This course introduces how accountants use data to ask better questions, clean messy evidence, analyze patterns, build visual stories, and make informed professional judgments.
Two Tuesday/Thursday sections in Jepson Center 012.
Joo Ha, PhD. Office Hours: Tuesdays and Thursdays, 10:20 AM–12:20 PM. Office: Jepson 214.
Open by framing this as a practical, hands-on course. Emphasize that students do not need to be technology experts at the start; the goal is steady growth and confidence.
Key takeaway: accounting analytics is not just software. It is disciplined thinking supported by tools.
What is one accounting decision that could become better with cleaner data?
Accounting work increasingly involves large datasets, dashboards, automated workflows, AI-assisted analysis, and communication with nontechnical decision makers.
Transactions, ledgers, subledgers, invoices, customer data, budgets, and public filings.
Trends, anomalies, risks, performance signals, and better business questions.
Dashboards, recommendations, controls, process improvements, and ethical judgment.
Use this slide to position students as future professionals who must verify, explain, and defend analyses. Tools help, but judgment matters.
Best answer theme: judgment is needed when defining the problem, validating data, interpreting outputs, and deciding what action is appropriate.
Where do you think accounting judgment is still needed even when the tool produces an answer?
You will study the role of big data, data science, and analytics in business, with practice in visualization, statistical methods, analytical models, and software tools.
Vocabulary and principles for accounting analytics.
Descriptive, diagnostic, predictive, and model-based analysis.
Excel, Power BI, Alteryx, Power Query, and AI-supported professional workflows.
Clear dashboards, documented analyses, and decision-focused interpretation.
Clarify that this is not a programming-only course. It is an applied accounting analytics course centered on professional use cases.
Key takeaway: students will build a bridge from accounting knowledge to practical analytics tools.
Which part feels most familiar to you: Excel, visualization, statistics, automation, or AI?
The course goals focus on accounting analytics foundations, practical quantitative skills, ethics and professional judgment, and adaptability to emerging technology and AI.
Understand core analytics concepts and why they matter in accounting.
Use Excel, Power BI, Alteryx, and AI-supported workflows to solve accounting problems.
Connect analytics choices to professional and societal implications.
Build strategies for lifelong learning with emerging technologies and AI.
Ask students to connect course goals to career readiness. The ethics/AI goal should be emphasized as a professional skill, not a side topic.
Correct: Integrating ethics and professional judgment. AI and automation require verification, documentation, privacy awareness, and skepticism.
What does “responsible use” of a new technology mean in an accounting context?
You will define analytics principles, apply models, clean and visualize data, evaluate ethics, detect AI errors, and adapt to emerging technologies.
Use correct analytics vocabulary in accounting contexts.
Interpret models using real accounting data.
Clean, transform, and visualize datasets.
Assess ethics, bias, hallucinations, and professional implications.
Explore new technologies strategically and responsibly.
Explain what you did, why you did it, and how you verified it.
Make the outcomes concrete by connecting them to future tasks: audits, advisory work, financial reporting, FP&A, controls, and process automation.
Strong responses should identify both a technical skill and a judgment/communication skill.
Which outcome seems most important for your future accounting career?
This course uses tools as professional instruments. You will learn when to use each tool, what it is good at, and how to explain its output.
Data modeling, DAX measures, interactive dashboards, and storytelling.
Repeatable workflows for cleaning, joining, transforming, and documenting data.
Extract, transform, clean, and aggregate data before analysis.
Brainstorming, code ideas, prompt practice, and checking communication. Always verify.
Emphasize tool choice: same dataset, different purpose. Power BI tells interactive stories, Power Query prepares data inside the Microsoft workflow, and Alteryx is ideal when students need a visual workflow that can be rerun and documented. Walk students through this example: import a CSV, keep needed columns, cleanse text and nulls, filter bad rows, then output a clean file.
Correct: Alteryx. It is particularly helpful for repeatable, documented preparation workflows such as cleansing, joins, and transformations. This example shows a simple accounting preparation flow from raw file to clean output.
If several tools can clean or summarize data, why might an accountant still choose Power BI, Power Query, or Alteryx for different tasks?
In-class activities are guided practice. Labs are independent or small-group application where students drive the problem-solving process.
