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Gear up for jobs in high-demand fields: data analytics, digital marketing, and more.We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. 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. 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.
In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets 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 visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic 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 to assemble, visualize, and analyze data. We introduce 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).