Course Description
From financial statements to operating performance, the environment we are living in is “data rich, information poor.” With the decision making pressure on accountants and managers, it is crucial to gather available data quickly and provide accurate analysis. In this course the student will learn to analyze financial statements using lesser known techniques, and to address the differences among reports generated under different accounting frameworks. Students will learn to use data analytics, identify the control weaknesses and identify patterns in the transactions that point to fraud and error will be studied. Finally, the student will learn how to analyze both financial and operational data by designing financial models and dashboards, making reasonable financial forecasts, and monitoring operational performance. In this course students will have exposure to several data analytics tools.
Course Content
1. Use the DuPont Model and ratio analysis to analyze financial statements
- Read financial statements properly
- Adjust financial statements
- Analyze financial statements
- Communicate results
2. Apply the Data Analytics Process
- Identify a quesion
- Manage data
- Perform test plan
- Address and refine results
- Communicate insights
- Track outcomes
3. Financial Modeling
- Design and create an interactive financial model using an appropriate data analytics tool
- Design the layout of the model clearly and logically
- Create clearly defined inputs and assumption sections
- Build powerful scenarios
- Incorporate all related schedules
4. Design and create a Dashboard using an appropriate data analytics tool
- Evaluate existing data
- Define and calculate Key Performance Indicators (KPI)
- Design charts and graphs for KPI
- Present dashboard
Learning Activities
In-class lectures in a computer lab and/or on-line
Means of Assessment
Assessment will be based on course objectives and will be carried out in accordance with the Douglas College Evaluation Policy.
Projects: A minimum of two separate group data analytics projects 40% - 50%
A minimum of two separate individual data analytics projects 30%
(a mininum of 10% should be assigned to each project,
no project should be assigned more than 40%)
Final Exam 20%-30%
Students may conduct research as part of their coursework in this class. Instructors for the course are responsible for ensuring that student research projects comply with College policies on ethical conduct for research involving humans, which can require obtaining Informed Consent from participants and getting the approval of the Douglas College Research Ethics Board prior to conducting the research.
Learning Outcomes
Upon completion of this course, the successful sturdent will be able to:
- Analyze financial statements using data analytics
- Use information from automated data capture to form and analyze financial statements
- Access, examine and integrate different data files
- Analyze data patterns, identify discrepancies and extract unusual items
- Examine financial data for existence of misrepresentations in areas such as matched or unmatched records, duplication and limit violation
- Design and construct financial models
- Create dashboards
- Apply the data analytics process to communucate insights and track outcomes
Textbook Materials
Data Analytics for Accounting, Richardson, Teeter, Terrall, latest international edition
and/or
other textbook(s) and/or material approved by the department