Engineering 2 for which it is a co-requisite. MIS 634 provides hands-on experience in designing, implementing, and querying data warehouses and large-scale database systems. The relevant software is introduced using demonstrations, in-class exercises and homework exercises that are closely tied to and executed in synch with the conceptual and theoretical material covered in MIS 633. Specifically, students will gain hands-on experience in using: (i) Alteryx - a widely used commercial tool for the Extract-Transform- Load (ETL) function, (ii) ERWIN - a widely used commercial tool for representing conceptual (e.g., E-R diagrams) and logical data models (e.g., relational DBMS) and (iii) a NoSQL database (e.g., MongoDB). Students in MIS 634 must also be enrolled in the associated 2-credit lecture course MIS 633 Business Intelligence & Data Integration course.

FIN 560 Federal Taxation of Individuals for Financial Planning (3)

This course deals with the methods and principles of US Federal income taxation. It is concerned with the history and politics behind the federal income tax laws and regulations, including major emphasis on tax provisions common to all types of taxpayers, particularly individuals. Topics include: tax authority, research, compliance and planning; gross income and exclusions; individual deductions and credits; tax rate schedules and calculation; filing status; investments and property transactions; self-employment income; retirement planning; home ownership and professional ethics.

MGT 700 Econometrics (3)

An introduction to the science of designing statistical models of economic processes. Students will be required to build and estimate a number of models during the term. Topics include: regression theory, statistical difficulties in regression analysis, advanced topics in single-equation regression, models of qualitative choice (such as, probit, logit), and simultaneous equation estimation.

FIN 565 Financial Plan Development (3)

This course integrates the different aspects of the financial planning process and demonstrates how to apply this knowledge to the development of a comprehensive financial plan. Students learn how to solve the main problems related to the financial planning process: cash management, debt management, taxation, insurance, retirement, investment, portfolio optimization, and estate planning. At the end of the course, students should be able to construct a plan according to the CFP Boards Financial Planning Practice Standards and client objectives. The course is appropriate for students who want to become financial planners and especially for those that plan to take the CFP Certification Examination.

FIN 550 Financial Planning and Risk Management (3)

This course will review the fundamental principles of financial planning, professional conduct, education planning, risk management and regulation. The course is aligned with the principle knowledge topics evaluated on the CFP Certification Examination. The course introduces you to the financial planning process and teaches you how to work with clients to set goals and assess risk tolerance. Learn how to process and analyze information, construct personal financial statements, develop debt management plans, recommend financing strategies, and understand the basic components of a written comprehensive financial plan. The course also covers the regulatory environment, time value of money, and economic concepts.

FA 631 Investment, Portfolio Construction and Trading Analytics (3)

The significant amount of information available in any field requires a systematic and analytical approach to select the most important information and anticipate major events. Machine learning algorithms facilitate this process understanding, modeling and forecasting the behavior of major social or economic systems and their variables.

This is an applied research course that explores how to apply fundamental machine learning models to predict financial time series and solve financial problems. Some of the financial applications explored are algorithmic trading, model calibration, portfolio optimization, and risk management.

FIN 658 Wealth Management Principles and Practices (3)

This is a course on the theory and practice of wealth management. It covers the building blocks and fundamental theoretical and practical aspects of investment management and financial planning for individual investors as well as applications that put the former to use by practitioners in the industry. Students will be exposed to some of the information, tools, and analysis available to investment management professionals today.

MIS 631 Data Management (2)

This 2-credit course focuses on data and database management, with an emphasis on modeling and design, and their application to business decision making. The course provides a conceptual understanding of both organizational and technical issues associated with data. The central theme concerns data modeling and databases. We examine organizational approaches to managing and integrating data. Among the topics included are normalization, entity-relationship modeling, relational database design, SQL, and data definition language (DDL). Discussed are specific applications such as strategic data management, master data management, and physical database design. The course concludes with a brief overview of Decision Support Systems, data warehousing and business intelligence, NoSQL databases (e.g., MongoDB) and cloud computing. The course includes a number of cases studies and modeling and design projects. Students in MIS 631 must also enroll in the associated 1-credit lab course MIS 632 Managing Data Lab.

