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 (if not taken in Core)

• BIA 610 Applied Analytics

• FA 550 Data Visualization Applications

• MIS 631 Data Management

• MIS 632 Data Management Lab

• MIS 633 Business Intelligence and Data Integration

• MIS 634 Business Intelligence and Data Integration Lab

• FIN 560 Federal Taxation of Individuals

• MGT 700 Financial Econometrics

• FIN 565 Financial Plan Development

• FIN 550 Financial Planning and Risk Management

• FA 631 Investment, Portfolio Construction and Trading Analytics

• FIN 658 Wealth Management Principles and Practices

• MIS 631 Data Management

• MIS 632 Data Management Lab

• BIA 668 Management of AI Technologies

• FE 512 Big Data Technologies

• MIS 637 Data Analytics and Machine Learning (if not taken as part of Core)

• MIS 645 Cyber Security Principles

• MIS 716 Blockchain Fundamentals and Applications

• MIS 720 Managing Enterprise Network Security Architectures

• MIS 760 Information Technology Strategy

• MGT 609 Project Management

• MGT 671 Technology & Innovation Management

• MGT 699 Strategic Management

• Hanlon Financial Lab Courses