| Graduate Program Head | Henock Louis |
|---|---|
| Program Code | MAA |
| Campus(es) | University Park (M.Acc.) |
| Degrees Conferred | Master of Accounting (M.Acc.) |
| The Graduate Faculty |
The Master of Accounting in Accounting Analytics program qualifies students to assume responsibilities beyond those that are traditionally assumed by graduates from Master of Accounting (MAcc) programs. The Accounting Analytics (MAA) curriculum focuses heavily on the use of complex searchable databases and analytical search tools in a financial accounting and auditing environment, with a core emphasis on modelling, mathematical applications, and predictive science. As students learn the use of quantitative tools, they will be trained to apply the acquired knowledge to identify exceptions to the norm within large data sets and to use the best approaches to quantify potential misstatements within financial accounting information.
Admission Requirements
Applicants apply for admission to the program via the Graduate School application for admission. Requirements listed here are in addition to Graduate Council policies listed under GCAC-300 Admissions Policies.
Applicants must:
- Submit a completed online Graduate School Application for Admission, including short admissions essay, resume, and three professional references with complete contact information.
- Résumé reflecting relevant professional experience including internships and co-op experiences.
- Submit official transcripts from all post-secondary institutions attended.
GMAT and/or GRE scores will not be required for admission. Accordingly, these scores will not be accepted.
The language of instruction at Penn State is English. English proficiency test scores (TOEFL/IELTS) may be required for international applicants. See GCAC-305 Admission Requirements for International Students for more information.
Degree Requirements
Requirements listed here are in addition to Graduate Council policies listed under GCAC-700 Professional Degree Policies.
The total number of credits required for the M.Acc. degree is 30 credits at the 400, 500, or 800 level, with at least 18 credits at the 500 or 800 level and at least 6 credits at the 500 level.
There are 30 specified credits comprised of the following courses:
| Code | Title | Credits |
|---|---|---|
| Core Courses | ||
| The core Accounting Analytics courses are designed to provide students with a broad set of skills and tools related to the field of Data Science, within an Accounting-focused knowledge domain. | ||
| ACCTG 432 | Accounting Information Systems | 3 |
| ACCTG 522 | Corporate Taxation and Financial Reporting | 3 |
| ACCTG 573 | Topics in Financial Reporting | 3 |
| ACCTG 804 | Data Analytics in the Accounting Profession | 3 |
| ACCTG 881 | Financial Statement Analysis | 3 |
| Electives | ||
| Students will also complete 12 credits of elective courses. A list of elective courses approved to count towards the degree requirements will be maintained by the program office. | 12 | |
| Culminating Experience | ||
| The culminating experience for the degree is a capstone course that provides an opportunity for students to apply and integrate the knowledge and skills that were gained throughout the program. | ||
| ACCTG 805 | Applied Accounting and Financial Analytics | 3 |
| Total Credits | 30 | |
Minor
A graduate minor is available in any approved graduate major or dual-title program. The default requirements for a graduate minor are stated in Graduate Council policies listed under GCAC-600 Research Degree Policies and GCAC-700 Professional Degree Policies, depending on the type of degree the student is pursuing:
Student Aid
Refer to the Tuition & Funding section of The Graduate School's website. Students in this program are not eligible for graduate assistantships.
Courses
Graduate courses carry numbers from 500 to 699 and 800 to 899. Advanced undergraduate courses numbered between 400 and 499 may be used to meet some graduate degree requirements when taken by graduate students. Courses below the 400 level may not. A graduate student may register for or audit these courses in order to make up deficiencies or to fill in gaps in previous education but not to meet requirements for an advanced degree.
Learning Outcomes
- Achieve a Data-Driven Mindset – the MAA program will help students develop a datadriven mindset, preparing them to interact with data scientists from an accountant’s perspective. The program will provide exposure to topics on unstructured data, data extraction, data modeling, visualizations and relational databases.
- Develop Business Intelligence – the MAA program will enable students to develop the skills necessary to apply modern analytical methods yielding descriptive, diagnostic, predictive, and prescriptive accounting information used for business intelligence.
- Interact with Current Technology – the MAA program will help provide experience with audit, tax, and financial analytical cases and technologies such as Excel, SAS, and Python and visualization software such as Tableau and Microsoft Power BI.
- Explore Contemporary Topics – the MAA program will expose student to contemporary topics such as fintech, cryptocurrencies, web scraping, and the use of blockchain.
Contact
| Campus | University Park |
|---|---|
| Graduate Program Head | Henock Louis |
| Director of Graduate Studies (DGS) or Professor-in-Charge (PIC) | Scott Collins |
| Program Contact | Tammy Whitehill |
| Program Website | View |