How to Apply
Learn about the admissions process and requirements to apply.
Learn how to extract, manipulate, and analyze data to improve audit quality and identify key audit risk areas. Become an expert in audit analytics. As part of the Rutgers Stackable Business Innovation Program (rSBI), the Audit Analytics Concentration is stackable with the following master's programs: MACC in Financial Accounting, MBA in Professional Accounting, MBA.
Both the first and the second courses of this concentration emphasize the usage of statistics and the interpretation of results towards a modern audit. Audit Analytics course introduces the application of audit analytics to organizations as audit evidence while the Special Topics in Audit Analytics course covers some specialized audit analytic techniques such as visualization, neural networks, and text mining. In the Information Risk Management course, students will gain an in-depth understanding of the audit process and risk management. Students will develop the knowledge needed to understand how accounting information systems work, and how to evaluate such systems. The last course (Individual Project in Audit Analytics) enables students to apply what they have learned to develop a deeper understanding of the application of analytics in audit.
You can take the course listed below as individual classes or as stackable courses towards the completion of a concentration.
This is the first course of the Audit Analytics Certificate Program. There are two main purposes of this course: (1) introduce the basic application of analytics to both internal and external audit processes in current ubiquitous computer-based information systems, and (2) introduce the application of audit analytics to organizations. This course emphasizes the usage of statistics and the interpretation of results to be used as audit evidence. It is designed to impart the theory and practice of the foundational statistical techniques applied in an audit.
This is the second course of Audit Analytics, which serves the purpose of further improving students’ analytic skills and promoting changes in the profession towards a modern audit. The course consists two parts: methodology and practice. The first part of the course is intended to develop students’ understanding of statistical inference. Students will learn how to apply some basic statistical models to the auditing problems, how to interpret the results, and troubleshoot some common problems. The second part of the course covers some specialized audit analytic techniques such as visualization, neural networks, and text mining.
An introduction to the advanced concepts underlying information risk management. This course aims to build on the basic principles of auditing and information risk management. Students will gain an in-depth understanding of the audit process and risk management. Additionally, students will develop the knowledge needed to understand how accounting information systems work, and how to evaluate such systems. Students learn to assess the reliability of information that is both captured and disseminated by such systems, as well as the threats and risks unique to computer information security.
The capstone course in the Audit Analytics line of study. Students participate in an individual study project with an advisor. Students will apply what they have learned to develop a deeper understanding in the application of analytics in audit. At the end of the course the student will have conducted a novel research project that will have involved applying analytics in an audit related setting.
The concentration and courses are offered by the Accounting & Information Systems Department
Sample Relevant Careers: Controller, Certified Public Accountant (CPA), Compliance Auditor, Cost Accountant, Financial Accountant, Financial Auditor, Government Auditor, Industrial Accountant, Information Technology Auditor, Internal Auditor, Management Accountant, Data Analyst, Consultant, Financial Advisor, Credit Analyst