Computerized Accounting and Data Analytics
Overview
This course will introduce students to the concepts and practices of computerized accounting and data analytics. This course will build on and apply previously acquired accounting and computer skills. In addition to spreadsheet techniques and applications using Excel, the student will learn to analyze financial statements using lesser-known techniques (e.g., The DuPont Model) and design financial models and dashboards using data analytics tools.
1. Review basic spreadsheet and Microsoft Excel concepts.
- Basic: worksheet environment, navigation, formatting, entering data/formulas, editing
- Intermediate: cell references, formulas, what if" analysis, charts and graphs
- Advanced: financial functions, specialized functions, table lookup, pivot tables and pivot charts, multi-sheet workbooks, importing from other sources, data forms, dashboards, and templates
2. Review and apply basic accounting concepts (e.g., balance sheet, income statement).
- Reading and interpreting financial statements
- Users of the financial statements and their needs
3. Apply Microsoft Excel concepts to accounting (e.g., financial statements, financial analyses, and supporting schedules), including a complete accounting worksheet and set of financial statements.
4. Apply the Data Analytics Process.
- Identify a question
- Manage data
- Perform test plan
- Address and refine results
- Communicate insights
- Track outcomes
5. Use the DuPont Model and ratio analysis to analyze financial statements.
- Read financial statements properly
- Adjust financial statements
- Analyze financial statements
- Communicate results
6. Design and create a visualization/dashboard using an appropriate data analytics tool.
- Evaluate existing data
- Create visualization/dashboard for key ratios / to cater to different users of the financial statements
- Define and calculate key ratios for a business owner
- Design charts and graphs for different users of the financial statements
- Present dashboard
7. Interpret and analyze pre-generated reports (e.g., developed through generative artificial intelligence tools) on a company’s financial health and assess its accuracy.
Lecture and/or seminar
All methods of instruction apply to in class, hybrid and/or online modes of learning.
Assessment will be based on course objectives and will be carried out in accordance with the Douglas College Evaluation Policy. An evaluation schedule is presented at the beginning of the course. This is a graded course.
Group Project(s):
- A minimum of one group data analytics or Microsoft Excel project 30%
Individual Activities:
- In-class activities, homework assignments, and data analytics/Microsoft Excel projects 30%
- Final Exam 40%
Students must write all examinations in order to obtain credit for the course.
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.
At the end of the course, the successful student should be able to:
- Create finance- and accounting-related spreadsheets and charts by enhancing previously acquired finance, accounting, and computer skills
- 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
- Create, interpret, and analyze dashboards
- Apply the data analytics process to communicate insights and track outcomes
Richardson, Teeter, Terrall. Data Analytics for Accounting. Latest International Edition
and/or
Storytelling with Data: A Data Visualization Guide for Business Professionals. Knaflic. Latest Edition
and/or
Other textbook(s) and/or material approved by the department
Requisites
Course Guidelines
Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester / year of the course, consider the previous version as the applicable version.
Course Transfers
These are for current course guidelines only. For a full list of archived courses please see https://www.bctransferguide.ca
| Institution | Transfer details for ACCT 1220 |
|---|---|
| Langara College (LANG) | LANG FMGT 2308 (3) |
| Simon Fraser University (SFU) | No credit |
| Thompson Rivers University (TRU) | TRU ACCT 2280 (3) |
| University Canada West (UCW) | UCW ACCT 1XX (3) |
| University of British Columbia - Okanagan (UBCO) | No credit |
| University of Northern BC (UNBC) | No credit |
| University of the Fraser Valley (UFV) | UFV BUS 1XX (3) |
| University of Victoria (UVIC) | No credit |