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Chicago Cab Case Version 1

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* Version 1 (2018) has random data retrieved via API and uses Python only. * Version 2 (May 2022) uses data from 2021 and uses Python only. * Version 3 (May 2022) uses data from 2021 and uses Python and Tableau. This accounting advisory case helps students use Python to gather and analyze data to help resolve a business problem. Accountants deal with data every day and are increasingly under pressure to utilize the latest tools to analyze the largest data set possible to solve client problems. This case will let you these tools to examine millions of rows of data—all with the goal of helping a client be more profitable. This case will cover the key skills needed in an analytics approach to data. First, you will be required to gather, load, and clean data. Cleaning data is a critical skill that is unavoidable on any project of real substance. Second, you will describe and explore the data. This is usually the part of any project that is the most fun. Learning how to do this effectively and efficiently is critical. Third, you will use regression analysis to draw conclusions about your data. Using statistical methods helps the analyst have more security that their recommendations are valid. Finally, you will take what you have learned to tell a story. That is, you will use what you have learned to make a recommendation to the client. In this case, Yellow Cab Chicago, a small association of several different commercial transportation and cab companies, has hired your small accounting and consulting firm to help them reduce costs and increase revenues for their member firms. Like many traditional cab companies, Yellow Cab is feeling significant pressure from ride-hailing companies like Lyft and Uber. Your assignment is to use an authentic, existing data set to better understand the situation Yellow Cab is facing and to investigate potential revenue opportunities.


Jessen Hobson

Professor of Accountancy
University of Illinois at Urbana-Champaign

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