Introduction to Data Analytics For Accounting

✍ Scribed by Vernon Richardson, Katie Terrell and Ryan Teeter

• Publisher: McGraw Hill LLC
• Year: 2024
• Tongue: English
• Leaves: 689
• Edition: 2
• Category: Library

✦ Synopsis

The combination of computerization and automation of many accounting tasks as well as the explosion of available data is changing the accounting profession. To address this, accountants are increasingly required to have an analytics mindset to perform their jobs. Building upon the fundamentals of accounting learned in prior courses, Introduction to Data Analytics for Accounting explores accounting concepts through the application of data analytics. We recognize students need to not only develop the skills to ask the right questions, but to learn how to use tools they may encounter in the workplace such as Excel®, Tableau®, and PowerBI® to examine and analyze data, and then effectively interpret results to make business decisions. This analytics mindset is crucial early in the study of accounting to meet the demands of today's accounting jobs.

✦ Table of Contents

Cover
Introduction to Data Analytics for Accounting
Dedications
About the Authors
Preface
Key Features
Available in Connect
New to the Second Edition
Acknowledgments
Brief Table of Contents
Detailed Table of Contents
Chapter 1: Ask the Question: Using Data Analytics to Address Accounting Questions
The Explosion of Data and the Impact on the Accounting Profession
Accountants Need to Develop Critical Thinking Skills
Data Analytics And The Amps Model
The AMPS Model: Ask the Question
The AMPS Model: Master the Data (Chapters 2ñ4)
The AMPS Model: Perform the Analysis (Chapters 5ñ9)
The AMPS Model: Share the Story (Chapter 10)
The Recursive Nature of the AMPS Model
Using Visualizations to Analyze Data and Communicate Results
Software Tools Available to Perform Data Analytics
Summary
Key Words
Answers to Progress Checks
Multiple Choice Questions
Discussion Questions
Brief Exercises
Problems
Labs Associated with Chapter 1
Lab 1-1: Excel: Journal Entries to Trial Balance
Lab 1-2: Excel: Calculating Depreciation Using Excel Functions
Lab 1-3: Excel: Creating a Mortgage Amortization Schedule
Chapter 2: Master the Data: An Introduction to Accounting Data
Data, Data Analytics, and Accounting Questions!
Master The Data: The Second Step of the Amps Model
What is Big Data?
Accounting Data Sources
Financial Accounting Data
Financial Accounting-Related Data
Managerial Accounting Data
Tax Data
Non-Accounting Data Sources
Data Ethics
Gathering Data
Protecting Data
Some Excel Basics: The Pivottable
Summary
Key Words
Answers to Progress Checks
Multiple Choice Questions
Discussion Questions
Brief Exercises
Problems
Labs Associated with Chapter 2
Lab 2-1: Excel: Accounts Receivable Summary by Customer
Lab 2-1: Tableau: Accounts Receivable Summary by Customer
Lab 2-1: Power BI: Accounts Receivable Summary by Customer
Lab 2-2: Excel: Inventory Management by Customer Profitability
Lab 2-2: Tableau: Inventory Management by Customer Profitability
Lab 2-2: Power BI: Inventory Management by Customer Profitability
Lab 2-3: Excel: Inventory Management by SKU Profitability
Lab 2-3: Tableau: Inventory Management by SKU Profitability
Lab 2-3: Power BI: Inventory Management by SKU Profitability
Chapter 3: Master the Data: Data Types Used in Accounting
Examples of Data Types
Introduction to Structured Data Types: Categorical versus Numerical
Additional Ways to Categorize Data Based on Tools
Analyzing Data Using Both Categorical and Numerical Variables in a Pivottable
Accounting Data, Data Types, and Accounting Databases
Simplified Product Tables
Data Dictionaries and Data Catalogs
Summary
Key Words
Answers to Progress Checks
Multiple Choice Questions
Discussion Questions
Brief Exercises
Problems
Labs Associated with Chapter 3
Lab 3-1: Excel: Identify and Work with Different Data Types
Lab 3-1: Tableau: Identify and work with Different Data Types
Lab 3-1: Power BI: Identify and Work with Different Data Types
Lab 3-2: Excel: Visualize Different Data Types
Lab 3-2: Tableau: Visualize Different Data Types
Lab 3-2: Power BI: Visualize Different Data Types
Chapter 4: Master the Data: Preparing Data for Analysis
What are the Differences among a Database, Excel, and Data Visualization Tools (Tableau and Power BI)?
Relational Databases
Relational Database Data Dictionaries And Entity-Relationship Diagrams
Relational Database Data Dictionary
Relational Database Diagrams
Data Storage: Advantages of Using Relational Databases
