Auditing A Practical Approach With Data Analytics
Data analytics and emerging technology tools continue to evolve the business world, and employers expect new skillsets from graduates. Prepare your students to meet the rapidly changing demands of the workforce and become the future auditors and accounting professionals of tomorrow with Auditing: A Practical Approach with Data Analytics, 2nd Edition.
Auditing A Practical Approach with Data Analytics
Throughout the course, students work through a practical, case-based approach with a decision-making focus, all within a real-world context with the Cloud 9 continuing case, Audit Decision Cases, and Audit Decision-Making Examples. These cases and resources help students learn to think critically within the auditing context and refine the professional judgement and communication skills needed to make real business decisions auditors face every day.
With Auditing: A Practical Approach with Data Analytics you will be able to help students develop a deeper understanding of auditing procedures and learn how to perform a real-world audit, stay up-to-date on the latest audit standards technology tools, and develop the key skills to become the auditors of tomorrow.
Integrated throughout WileyPLUS, the Cloud 9 Continuing Case with accompanying assessment allows students to apply auditing topics to real-world company scenarios, better understanding the varied responsibilities and skillset required of the modern auditor.
The Audit Data Analytics chapter helps students understand what audit data analytics (ADAs) are and when an auditor can use them during an audit. Students work through various ADA approaches, data and documentation, and considerations to make when preparing visualizations. Feature boxes have been added to focus on examples of ADA software and end-of-chapter material tests student understanding of audit data analytics.
Data analytics is incorporated throughout the course. In response to the changing demands of the auditing profession, an entire data analytics chapter, as well as additional data analytics content woven throughout the course, helps students understand and apply their learning in a professional environment. Additionally, Tableau and IDEA exercises are included to give students practice in market-leading data visualization software that can be downloaded for free.
Auditing: A Practical Approach with Data Analytics, 2nd Edition helps students develop a deeper understanding of auditing procedures and learn how to perform a real-world audit, stay up-to-date on the latest audit technology tools, and develop the key skills to become the auditors of tomorrow. Throughout the course, students learn core auditing concepts efficiently through integrated assessment and WileyPLUS Adaptive Assignments, bridging the gap between course topics and real-world application.
IDEA Cases with NEW! Walkthrough Videos allow students to practice using IDEA software to analyze data within the auditing context. The new walkthrough videos give students step-by-step overviews on how to use IDEA software. An IDEA casebook and accompanying data sets, provided by Audimation Data Analytic Software and Services, is also available.
Students are introduced to the language, key processes, and level of thinking required to build ethical and audit reasoning through an integrated case-based approach that better prepares them for successful completion of the CPA exam and the builds the confidence needed to succeed as a modern auditing professional.
Rapid data streams are generated continuously from diverse sources including users, devices, and sensors located around the globe. Modern analytics services require the analysis of large quantities of such data streams derived from disparate geo-distributed sources. Further, the analytics requirements can be complex, resulting in complex trade-offs between cost, performance, and accuracy. A typical geo-distributed analytics service uses a hub-and-spoke model, comprising multiple edges connected by a wide area network (WAN) to a central data warehouse, which leads to the question of how much computation should be performed at the edges versus the center. While the traditional approach to analytics processing is to send all the data to a dedicated centralized location, an alternative approach would be to push all computing to the edge for in situ processing. However, neither approach is optimal for modern analytics requirements. Instead, the optimal solution often entails carefully orchestrating the analytics processing at both the center and the edges and is driven by factors such as application, data, and resource characteristics.
One central and enduring image of the social science researcher is of an individual who commits a great deal of time to collecting original, primary data from a field of enquiry. This approach is often underpinned by a sincerely held belief that key research questions can only be explored by the collection of ever new, and ever greater amounts of data, or that already existing data are insufficient for researchers to test their ideas. Yet such an approach to social science research can be problematic not least because the collection of primary data can be an expensive, time-consuming, and even wasteful approach to social enquiry.
Secondary analysis can serve many purposes, as well as being a valid approach in its own right. However, despite its widespread application, secondary analysis is often undervalued or perceived to be the preserve of only those interested in the re-use of large-scale survey data.
Highlighting both the theory and practice of secondary analysis and the use of secondary sources, this collection considers the nature of secondary analysis as a research tool; reflects on the definitional debates surrounding terms such as secondary analysis, data re-use and restudies; illustrates how secondary analysis is used in social science research; and finally reviews the practical, methodological and ethical aspects of secondary analysis.
In 1997, the Division of Continuing Studies, now the Division of Graduate Professional Studies, was established in the Rabb School specifically to extend the opportunity for excellent, applied professional education at the graduate level to a more diverse, part-time, working-adult population. All degree programs in the division are professionally oriented, applied in nature (combining requisite theory with the practical application of learned material), and taught by expert adjunct faculty who are practitioners of their subject matter in their professional lives.
The Strategic Analytics program offers a comprehensive study of these two components: the data itself and its business application, analyzed through a specific set of tools and techniques. Through the study of predictive, descriptive and prescriptive analytics, students will learn to identify patterns and trends within data to interpret and communicate the results in valuable and practical terms.
The Master of Science in Digital Marketing and Design program blends principles of design, tactics, and analysis across digital marketing, with a practical and applied focus. This program will cover the design and development of interactive media for use in digital marketing, the tactics necessary to deploy digital marketing initiatives, and the analytical frameworks to assess what is working and what is not in order to grow and optimize digital marketing campaigns. Students will gain a solid foundation in current web, media, and interface design practices across multiple platforms. Armed with the skills that inform what is technically, possible, students will then explore techniques to envision, plan, manage, and analyze digital marketing campaigns. Candidates will exit the program with a rich toolkit suitable for bringing a sound digital marketing approach to a variety of industries and companies.
The Master of Science in User-Centered Design program prepares students to guide a human-centered perspective in such areas as User Interface Design, Human Computer Interaction, Human Factors, User Experience (UX) and related specializations. The program provides students with the opportunity to develop a portfolio of artifacts that demonstrate their knowledge and ability to apply innovative thinking and a human centered approach to design as well as the leadership skills needed to implement and advocate for design thinking to foster innovation.