![]() ![]() Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. ![]() In the second section of the course, we emphasize the importance of assembling data. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. In the first section, we bridge accountancy to analytics. We’ve divided the course into three main sections. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. Accounting has always been about analytical thinking.
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