Data Integrity Benefits of Storing Data in Relational Databases
Internal Control Benefits of Storing Data in Relational Databases
Extract, Transform, and Load: Using Excel, Power BI, Tableau, and Query Tools to Access Data in Company Databases
Extract, Transform, and Load
Extract: Connecting to Data in Excel
Extract: Connecting to Data in Tableau
Extract: Connecting to Data in Power BI
Extract and Transform: Connecting to a Subset of Data from a Database Using SQL
Extract, Transform, and Load: Using Excel Query Tools to Access Data in Databases External to the Company
Obtaining Data from the Web through Excel
Summary
Key Words
Answers to Progress Checks
Multiple Choice Questions
Discussion Questions
Brief Exercises
Problems
Labs Associated with Chapter 4
Lab 4-1: Excel: Working with Data in Ranges and Tables
Lab 4-2: Excel: Linking Two Tables Using VLOOKUP for State Tax Rates
Lab 4-3: Excel: Linking Two Tables Using VLOOKUP for Relational Data
Lab 4-4: Excel: Linking Tables with a Model
Lab 4-4: Tableau: Linking Tables with a Model
Lab 4-4: Power BI: Linking Tables with a Model
Appendix 4A: SQL Queries
Chapter 5: Perform the Analysis: Types of Data Analytics
The Next Step of the Amps Model: Perform the Analysis
Matching the Analytics with the Accounting Question
Descriptive Analytics
Statistical and Summarization Tools for Descriptive Analytics
Examples of Descriptive Analytics
Diagnostic Analytics
Identify Anomalies/Outliers
Finding Previously Unknown Linkages, Patterns, or Relationships Between and Among Variables
Predictive Analytics
Prescriptive Analytics
Summary of Analyses used to Address Accounting Questions
A Review of Basic Statistics and Hypothesis Testing
Population vs. Sample
Parameters vs. Statistics: What Is the Difference?
Describing the Sample by Its Central Tendency, the Middle, or the Most Typical Value
Describing the Spread (or Variability) of the Data
Probability Distributions
Hypothesis Testing
Statistical Testing
Statistical Test of a Difference of Means of Two Groups
Interpreting the Statistical Output from a Regression
Introduction to Tools used in Data Analytics
Perform the Analysis Using Microsoft Excel Tools/Functions
The Excel Data Analysis Toolpak
Summary
Key Words
Answers to Progress Checks
Multiple Choice Questions
Discussion Questions
Brief Exercises
Problems
Labs Associated with Chapter 5
Lab 5-1: Excel: Descriptive Statistics for the Retail Industry
Lab 5-1: Tableau: Descriptive Statistics for the Retail Industry
Lab 5-1: Power BI: Descriptive Statistics for the Retail Industry
Lab 5-2: Excel: Using Conditional Formatting to Perform Bank Reconciliations
Chapter 6: Perform the Analysis: Descriptive Analytics
Defining Descriptive Analytics
Accounting Data used in Descriptive Analytics
Tools And Techniques Used In Descriptive Analytics
Examples of Descriptive Analytics
Descriptive Analytics of Financial Performance Using Tables and Graphs
Considering the Right Comparison Group for Analysis
Descriptive Analysis Using PivotTables and Bar Charts for Accounts Receivable Aging
Horizontal, Vertical, and DuPont Analysis of Financial Performance
Using Descriptive Analytics to Identify Phenomena That Might Require Additional Analysis, Including Diagnostic Analytics
Summary
Key Words
Answers to Progress Checks
Multiple Choice Questions
Discussion Questions
Brief Exercises
Problems
Labs Associated with Chapter 6
Lab 6-1: Excel: Accounts Receivable Aging
Lab 6-1: Tableau: Accounts Receivable Aging
Lab 6-1: Power BI: Accounts Receivable Aging
Lab 6-2: Excel: Horizontal Analysis of Financial Performance with Sparklines
Lab 6-3: Excel: Vertical Analysis of Financial Performance (with Sparklines)
Lab 6-4: Excel: DuPont Analysis of Financial Performance
Chapter 7: Perform the Analysis: Diagnostic Analytics
Defining Diagnostic Analytics
Identifying Anomalies and Outliers
Diagnostic Analytic Techniques for Identifying Anomalies and Outliers
Finding Previously Unknown Linkages, Patterns, or Relationships Between and Among Variables
Performing Drill-Down, Detailed Analytics
Determining Statistical Linkages, Patterns and Relationships Among Variables Using Statistical Tools and Techniques
Hypothesis Testing Using a Difference in Means
Hypothesis Testing Using Regression
Summary
Key Words
Answers to Progress Checks
Multiple Choice Questions
Discussion Questions
Brief Exercises
Problems
Labs Associated with Chapter 7
Lab 7-1: Excel: Test of Separation of Duties
Lab 7-1: Tableau: Test of Separation of Duties
Lab 7-1: Power BI: Test of Separation of Duties
