MACC KPMG data and analytics track

The W. P. Carey Master of Accountancy (MACC) is among a select group of accounting master’s programs that offers a data and analytics track in partnership with KPMG.

Delving into the technologies and methodologies used in today’s complex, data-centric audits, the KPMG data and analytics track prepares you with the analytical skills and critical thinking needed to keep pace with the modern world.

Distinct opportunity for future accountants

Students admitted to the KPMG data and analytics track will put new skills and knowledge to use as full-time KPMG interns for approximately eight weeks in the winter (January to March). After graduating, students in the KPMG data and analytics track will start as a full-time Experienced Associate at KPMG with the opportunity for an accelerated leadership career path.

Please note that data and analytics internships and experienced associate positions with KPMG are limited to those selected by KPMG for inclusion in this program. Learn more about how and when to apply to the W. P. Carey MACC.

Rigorous curriculum with real-world relevance

Going beyond traditional accounting principles, the KPMG data and analytics track provides a solid foundation in the technologies and methodologies used to navigate today’s highly complex and data-centric audit environment.

Note: The Data & Analytics Track curriculum as listed is tentative, pending final approval.

Addresses internal control frameworks and U.S. Generally Accepted Auditing Standards related to an auditor’s consideration of internal controls for purposes of financial statement audits and audits of internal control over financial reporting. Covers various schemes pertaining to misappropriation of assets, corruption, and fraudulent financial reporting. Students will apply audit software to evaluate control risks and fraud detection.

Addresses the emerging roles of accounting analytics in business, auditing, and tax contexts. Technological advances have allowed the capture and economic storage of massive accounting and business data and the focus of this class is how to productively gather and apply big data to a variety of accounting-related contexts.

Builds on skills obtained in the beginning auditing course, including audit planning, evidence, and reporting procedures along with addressing audit risks. Case- and application-based approach for evaluating advanced analytical procedures as well as auditing complex accounting estimates and fair values measurements.

Develops familiarity and expertise with tools and techniques for framing research questions about accounting and financial reporting issues, finding authoritative answers and evidence-based answers to such questions, and communicating the results. Case-based course provides hands-on experience researching relevant accounting issues.

Addresses theories of probability and uncertainty through the use of statistics including descriptive, predictive, and prescriptive analyses, as well as regression and other models to support audit decisions and conclusions.

Analysis of the role of financial statements and nonfinancial indicators in measuring value creation. Students will develop equity valuation models that incorporate business strategy and forecasting in evaluating the economic drivers of value. Students will also create and present a high-quality valuation report.

Focuses on understanding how to leverage visualization to understand large data sets, including the visualization strategies, management, format, and preparation of the dataset to be used and how to appropriately apply those insights to communications with fellow auditors, clients, and relevant third parties.

Takes a closer look at organizational and technical implications of classes of emerging technologies, placing emphasis on new, emerging, and potential technologies for audit and business data and analytics. In the past the course has focused on Big Data, Smart Mobility, and the Internet of Things as classes of emerging technologies.

Exploration of corporate governance systems by which business corporations are directed and controlled, and how these may contribute to sustainable enterprise. A case-based approach facilitates class discussion on the mechanisms of governance, analysis organizational strategies that facilitate sustainable, high-performing organizations, and the balance among various stakeholders.

Introduction to the practice of data mining and predictive modeling, with emphasis on use of data mining for audit and business applications. Study fundamental data-mining principles and techniques, and examine real-world examples and data to place data-mining techniques in context and develop data-analytic thinking. Students will complete this course with a broad set of practical data mining and predictive modeling skills based on hands-on experience with SAS Enterprise Miner.