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January 27, 2025
Article · 6 min read
This course is part of Accounting Data Analytics Specialization
Instructor: Ronald Guymon
29,538 already enrolled
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(440 reviews)
(440 reviews)
Articulate the benefits of using Big Data and analytics in the modern accounting profession.
Describe and implement a framework for using Big data to help provide insights that lead to action.
Critique the ability of a dataset to answer questions, then assemble data from different sources for summarization, visualization, and analysis.
Use Excel, Tableau, and Visual Basic for Applications to design and perform basic and advanced analyses.
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Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. 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. 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. 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.
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).
In this module, you will become familiar with the course, your instructor and your classmates, and our learning environment. This orientation module will also help you obtain the technical skills required to navigate and be successful in this course.
2 videos6 readings1 discussion prompt1 plugin
In this module, you will learn how the accounting profession has evolved. You will recognize how data analytics has influenced the accounting profession and how accountants have the ability to impact how data analytics is used in the profession, as well as in an organization. Finally, you will learn how data analytics is influencing the different subdomains within accounting.
12 videos2 readings1 quiz2 assignments1 discussion prompt
In this module, you will learn to recognize the importance of making room for empirical enquiry in decision making. You will explore characteristics of an analytical mindset in business and accounting contexts, and link those to your core courses. You will then evaluate a framework for making data-driven decisions using big data.
12 videos2 readings4 assignments
This module looks at specific characteristics of data that make it useful for decision making.
12 videos2 readings3 assignments
In this module, you will learn fundamental principles that underlie data visualizations. Using those principles, you will identify use cases for different charts and learn how to build those charts in Excel. You will then use your knowledge of different charts to identify alternative charts that are better suited for directing attention.
17 videos2 readings4 assignments1 peer review
In this module, you’ll learn how to use Tableau to do with data what spies do when observing their surroundings: get an overview of the data, narrow in on certain aspects of the data that seem abnormal, and then analyze the data. Tableau is a great tool for facilitating the overview, zoom, then filter details-on-demand approach. Tableau is a lot like a more powerful version of Excel's pivot table and pivot chart functionality.
13 videos2 readings3 assignments
In this module, you'll be guided through a mini-case study that will illustrate the first three parts of the FACT model, with a focus on the C, or calculations part of the FACT model. First, you will perform a correlation analysis to identify two-way relationships, and analyze correlations using a correlation matrix and scatter plots. You will then build on your knowledge of correlations and learn how to perform regression analysis in Excel. Finally, you will learn how to interpret and evaluate the diagnostic metrics and plots of a regression analysis.
13 videos2 readings4 assignments1 peer review
In this module, you’ll learn how the regression algorithm can be applied to fit a wide variety of relationships among data. Specifically, you’ll learn how to set up the data and run a regression to estimate the parameters of nonlinear relationships, categorical independent variables. You’ll also investigate if the effect of an independent variable depends on the level of another independent variable by including interaction terms in the multiple regression model. Another aspect of this module is learning how to evaluate models, regression or otherwise, to find the most favorable levels of the independent variables. For models that explain revenue, the most favorable levels of the independent variables will maximize revenue. In contrast, if you have a model that describes costs, like a budget, then the most favorable levels of the independent variables will minimize costs. Optimizing models can be difficult because there are so many inputs and constraints that need to be managed. In this module, you’ll learn how to use the Solver Add-In to find the optimal level of inputs. For some models, the dependent variable is a binary variable that has only two values, such as true/false, win/lose, or invest/not invest. In these situations, a special type of regression, called logistic regression, is used to predict how each observation should be classified. You’ll learn about the logit transformation that’s used to convert a binary outcome to a linear relationship with the independent variables. Excel doesn’t have a built-in logistic regression tool, so you’ll learn how to manually design a logistic regression model, and then optimize the parameters using the Solver Add-In tool.
12 videos2 readings3 assignments
The lessons in this module are organized around several useful tasks, including stacking multiple dataframes together into one dataframe, creating multiple histograms to accompany the descriptive statistics, and learning how to perform k-means clustering. After going through this module, you’ll not only gain a foundation to help you understand coding, but you’ll also learn more about analyzing financial data. Along the way, I hope that you’ll also pick up on a few other useful Excel functions.
14 videos4 readings4 assignments1 plugin
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This course is part of the following degree program(s) offered by University of Illinois Urbana-Champaign. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
This course is part of the following degree program(s) offered by University of Illinois Urbana-Champaign. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
University of Illinois Urbana-Champaign
Degree · 1.5 – 3 years
University of Illinois Urbana-Champaign
Degree · 6-10 months
University of Illinois Urbana-Champaign
Degree · 8 months
¹Successful application and enrollment are required. Eligibility requirements apply. Each institution determines the number of credits recognized by completing this content that may count towards degree requirements, considering any existing credits you may have. Click on a specific course for more information.
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Reviewed on May 30, 2020
It teaches us the basics of data analytics and it is very progressive. There are assignments to help us understand and practice the methods being taught. This allows us to have first-hand experiences.
Reviewed on Jun 2, 2020
The lecturer is very concise and goes through concepts in depth by showing examples that build on top of previous knowledge and used dataset that was previously introduced.
Reviewed on Jun 11, 2020
Very insightful session on how to get the best picture out of huge data. I certainly like the homework as it gave me time to practice on certain items. I highly recommend to those who take
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