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ISBN10: 1266079211 | ISBN13: 9781266079214

* The estimated amount of time this product will be on the market is based on a number of factors, including faculty input to instructional design and the prior revision cycle and updates to academic research-which typically results in a revision cycle ranging from every two to four years for this product. Pricing subject to change at any time.
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Data Analytics for Accounting is designed to prepare your students with the necessary tools and skills they need to successfully perform data analytics through a conceptual framework and hands-on practice with real-world data. Once students understand the foundation, they are provided hands-on practice with real-world data sets and various data analysis tools which students will use throughout the rest of their careers. The data analysis tools are structured around three tracks—the Microsoft track (Excel, Power Pivot, and Power BI), the Tableau track (Tableau Prep and Tableau Desktop), and the Alteryx track. Using multiple tools allows students to learn skills in various software to gain technical agility. Data Analytics for Accounting is a full course data analytics solution guaranteed to prepare your students for their future careers as accountants.This title is part of the Self Print Evergreen program, which means the McGraw Hill eBook will soon offer enhanced functionality, allowing students to print text content. With self-print enabled, students can easily access and print the most up-to-date material, including all Evergreen updates. By offering the McGraw Hill eBook either standalone or within our Connect or GO platforms, students gain access to this convenient print feature at no additional cost for print materials.
2. Mastering the Data
3. Performing the Test Plan and Analyzing the Results
4. Communicating Results and Visualizations
5. The Modern Accounting Environment
6. Audit Data Analytics
7. Managerial Analytics
8. Financial Statement Analytics
9. Tax Analytics
10. Project Chapter (Basic)
11. Project Chapter (Advanced): Analyzing Dillard’s Data to
Predict Sales Returns
Appendices:
A. Basic Statistics Tutorial
B. Excel (Formatting, Sorting, Filtering, and PivotTables)
C. Accessing the Excel Data Analysis Toolpak
D. SQL Part 1
E. SQL Part 2
F. Power Query in Excel and Power BI
G. Power BI Desktop
H. Tableau Prep Builder
I. Tableau Desktop
J. AlteryxK: Data Dictionaries
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