Course and Faculty Introduction

Accounting has always been about analytical thinking. 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 professionals develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. 

Upon successful completion of this course, you will earn 21.5 ICMA CPE, a certificate of completion, and a digital badge.



Learning Outcomes

Upon successful completion of this course, you will be able to:

  • 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, and then assemble data from different sources into a structure that can be summarized, visualized, and analyzed.

  • Use Excel, Tableau, and Visual Basic for Applications to design and perform basic and advanced analyses.

Course Overview

Bridging Accountancy to Analytics

We identify how tasks in managerial accounting 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.

Assembling & Visualizing Data

Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. 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.

Mastering Data Analytic Tools

We 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).

Course Curriculum

  • 1

    Module 0: Course Introduction

    • Course Introduction

    • Glossary

  • 2

    Module 1: Introduction to Accountancy Analytics

    • Module 1 Overview

    • The Importance of Data and Analytics in Accounting

    • Advanced Data Analytics in Managerial Accounting Overview

    • Data Analytics Tools: Spreadsheets vs. Data Science Languages

    • Module 1 Conclusion
  • 3

    Module 2: Accounting and an Analytics Mindset

    • Module 2 Overview

    • Making Room for Empirical Enquiry

    • System 1 vs. System 2 Mindset

    • Knowledge Check 2-1

    • Linking Core Courses to Analytical Thinking

    • Inductive and Deductive Reasoning

    • Advanced Analytics and the Art of Persuasion

    • Knowledge Check 2-2

    • FACT Framework: Frame the Question

    • FACT Framework: Assemble the Data

    • FACT Framework: Calculate the Results

    • FACT Framework: Tell Others About the Results

    • FACT Framework: Review

    • Knowledge Check 2-3

    • Module 2 Conclusion

    • Module 2 Quiz
  • 4

    Module 3: Data and its Properties

    • Module 3 Overview

    • Characteristics that Make Data Useful for Decision Making

    • Structured vs. Unstructured Data

    • Properties of a Tidy Dataframe

    • Data Types

    • Data Dictionaries

    • Knowledge Check 3-1

    • Wide Data vs. Long Data

    • Merging Data

    • Data Automation

    • Visualization Distributions

    • Visualizing Data Relationships

    • Knowledge Check 3-2

    • Module 3 Conclusion

    • Module 3 Quiz
  • 5

    Module 4: Data Visualization 1

    • Module 4 Overview

    • Why Visualize Data?

    • Visual Perception Principles

    • Data Visualization Building Blocks

    • Knowledge Check 4-1

    • Basic Chart Data

    • Scatter Plots

    • Bar Charts

    • Box and Whisker Plots

    • Line Charts

    • Maps

    • Knowledge Check 4-2

    • Financial Chart Data

    • Waterfall Charts

    • Candlestick Charts

    • Treemaps and Sunburst Charts

    • Sparklines and Facets

    • Charts to Use Sparingly

    • Knowledge Check 4-3

    • Module 4 Conclusion

    • Module 4 Quiz
  • 6

    Module 5: Data Visualization 2

    • Module 5 Overview

    • Getting Started with Tableau

    • Scatter Plots in Tableau- 1

    • Scatter Plots in Tableau- 2

    • Bar Charts and Histograms in Tableau

    • Box Plots and Line Charts in Tableau

    • Adding Dimensions in Tableau

    • Facets and Groups in Tableau

    • Knowledge Check 5-1

    • Data Joins in Tableau

    • Tableau Analytics- Forecasts

    • Tableau Analytics - Clusters and Confidence Intervals

    • Communicating Tableau Analyses

    • Knowledge Check 5-2

    • Module 5 Conclusion

    • Module 5 Quiz
  • 7

    Module 6: Analytic Tools in Excel 1

    • Module 6 Overview

    • Framing a Question: Larry's Commissary

    • Assembling Data

    • Data Analysis ToolPak and Descriptive Statistics

    • Correlation

    • Knowledge Check 6-1

    • Linear Models

    • Simple Regression

    • Regression Diagnostics 1: Regression Summary, ANOVA, and Coefficient Estimates

    • Knowledge Check 6-2

    • Multiple Regression

    • Regression Diagnostics 2: Predicted Values, Residuals, and Standardized Residuals

    • Regression Diagnostics 3: Line Fit Plots, Adjusted R Square, and Heat Maps for P-Values

    • Making a Forecast with a Linear Model

    • Knowledge Check 6-3

    • Module 6 Conclusion

    • Module 6 Quiz
  • 8

    Module 7: Analytic Tools in Excel 2

    • Module 7 Overview

    • Polynomial Regression Models

    • Categorical Variables

    • Multiple Indicator Variables

    • Interaction Terms

    • Regression Summary

    • Knowledge Check 7-1

    • Optimization with Excel Solver

    • Solver Constraints and Reports

    • Logit Transformation

    • Simple Logistic Regression

    • Logistic Regression Accuracy

    • Knowledge Check 7-2

    • Module 7 Conclusion

    • Module 7 Quiz
  • 9

    Module 8: Automation in Excel

    • Module 8 Overview

    • Recording Macros

    • Basics of VB Editor

    • Basics of VBA

    • Knowledge Check 8-1

    • For Loops, Variables, Index Numbers, and Last Rows

    • Programming Hints

    • Conditional Statements

    • Knowledge Check 8-2

    • Macro for Creating Multiple Histograms

    • Clustering Overview

    • K-Means Clustering in Excel

    • K-Means Clustering Macro

    • Clustering On a Larger Scale

    • Knowledge Check 8-3

    • Module 8 Conclusion

    • Module 8 Quiz

    • Survey Instructions

    • Feedback Survey

    • Survey Verification

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