MIS 632 Data Management Lab (1)

This 1-credit lab course provides an experiential learning component for MIS 631 Data Management for which it is a co-requisite. MIS 632 provides hands-on experience in designing, implementing, and querying data bases. The relevant software is introduced using demonstrations, in-class exercises and homework exercises that are closely tied to and executed in synch with the conceptual and theoretical material covered in MIS 631. Specifically, students will gain hands-on experience in: (i) ERWIN - a widely used commercial tool for representing conceptual (e.g., E-R diagrams) and logical data models (e.g., relational DBMS), (ii) PostgreSQL (relational database software), (iii) SQL Structured Query Language) and (iv) MongoDB a NoSQL document data store. Students in MIS 632 must also be enrolled in the associated 2-credit lecture course MIS 631 Managing Data course.

BIA 568 Management of AI Technologies (3)

Artificial Intelligence (AI) is an interdisciplinary field that draws on insights from computer science, engineering, mathematics, statistics, linguistics, psychology, and neuroscience to design agents that can perceive the environment and act upon it. 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 face recognition. In each application area, the course focuses on issues related to management of AI projects, including fairness, accountability, transparency, ethics, and the law.

FE 512 Database Engineering (3)

The course will introduce the Hanlon Financial Systems Lab database resources and the tools required to use them. More generally, the course will introduce a variety of software tools used to interact with data storage systems. The focus will be on how to use these tools in practice to perform common data engineering tasks, with a focus on the financial services industry, in both on-premises and cloud environments. The course will require students to spend a significant amount of time programming in Python, SQL and other languages.

MIS 637 Data Analytics and Machine Learning (3)

If not taken as part of core

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.

MIS 645 Cyber Security Principles (3)

This comprehensive course will cover the key security concepts for managers. In the first phase, security fundamentals will be covered with emphasis on levels of security (network, system software, middleware, applications, business process), authentication, authorization, access, and integrity. In the second phase, the key security technologies such as cryptographic algorithms (symmetric and asymmetric encryption), PKI, digital certificates, and corporate security will be discussed. The last phase of this course will discuss the management issues of security policies and security administration and describe how various security technologies and approaches can be applied to cyber security. Topics will include an overview of Internet security, Web security, Web application security, wireless and mobile Web security, and other emerging cyber information issues. Students will conduct security audit of Web sites and Web-based corporate applications.

MIS 716 Blockchain Fundamentals and Applications (3)

The course introduces students to Blockchain technology. Blockchain technology has the potential to revolutionize the way transactions are created, recorded and protected. As a distributed ledger, based on cryptography, Blockchain is incorruptible and enforces transparency, and is highly secure. This is a “hands” on system design course which introduces the students to key concepts of Blockchain using examples and case studies. Using Blockchain Fabric and Composer, students will implement their design in a simulated environment.

MIS 720 Managing Enterprise Network Security Architectures (3)

This course explores the design of secure network architectures to meet business requirements. Business reliance on internal and carrier network systems and services is extremely high, and even short-term service disruptions can have catastrophic effects on business capabilities. Students will study the network technologies and services relied upon most by organizations today as they build robust network services. This includes exploration of TCP/IP networks, carrier network capabilities and limitations, as well as cloud computing and other virtual services. Technology concepts are applied to the enterprise context, as students examine opportunities and challenges for business, technology leaders.

MIS 760 Information Technology Strategy (3)

The objective of this course is to address the important question, “How to improve the alignment of business and information technology strategies?” The course is designed for advanced graduate students. It provides the student with the most current approaches to deriving business and information technology strategies, while ensuring harmony among the organizations. Topics include business strategy, business infrastructure, IT strategy, IT infrastructure, strategic alignment, methods/metrics for building strategies and achieving alignment.

MGT 609 Project Management Fundamentals (3)

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.

MGT 671 Technology & Innovation Management (3)

This course introduces the student to topics in the management of technology and examines the critical role of technology as a strategic resource to enable management to achieve organizational objectives. Topics include entrepreneurship, developing and managing new ventures, managing innovation, the technology life-cycle and technology forecasting, management of research and development (R&D) personnel and projects, evaluation of R&D projects ,and integrating technology strategy with the organization’s overall business strategy.

MGT 699 Strategic Management (3)

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.

Hanlon Financial Lab Bundle

Students can also take 3 one-credit Hanlon Financial Lab courses. These include:

• FE 511

• FE 513

• FE 514

• FE 515

• FE 516

• FE 517

• FE 518

• FE 519

• FE 520

• FE 524