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Accounting Analytics, 1st Edition

Daniel Ames

  • Copyright 2027
  • {{checkPublicationMessage('Available 18 September 2026', '2026-09-18T00:00:00+0000')}}
Starting At $177.95 See pricing and ISBN options
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Accounting Analytics 1st Edition by Daniel Ames
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Overview

"Accounting Analytics: Turning Data into Decisions" guides students on how analysts approach questions, gather and prepare data, perform meaningful analysis and communicate insights. The book's 15 chapters strike a balance between conceptual grounding and hands‑on application, providing a strong pedagogical design, illustrations and problem sets using tools such as Excel, Tableau, Alteryx and Python. Chapters build off of the CRAFT framework -- Consider, Retrieve, Analyze, Frame and Tell -- which provides students with an intuitive structure for thinking through analytics problems. Assessment materials include learning objectives, progress checks, discussion questions and in-depth labs or cases. The book aligns with analytics learning objectives from AACSB and the AICPA to integrate into modern accounting curricula and support programs that build data literacy and analytical confidence.

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Meet the Authors

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Daniel Ames

Daniel Ames, Ph.D., is a Professor of Accounting at Bryant University. He teaches and researches in the areas of accounting analytics, financial reporting, and emerging technologies in the profession. His work bridges data analytics and decision-making, with a focus on how accountants transform raw data into actionable conclusions. Dr. Ames has led curricular innovations in analytics education, designed graduate programs, and regularly collaborates with faculty and industry leaders on the future of accounting practice. In his free time, he enjoys running and volunteer work. He and his wife are the parents of seven children.

Features

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  • All chapters include thought questions, multiple‑choice items and problems, plus advanced Excel, Tableau, Alteryx and Python labs in CNOWv2 to support deeper skill development.
  • Each chapter uses the CRAFT framework -- Consider, Retrieve, Analyze, Frame and Tell -- to guide students from raw data to decisions and help them see the bigger analytical process.
  • Hands‑on use of Alteryx, Python, Excel and Tableau gives students practical experience with tools that accountants and analysts rely on, connecting coursework to real workflows.
  • A dedicated chapter, “Tidy Data,” explains data preparation in depth, giving students clear guidance for cleaning, structuring and organizing data for effective analysis.
  • Students build statistical skills through t-tests, regression and logistic regression, supported by clear accounting examples that make analytics accessible to all learners.
  • End‑of‑chapter assessments reinforce key learning objectives, allowing students to apply skills they learned.
  • The broad range of topics gives instructors flexibility, allowing them to select the chapters most essential to their course and tailor the material as needed.

Table of Contents

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1. Introduction to Data Analytics.
2. Tidy Data.
3. Descriptive Statistics.
4. Data Visualization.
5. Probability and Statistical Inference.
7. More topics in regression analysis.
8. Logistic Regression Analysis.
9. Data Mining.
10. Robotic Process Automation.
11. Audit Analytics.
12. Tax Analytics.
13. Managerial Analytics.
15. AI and Accounting.