Merger, The: A Data Analytics Project, 1st edition

For courses in accounting info systems, data analytics, and auditing.

A comprehensive project to manipulate data and make important decisions in accounting

The Merger: A Data Analytics Project was designed to help instructors incorporate big data into their course while simultaneously meeting new accreditation standards. As such, it has students work with data to make important accounting decisions. Learners analyze a comprehensive data set, answer accompanying questions, and then write a report summarizing their findings.

By using real company data and situations in this 1st Edition, students are well equipped to make effective business decisions in class and in the real world.

Hallmark features of this title

Decision making with large data sets

• The text provides comprehensive data from an actual company, and asks students to analyze and make a series of business decisions. The project takes 4 to 5 weeks to complete.
• First, students load the merger data set, then analyze it by answering 10 to 15 questions.
• Next, students write a report that visualizes their findings and a process memo explaining how they found the solution.

New and updated features of this title

Incorporate data analytics into your course

• An Instructor's Resource Manual lets you easily merge cases into your course. You're also given instruction on how to assign, implement and grade the project.
• Meet the new AACSB standards by integrating data analytics into your accounting class.

Real-world problem-solving

• Students must figure out how to combine several CSV files from 2 merging companies (LargeMart and BullsEye) into 1 Microsoft Access database that they can then query.
• Students get practice with ETL (Extraction, Translation, Load). This project exposes students to the importance of data integrity in the ETL process.
• They are given (a total of 8) quarterly payroll register files with employee data, both hourly and salaried, for a year. Using this data, which consists of 1.7 million payroll files to query (including 10 different datasets), students can hone their analytical skills.