Overview

Class Central Tips

In this program, you’ll gain essential applied knowledge and develop the skills to evaluate and solve real-world business challenges. You’ll learn how to effectively present and communicate analysis and solutions—critical for turning findings into actionable insights—and build the in-demand skill set to prepare for career success in roles involving increasingly complex scenarios and large data sets. These roles include auditors, finance managers, management accountants, business analysts, and tax accountants and advisors.

You’ll benefit from access to rich on-demand content and live interactive sessions with faculty. As you work through in-depth exercises and case studies, you’ll receive actionable feedback from teaching staff and fellow learners.

Earning your certificate qualifies as continuing professional education for CPAs, and the curriculum also meets the needs of non-accounting professionals. Upon successful completion of the program, you may apply up to 12 credits towards the CPA educational requirements for licensure.

The certificate stacks directly into the full [Master of Science in Accountancy](https://www.coursera.org/degrees/imsa) (iMSA), [Master of Business Administration](https://www.coursera.org/degrees/imba) (iMBA), and [Master of Science in Management](https://www.coursera.org/degrees/ms-management-illinois) (iMSM) from the University of Illinois.

Syllabus

Course 1: ACCY 569: Data Driven Decisions in Accounting (Required)
- In this course, you’ll learn to use analytic software to explore, visualize, and statistically analyze accounting data. (2 credit hours)

Course 2: ACCY 576: Data Preparation for Accounting (Required)
- In this course, you’ll focus on developing Python skills for assembling business data, within the context of multiple accounting settings, including financial statement data, stock data, loan data, point-of-sale data, and more. (2 credit hours)

Course 3: ACCY 577: Machine Learning for Accounting
- In this course, you’ll explore machine learning algorithms and their applications in accounting problems. Topics covered include classification, regression, clustering, text analysis, and time series analysis. Feature importance and model optimization will also be discussed. (2 credit hours)

Course 4: ACCY 578: Accounting Analytics Applications
- In this course, you can develop your knowledge regarding the roles, methods, and implications of business and data analytics in accounting via real-world applications of fundamental and advanced analytics principles. Application opportunities span multiple areas of accounting, including audit, fraud identification and detection, financial accounting, and managerial accounting. After engaging in this course, you’ll possess a foundational understanding of the role of business and data analytics in accounting and be able to apply this knowledge to real-world business use cases. (1 credit hour)

Course 5: MBA 563: Data Toolkit: Business Data Modeling & Predictive Analytics
- In this course, you’ll learn to use several tools for processing business data and producing actionable insights. You’ll perform tasks including extracting, transforming, and loading data (ETL) to conducting exploratory data analytics (EDA). You’ll also gain experience finding patterns in data by using machine learning (ML) algorithms. You’ll develop a conceptual foundation for why ML algorithms are useful and explore how the resulting models from those algorithms are used to uncover actionable insights related to business problems. Ultimately, you’ll possess the skill set to evaluate when an algorithm should be used, have the ability to run these algorithms with R and RStudio, and communicate the results using notebooks. (4 credit hours)

Course 6: MBA 564: Data Analytics Applications in Business
- In this course, you’ll develop your knowledge regarding the roles, methods, and implications of business and data analytics. Application opportunities span multiple business areas, including marketing, finance, supply chain, and accounting. Topics covered may include forecasting using time series models, modern portfolio theory, measurement and scaling, A/B testing, ANOVA, conjoint analysis, and more. (2 credit hours)

Course 7: MBA 565: Infonomics
- In this course, you’ll explore methods for monetizing, managing, and measuring information from a non-technical perspective—as if it were any other kind of corporate asset. You’ll also cover topics such as information’s unique economic characteristics and legal status, the importance of alternative data sources, new and emerging information-related roles, and concerns surrounding information ownership, sovereignty, privacy, and ethics. (4 credit hours)