ACCT 3337: Introduction to Data Analytics in Accounting
1. What Is ACCT 3337 About?
ACCT 3337: Introduction to Data Analytics in Accounting is an accounting course focused on using data to answer accounting and business questions. Students learn the data analytics process from preparing data through analyzing it and communicating the results.
The course combines accounting concepts with hands-on work in data analytics tools. Students work with accounting and business data, learn different types of analytics, create visualizations, and use their findings to support recommendations.
Course information
| Detail | Information |
|---|---|
| Course | ACCT 3337 |
| Title | Introduction to Data Analytics in Accounting |
| University | University of Houston |
| Semester | Fall 2026 |
| Format | On-campus, Monday and Wednesday, 11:30 AM–1:00 PM |
| Prerequisites | Not specified in the provided syllabus |
2. What Do Students Learn?
By the end of ACCT 3337, students are expected to be able to:
- Explain how data analytics affects accounting work and why it matters in accounting and business.
- Prepare data for analysis using extraction, transformation, and loading (ETL) techniques.
- Use different data analytics tools to perform analysis.
- Create visualizations that communicate useful insights from accounting and business data.
- Use analytical results to support recommendations.
The course also develops students’ ability to analyze and interpret financial and other information, identify accounting problems, and recognize ethical and regulatory issues.
3. ACCT 3337 Course Topics
The course follows the data analytics process from identifying a question to communicating the results.
Asking the Question
Students begin by considering how data analytics can be used to address accounting questions and business problems.
Mastering the Data
Students learn about accounting data, different data types, and how to prepare data for analysis.
Performing the Analysis
The course introduces four major types of analytics:
- Descriptive analytics — What happened?
- Diagnostic analytics — Why did it happen?
- Predictive analytics — What might happen next?
- Prescriptive analytics — What should we do?
Sharing the Story
Students learn how to communicate analytical findings clearly and use them to support business recommendations.
Artificial Intelligence
Fundamentals of artificial intelligence are integrated into the course throughout the semester.
4. How Is the Course Structured?
ACCT 3337 combines lectures, discussions, in-class activities, assignments, labs, exams, and a capstone project.
The course progresses through four main stages:
Ask the Question → Master the Data → Perform the Analysis → Share the Story
Students first learn how analytics can address accounting questions, then move into data preparation and analysis before learning how to communicate their findings.
The course also incorporates artificial intelligence content and concludes with capstone project work.
Classes meet twice a week on campus, and the syllabus describes the sessions as including lectures, interactive activities, breakout discussions, and self-paced activities.
5. What Assignments Are in ACCT 3337?
Assignments
Students complete assignments through platforms including McGraw Hill Connect, EYARC, and Canvas. These assignments provide practice with course concepts and analytical skills.
Labs
Labs are a major part of the course and give students opportunities to work directly with analytics tools. Some Excel, Tableau, and Power BI labs are completed during class.
Engagement Activities
Engagement activities include activities such as:
- Syllabus quiz
- Academic honesty certification
- LockDown Browser practice exam
- In-class attendance and activities
Exams
Students take:
- One midterm exam
- One comprehensive final exam
Exams cover lectures, discussions, labs, selected textbook chapters, and other course materials.
6. ACCT 3337 Grading Breakdown
| Course Component | Weight |
|---|---|
| Assignments — Connect, EYARC, Canvas | 15% |
| Labs | 35% |
| Midterm Exam | 15% |
| Final Exam | 15% |
| Engagement Activities | 5% |
| Capstone Project | 15% |
| Total | 100% |
Labs are the largest individual component of the course grade, accounting for 35%.
Grading Scale
| Grade | Percentage |
|---|---|
| A | 93–100 |
| A− | 90–92.99 |
| B+ | 87–89.99 |
| B | 83–86.99 |
| B− | 80–82.99 |
| C+ | 77–79.99 |
| C | 73–76.99 |
| C− | 70–72.99 |
| D+ | 67–69.99 |
| D | 63–66.99 |
| D− | 60–62.99 |
| F | Below 60 |
7. What Is the Final Project?
The course includes a Capstone Project, worth 15% of the final grade.
The capstone portion of the course focuses on applying the analytics process, with dedicated class sessions covering capstone projects using the AMPS Model.
The provided syllabus does not give enough detail about the specific project requirements to describe the final deliverable further without adding assumptions.
