Master of Science in Professional Accounting and Analytics Curriculum
Seton Hall University’s M.S. in Professional and Accounting and Analytics provides accountants with cutting-edge knowledge and skills in data analytics, business intelligence, database management and enterprise systems. While geared towards recent graduates with a bachelor’s degree in accounting, more experienced accounting professionals will also benefit from the high-technology orientation of this STEM-intensive program. This 30-credit graduate-level program will complement a bachelor’s degree in accounting to meet the 150-hour educational requirement for the CPA credential.
- Required accounting/tax courses: 9 credit hours
- Required data analytics courses: 12 credit hours
- Required business courses: 9 credit hours (including 6 credits of electives)
I. Required accounting/Tax Courses (9 credits)
A. Research-Based Course - Take one of the following two courses:
BTAX 6003 Tax Research (3)
B. Accounting Electives*
BACC 7136 Big Data and Business Impact (3)
II. Required Data Analytics Courses (12 credits)
A. Data Analytics Required Course:
BSAN 7001 Introduction to Data Analytics/Business Intelligence (3)
B. Data Analytics Electives*
BSAN 7031 Databases and SQL (3)
III. Required Business Courses (9 credits)
A. Business Required Course:
BLAW 7314 Commercial Law (3)
B. Graduate Business Electives (6 credits)
Includes any three-credit non-accounting graduate business elective where the student satisfies the course prerequisite, three-credit graduate internship courses (BACC7190 and BACC7191) if the student has a full-time professional accounting work experience, and any additional accounting/tax elective once additional electives are added to the online program.
- A capstone accounting course designed to see how students handle somewhat ambiguous accounting problems. The course is largely a case-study course with students expected to do significant accounting research with many written reports. Prerequisite: BACC 7123 or the equivalent.
- Study of successful methodology of research in federal taxation applied to the solution of both routine and complex tax problems. Topics include research sources, materials and tools, including court reporters, government documents, IRS rulings, professional periodicals, tax services and citators, and computerized tax research.
- This course will provide participants with a clear understanding of enterprise applications like accounting, materials management, sales and distribution, materials requirement planning and process manufacturing. Each of these applications will be covered through the use of the SAP enterprise systems. In addition, the course will cover security, auditing, evaluation and implementation as applied to information systems. Prerequisites: BITM 7724 or equivalent.
- This course explores the exponential growth in complex data and information created by business and society. Big Data has become so valuable that the World Economic Forum deemed it a new class of economic asset, like oil. Students will study various applications and analytical tools used to derive insight from big data, and how experts in accounting, finance, and operations utilize big data applications to manage reporting, risk management, and compliance. Students learn how different industries leverage the data to impact the bottom line and create competitive advantage.
Required Data Analytics Courses
- Business decision-making should, when possible, rely on data and the conclusions that can be drawn from that data. This course is an introduction to business data analytics; it covers descriptive statistics, data visualization, probability basics, and relationships between two or more variables. One focus is on learning and contrasting traditional statistical approaches (inference) and “big data” approaches. Much of the course will entail the use of Excel, as spreadsheet software is arguably the most commonly available and most frequently used tool for analyzing business data. We will equally be using R – a popular, open-source, statistical package.
Data Analytic Electives
- In the initial stages of a data analysis project, analysts must often deal with large and unfamiliar data sets. By asking good questions and finding answers in the data, they arrive at useful insights – and this captures the core of exploratory data analysis (EDA). EDA often serves as a precursor to the process of building predictive models. Equally often, EDA yields significant insights that prove to be very useful in themselves. This course covers the art and science of EDA. Through numerous examples, the course will develop participants’ ability to formulate interesting and important questions. Answering these questions generally involves significant slicing, dicing, aggregating and reshaping of the data; this course will equip participants with the requisite skills. EDA relies heavily on data visualization and the course will equip participants with the skills to generate, and effectively present, evocative graphs that tell stories. The course will equip participants with a framework to enable them to ask the right questions and with the skills to explore and find answers.
- In most business situations, being able to determine, with reasonable accuracy, the value of some unknown can be beneficial. For example, it would be useful for a company to know if a prospective customer would default on payments (classification), or to know the number of units of a product that it might be able to sell during the next quarter at a given store (regression). Quite often, even seemingly inaccurate estimates of such unknowns can lead to large monetary gains for a company if the new knowledge can lead to a discernable difference in performance. This is the domain of predictive modeling – using historical data to determine the value of an unknown. The course covers both classification and regression techniques. The course will equip participants with the ability to identify situations that could benefit from predictive models, to identify the data requirements and work with others to obtain the data, to manipulate the data into a form usable for predictive analysis, and to build, evaluate, present and deploy the models.
- Relational database technology revealed the power of a simple data model coupled with the nonprocedural Structured Query Language (SQL) that enabled data independence and unleashed the power of computing applications. Despite the growing importance of other data models, like schema-free and distributed-data models, the relational data model still reigns supreme in many application domains. The overwhelming majority of business data is still stored in relational databases, and any business analyst needs to understand how to extract data from them. This course provides thorough coverage of SQL. The course also covers data warehousing concepts, as analysts will need to design data warehouses for end users to perform their data analysis. Another important topic in the course is database design. While business analysts might not design databases for mission critical processes, they might be called upon to design departmental databases. This will require an understanding of database design diagrams. With this in mind, the course also covers the use of Entity Relationship Models for database design.
- This course will acquaint the advanced student of business and potential candidates who will sit for the CPA exam with certain advanced concepts in the study of law, mainly involving the Uniform Commercial Code and other aspects of commercial law. The course includes a thorough review of contract law; sales (Article 2); buyer's and seller's remedies under the Common Law and the Uniform Commercial Code; bailments (leases of commercial/personal property); a review of business organizations (liability and taxation); agency and employment issues (independent contractor); an introduction to securities law; bankruptcy; and security interests.