Course Description:

Why Accounting Analytics Matter: Data analytics is the discovery of patterns/knowledge from data. However, accounting students are not here just to learn about data analytics, they are here to learn data analytics in order to make better accounting and business decisions. Hence, the intent of this course is to provide an intuitive and practical introduction to data analytics tools/concepts using

problems/applications in financial and managerial accounting, auditing, taxation, and accounting information systems. The primary tool used will be R,  Excel and Tableau. Applications of data analytics in accounting include topics such as:

● Financial Accounting:  Compare  competing  strategies  (product  differentiation  and  cost leadership) through ROA decomposition, establish a company’s relative position (competitive advantage, parity, disadvantage) versus its peers.

● Managerial Accounting: Understand how we translate data into the information needed to monitor the performance of a business. For example, work with a retail business to analyze their sales and develop an interactive business dashboard.

● Auditing: Audit client records to identify fraud and assess inventory valuation.

● Taxation: Analyze client data for compliance with IRS rules.

● Accounting Information Systems:   Evaluate   payoffs   from   technology   investments. Understand emerging technologies (e.g., cloud computing, blockchain) and predict rate of adoption.

BAFM Program Level Learning Outcomes

Course Learning Outcomes

Programs delivered  by the School of Accounting and  Finance  (SAF) are designed to  provide students with the competencies, professionalism and practical experience that they need to excel in their chosen careers. By the end of the course, the students should be able to achieve the following objectives:

1.  Business Understanding: Identify business applications where we can use data analytics concepts and tools to answer questions and solve problems.

2.  Data Understanding and Preparation: Identify sources of data, collect and extract data, get familiar with data structure, identify quality issues, clean and transform data for analysis.

3.  Modeling: Explain data mining classification and/or prediction models in plain English, using simple examples and tools.

4.  Evaluation: Leverage mathematical (i.e., test statistics) and logical techniques to evaluate how valuable a model is, what it has found, and what you may want to do with the results.

5.  Deployment: Communicate your results and use the new insight to answer questions and solve problems.

This course will  pursue these objectives  by discussing the  basic theory  of data analytics and implement data analytics using R in a business context, using real-world data sets (in the measure possible) and with a view of developing professional skills.

This course’s learning outcomes map to the Program Level learning outcomes as follows:

Course Resources:

●     Textbook – Stratopoulos, T. (2022). Analytic Methods for Business: Foundation of Data Mining. Waterloo, ON.

Other Materials:

1.  R – a programming language that is platform agnostic and free to acquire – installation instructions to be given

2.  Excel for Microsoft 365, available to all students via UW’sOffice 365subscription

Course Evaluation:

Class Participation

Active participation leads to higher retention and understanding. Students should review assigned material before they come to class. Participation includes , during lecture, answering questions, making comments, and asking questions that help students understand the material, as well as working individually and in teams on class assignments/presentations.

To facilitate class participation and class interaction in lectures, we will use Top Hat as well. The most important component in these exercises is the opportunity to participate and if necessary, discuss the question within your team.

●     There are no make-ups for attendance and participation missed.

●     Impersonation, including the use of someone else’s Top Hat account carries a penalty of zero in class participation and will be reported as a violation of academic integrity

Your participation grade will be based on the number of Top Hat questions you have answered during the semester as well as your active participation during class time.

Weekly Quizzes

Weekly online quiz on topics and concepts covered in class. The quiz questions may be multiple choice, true/ false, or numeric (based on completion of R script analysis of assigned data sets) questions.

Team Assignment

This team project is designed to help you develop your analytics mindset. By way of a reminder, an analytics mindset is the ability to:

1) Ask the right questions.

2) Extract, transform and load relevant data.

3) Apply appropriate data analytics techniques.

4) Interpret and share the results with stakeholders.

More specifically, I will provide you with an accounting analytics case and you will have to work on this with your team. The  project  has two deliverables: the data analytics component, and the communication component. You will have to use R to complete the data analytics part and create a presentation. Details to be given during the semester.

Students are allowed to make their own groups of 5, but students who have not by May 30, will be auto-enrolled into a group.

Team Peer Evaluations

At the end of the course you will be asked to complete a summative peer evaluation of each of the members of your team along with a self-evaluation. These will impact your team assignment grade. Significant group issues that cannot be remedied by the peer evaluation should be raised to be resolved as early as possible.

Case-Based Exams

There are two case-based exams (midterm and final). Both exams may include material from the text, assigned additional readings, assignments, and lectures Both exams are cumulative, open book and open note. Further details on each exam will be provided ahead of the scheduled exam date.

Both examinations must be the exclusive work of the individual student.

Late Submission Policy

All submissions that are any time after the required submission date, that have not been granted an exception based on extenuating circumstances – with documentation, will be considered late. Late submissions are reduced by a nominal 10% of the mark. For example, consider a submission due June 17, 2022 11:59PM, deserving of 80%. If submitted June 18, 2022 12:00AM would receive a mark of 70%. Every subsequent day will be an additional 10% deduction.