Introduction to Data Analytics (course)

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Audit Areas

This introduction to data analytics forces auditors out of the “What did they do last year?” mentality and teaches them to ask, “What does the data tell me?” This is a hands-on learning experience in which the participants use basic analytical tools to form a better understanding of a company and audit risk.

During this course, the participants learn to apply a structured approach to evidence-based decisions, beginning with a clearly defined audit objective and how to translate that objective into a data analysis question. They will learn a small number of useful data analysis techniques that can help with risk assessment, audit planning and the design of audit procedures. Finally, they will use data analytics tools to apply these techniques to the case study data set and analyze the results.

Learning Outcomes

  • Apply the definition of data analytics to the audit engagement tasks you currently face, including engagement management.
  • Follow the analytics life cycle approach to address audit issues, including:
  • Defining audit planning objectives and creating an audit question to be solved through analysis.
  • Leveraging a basic understanding of the client and its industry to create a testable hypothesis.
  • Modeling a data analysis solution to test the hypothesis using a small selection of data mining techniques.
  • Analyzing the results and use the analysis to plan appropriate subsequent actions.

Course Agenda

  • What Is Data Analytics and What Can It Do for Me?

    We define data analytics as a tool that helps one make better-informed decisions. We provide them with an overview of the data analytics lifecycle, a structured process that will allow them to deploy data analytics more effectively and efficiently.
  • Flatiron Solar

    The participants gather anecdotal information about their case study client, Flatiron Solar. They review a preliminary analytical review of the company that is typical of what most audit teams produce.
  • Build Better Habits

    The class returns to the original planning analytics for Flatiron and compares it to what they discovered by applying the data analytics lifecycle. In this way they discover the value in performing a rigorous analysis of financial data during audit planning.
  • Frame the Problem and Form a Hypothesis

    Using the Flatiron case study, the participants apply the first two steps in the data analytics lifecycle.
  • Data Analytics Techniques and Modeling

    We introduce plain English versions of the core principles of data analytics such as classification, prediction, association, data and dimension reduction, data exploration, and our favorite, the bell-shaped curve.
  • Data Analytics Workshop

    Using the firm’s data analysis tools, participants examine two years of sales transactions at Flatiron Solar and analyze the results.
  • Take Action

    Based on the results of their analysis, the teams redesign their audit approach at Flatiron Solar to address the newly discovered and revised audit risks.