8. What Readings and Materials Are Used?
The required textbook is:
Introduction to Data Analytics for Accounting
Authors: Richardson, Terrell, and Teeter
Edition: 2nd
Publisher: McGraw Hill
The course requires the Connect Online Access edition because assignments are included through Connect.
Main software and platforms
Students work with:
- Microsoft Excel
- Power BI
- Tableau
- McGraw Hill Connect
- Canvas
- Microsoft Teams
- OneDrive
Power BI and Tableau are used for data visualization and analytics activities.
9. ACCT 3337 Weekly Topics
The Fall 2026 schedule progresses through the following topics:
| Period | Main Topic |
|---|---|
| Aug. 24 | Introduction and syllabus |
| Aug. 26–31 | Asking the Question: Using Data Analytics for Accounting Questions |
| Sept. 2–9 | Introduction to Accounting Data |
| Sept. 14–16 | Data Types Used in Accounting |
| Sept. 21–23 | Preparing Data for Analysis |
| Sept. 28–30 | Artificial Intelligence |
| Oct. 5 | Midterm |
| Oct. 7–12 | Types of Data Analytics |
| Oct. 14–19 | Descriptive Analytics |
| Oct. 21–26 | Diagnostic Analytics |
| Oct. 28–Nov. 2 | Predictive Analytics |
| Nov. 4–9 | Prescriptive Analytics |
| Nov. 11–16 | Sharing Analytics Results |
| Nov. 18 | Artificial Intelligence |
| Nov. 23–Dec. 2 | Capstone Projects using the AMPS Model |
The syllabus notes that the detailed schedule and specific assignment due dates are provided through Canvas and that the schedule may change.
10. What Skills Can Students Develop?
ACCT 3337 provides practical experience with:
- Data preparation
- ETL techniques
- Accounting data analysis
- Descriptive, diagnostic, predictive, and prescriptive analytics
- Excel-based analysis
- Power BI
- Tableau
- Data visualization
- Communicating analytical findings
- Using data to support recommendations
- Identifying accounting and business questions
- Interpreting financial and other business information
The course therefore combines accounting knowledge, analytical thinking, technology skills, and business communication.
11. What Should Students Expect?
Expect hands-on analytics work
This is not solely a lecture-based accounting course. Labs and assignments require students to work with data and analytics software.
Expect regular work outside class
The syllabus estimates approximately 6–9 hours of work outside class each week, including reading, assignments, and reviewing course materials. Weekly work generally includes reading chapters, completing chapter assignments, labs, and participating in class activities.
Expect to use several technology tools
Students need access to a laptop and software including Excel, Power BI, and Tableau. The syllabus states that the assignments are designed around Windows-based versions of Excel, Power BI, and Tableau, which may create additional considerations for students using Macs.
Expect two exams
There is a midterm and a comprehensive final exam. The syllabus states that exams are completed in one sitting using LockDown Browser.
Expect consistent practice
Because labs make up 35% of the course grade, staying current with hands-on assignments is particularly important. The syllabus allows some late submissions for assignments and labs within specified Connect availability periods, but students are encouraged to complete work by the posted deadlines.
12. ACCT 3337 at a Glance
| Category | Summary |
|---|---|
| Course | ACCT 3337: Introduction to Data Analytics in Accounting |
| University | University of Houston |
| Semester | Fall 2026 |
| Format | On-campus, Monday & Wednesday |
| Main focus | Data analytics applied to accounting and business |
| Required textbook | Introduction to Data Analytics for Accounting, 2nd ed. |
| Major tools | Excel, Power BI, Tableau |
| Major assignments | Assignments, labs, exams, engagement activities, capstone project |
| Midterm | 15% |
| Final exam | 15% |
| Labs | 35% |
| Capstone | 15% |
| Outside-class workload | Approximately 6–9 hours per week |
| Final project | Capstone Project using the AMPS Model |
Final Takeaway
ACCT 3337 introduces students to data analytics as it applies to accounting and business. The course moves through the full analytics process: identifying questions, preparing accounting data, performing different types of analysis, and communicating the resulting insights.
Students should expect a practical, technology-focused course involving Excel, Power BI, Tableau, data preparation, analytics labs, exams, and a capstone project. With labs accounting for 35% of the final grade and an estimated 6–9 hours of work outside class each week, the course requires regular hands-on practice rather than relying primarily on exam preparation.