Lab 7-2: Excel: Days of the Week Journal Transactions
Lab 7-2: Tableau: Days of the Week Journal Transactions
Lab 7-2: Power BI: Days of the Week Journal Transactions
Lab 7-3: Excel: Using the MATCH() Function to Perform Bank Reconciliations
Lab 7-4: Excel: Benfordís Law
Lab 7-5: Excel: Fuzzy Matching and Fake Employees/Vendors
Lab 7-6: Excel: Sequence Check: Identifying Missing Checks
Lab 7-7: Excel: Duplicate Payments
Lab 7-8: Excel: Looking for Fraud by Examining Relationships within a Data File: Accounts Payable Clerks and Company Vendors
Lab 7-8: Tableau: Looking for Fraud by Examining Relationships within a Data File: Accounts Payable Clerks and Company Vendors
Lab 7-8: Power BI: Looking for Fraud by Examining Relationships within a Data File: Accounts Payable Clerks and Company Vendors
Lab 7-9: Excel: Evaluating the Relationship between Sales and Advertising Expense
Chapter 8: Perform the Analysis: Predictive Analytics
Introduction to Predictive Analytics
Classification
Bankruptcy Classification
Loan Extension Classification
Fraud/No Fraud Classification
Regression
Base Rates and Base Rate Fallacy
Forecasting Future Performance using Time Series Analysis
Predictive Analytics and Hypothesis Testing
Predictive Analytics and Machine Learning
Summary
Key Words
Answers to Progress Checks
Multiple Choice Questions
Discussion Questions
Brief Exercises
Problems
Labs Associated with Chapter 8
Lab 8-1: Excel: Predicting Bankruptcy Using Altmanís Z
Lab 8-2: Excel: Classifying Loan Acceptance Using Lending Club Data
Lab 8-3: Excel: Estimating Cost Behavior Using Regression Analysis
Lab 8-4: Excel: Estimating Activity-Based Costing Drivers Using Regression Analysis
Lab 8-5: Excel: Estimating Borrower Interest Rates Using Regression Analysis with Lending Club Data
Lab 8-6: Excel: Forecasting Future Performance (Sales and Earnings for IBM and Netflix)
Lab 8-7: Tableau: Forecasting Future Performance (Sales and Earnings for IBM and Netflix)
Lab 8-8: Power BI: Forecasting Future Performance (Sales and Earnings for IBM and Netflix)
Chapter 9: Perform the Analysis: Prescriptive Analytics
Linking Back to the Amps Model
Definition of Prescriptive Analytics
Constraints
Changing Conditions
Prescriptive Analytics Techniques
Marginal Analysis
Make-or-Buy Analysis: Making Outsourcing Decisions
Cash Flow Analysis
Accounting Rate of Return and Payback Period
Net Present Value and Internal Rate of Return
Evaluating Future Cash Flows: Net Present Value and Installment Payments
Evaluating Future Cash Flows: Capital Budgeting and Investment Decisions
Goal Seek Analysis
Scenario Analysis
An Example of Scenario Analysis Using Potential Tax Rate Scenarios
Sensitivity Analysis
Optimization
Summary
Key Words
Answers to Progress Checks
Multiple Choice Questions
Discussion Questions
Brief Exercises
Problems
Labs Associated with Chapter 9
Lab 9-1: Excel: Lump Sum or Annuity?
Lab 9-2: Excel: Evaluating Investments Using NPV
Lab 9-3: Excel: Capital Budgeting Using NPV
Lab 9-4: Excel: Evaluating Investments Using IRR
Lab 9-5: Excel: Capital Budgeting Using IRR
Lab 9-6: Excel: Face, Discount, or Premium?
Lab 9-7: Excel: What-If Analysis with Goal Seek/Breakeven
Lab 9-8: Excel: What-If Analysis with Goal Seek/Final Exam Grade
Lab 9-9: Excel: What-If Scenario/ Tax Rates
Chapter 10: Share the Story
The Basics of Data Visualization
Visualizing Descriptive Statistics and Analytics
Presenting Data in a Dashboard
Bar Charts versus Histograms
Visualizing Diagnostic Statistics and Analytics: Outliers and Anomalies
Exploratory Diagnostic Analytics Using Data Visualization
Visualizing Predictive Statistics and Analytics
Correlation and Regression
Forecasting with Time Series Data
Visualizing Prescriptive Statistics and Analytics
Sensitivity Analysis
Breakeven Analysis
Communicating your Data with Words: Executive Summaries and Reports
Summary
Key Words
Answers to Progress Checks
Multiple Choice Questions
Discussion Questions
Brief Exercises
Problems
Labs Associated with Chapter 10
Lab 10-1: Excel: Create a Dashboard Using PivotTables and Slicers
Lab 10-2: Tableau: Create a Dashboard
Lab 10-2: Power BI: Create a Dashboard
Chapter 11: Capstone Projects Using the AMPS Model
Using the Amps Model to Address Accounting Questions
Application of the Amps Model to Your Own Project(S)
Project 1: Using the Amps Model to Address the Question of Loan Repayment
Ask the Question
Master the Data
Perform the Analysis
Share the Story
Project 2: Completing Your Own Project Using the Amps Model
Index