Curriculum Guideline

Data Analytics for Managers

Effective Date:
Course
Discontinued
No
Course Code
ACCT 3880
Descriptive
Data Analytics for Managers
Department
Accounting
Faculty
Commerce & Business Administration
Credits
3.00
Start Date
End Term
Not Specified
PLAR
No
Semester Length
15 Weeks
Max Class Size
35
Contact Hours

Lecture/Seminar: 2 hours lecture & 2 hours seminar

OR

Hybrid: Alternating weeks of

  • 2 hours lecture and 2 hours seminar
  • 4 hours online
Method Of Instruction
Lecture
Seminar
Online
Hybrid
Methods Of Instruction

In-class lectures in a computer lab and/or on-line

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 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
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%

To pass this course, students must obtain a minimum of 50% on invigilated assessments, with the 50% calculated on a weighted average basis.

Invigilated assessments include, in-class quizzes, in-class tests, midterm exam(s) and the final exam.

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.

Textbook Materials

Data Analytics for Accounting, Richardson, Teeter, Terrall, latest international edition

and/or

other textbook(s) and/or material approved by the department 

Prerequisites

(ACCT 1210 OR ACCT 1235 OR ACCT 3008) and (CSIS 1190 or CSIS 2200)

Or currently active in

  • PDD Accounting
  • PDD Data Analytics

Or (ACCT 1210 OR ACCT 1235) and currently active in

  • PBD Accounting and Finance
  • PDD Accounting Studies
  • PBD Accounting

Or permission of the instructor

A minimum grade of C is required in all courses.

Equivalencies

Courses listed here are equivalent to this course and cannot be taken for further credit:

  • No equivalency courses