The virtual course "Introduction to the analysis and visualization of accounting data - Virtual Course - Coursera", is a course with different contents and offers video classes from . Explore its essential features, and click the orange button for detailed information on the Coursera e-Learning platform.
Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of mathematics and order in analyzing business transactions. The skill set that accountants have needed to perform math and maintain order has evolved from pencil and paper, to typewriters and calculators, to spreadsheets and accounting software. A new skill set that is becoming more important to nearly every aspect of business is big data analytics: analyzing vast amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analysis programming languages such as Python and R. We have divided the course into three main sections. In the first section, we connect accounting with analysis. 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 then explore how those tasks can be completed more effectively and efficiently through use of big data. analytics. We then present a FACT framework to guide big data analysis: ask a question, assemble data, calculate the data, and tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain the desirable characteristics of both the data and data sets that will lead to effective calculations and visualizations. In the third and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe the principles of visual perception and then apply those principles to create effective visualizations. We then look at fundamental data analytics tools such as regression, linear programming (using Excel Solver), and clustering in the context of point-of-sale data and loan data. We conclude by demonstrating the power of data analytic programming languages for assembling, visualizing, and analyzing data. We present Visual Basic for Applications as an example of a programming language and the Visual Basic Editor as an example of an integrated development environment (IDE). We conclude by demonstrating the power of data analytic programming languages for assembling, visualizing, and analyzing data. We present Visual Basic for Applications as an example of a programming language and the Visual Basic Editor as an example of an integrated development environment (IDE). We conclude by demonstrating the power of data analytic programming languages for assembling, visualizing, and analyzing data.
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