We work through new concepts together step by step.
You apply what you learned and create deliverables.
Some tasks are intentionally difficult to build confidence and persistence.
Use demos, tutorials, classmates, TA support, and office hours.
Set expectations that labs are completion/engagement focused but still require authentic effort. Encourage students to document what they tried.
Strong process: reread task, inspect data, check examples, ask a classmate to compare approach, use AI as a coach, then ask the instructor with specific evidence.
When a lab feels difficult, what should you try before concluding that you are stuck?
Your course grade is based on assignments, labs, two midterm exams, and a group project. The largest categories reward practical analytical work and application.
5% through EY Experience platform.
20% in-class, completion and engagement focused.
50% total across two in-class midterms.
25% in groups of 2–3 with peer review and documented AI use.
Remind students that labs are not “optional practice.” They are graded and help prepare students for exams and the project.
Correct: Midterm exams. Together, the two midterms account for 50% of the final grade.
What habits would help you succeed when the course includes both exams and hands-on labs?
EY Experience assignments must be submitted by the published deadline because the platform automatically closes. Labs are due by the end of the session. There is no extra credit and grades are not rounded.
Submit before the platform closes. Late submissions cannot be accepted.
Submit by the end of class. Completion and engagement are central.
Focus effort on required work and stated outcomes.
The grading scale is applied as stated.
This is a good slide to emphasize fairness and consistent standards. Encourage calendar reminders and early communication.
Correct: the platform automatically closes submissions, so students need to submit before the published deadline.
What is one practical system you can use to avoid missing platform-based deadlines?
AI tools may be used for brainstorming, code suggestions, sample datasets, visualization ideas, and writing clarity. Final work must demonstrate your own thinking and must document AI use.
Brainstorm, outline, refine code snippets, generate sample datasets, explore visuals, check clarity.
Identify the tool/version, include exact prompts, and explain how you verified or refined output.
Submitting AI-generated work as your own.
AI may hallucinate, reflect bias, or mishandle confidential information.
Frame AI documentation as professional documentation, not punishment. Students should learn to use AI transparently and skeptically.
Correct: Students must identify the tool/version, include exact prompts, and explain how they verified or refined the AI output.
How is using AI similar to and different from asking a classmate for help?
Bring a laptop, use class tools during class activities, keep non-course tabs closed, place phones face down, and prepare for device-free moments when discussion requires attention.
Use the lab monitor, keyboard, mouse, and wired network when available.
Excel, Power BI, and Alteryx will be available without purchase.
Power BI Desktop and Alteryx Designer are Windows-based.
No off-task tabs, phones face down, and earbuds removed at the start of class.
Point students to Canvas software instructions. Make the device policy about community learning rather than punishment.
Correct: Power BI and Alteryx are Windows-based, so Mac users may need the Business Virtual Desktop, dual boot, or emulator access.
How can a technology policy protect both your learning and nearby classmates’ learning?
This course depends on collaboration, respect, regular attendance, curiosity, kindness, and shared responsibility for the learning environment.
Bring curiosity and contribute to small-group work.
Listen actively and value each other’s ideas.
Review posted materials and consult classmates if you miss class.
Help create a space where everyone feels welcomed and supported.
Use this slide to establish norms before the first group activity. Ask students to name behaviors that help or hurt collaboration.
Strong examples: sharing work, documenting steps, explaining thinking, asking specific questions, keeping devices on task, and giving respectful feedback.
What does professionalism look like during a group data lab?
The opening weeks build core spreadsheet skills: basic functions, conditional logic, lookups, PivotTables, descriptive statistics, regression, and Midterm 1.
Course intro, syllabus, basic Excel functions, conditional logic.
Excel lookup and Lab 1: Excel Foundations.
PivotTables and Lab 2: Pivot Table.
Excel statistics demo, stat game, descriptive and regression analysis.
Midterm 1 after Excel foundations. This checks basic functions, logical functions, and lookups.
Students may feel the first five weeks move quickly. Emphasize that Midterm 1 is the first major checkpoint and encourage students to build a practice routine after each class.
Key takeaway: early Excel skills become the foundation for later Power Query, Power BI, and Alteryx work. Midterm 1 checks basic functions, logical functions, and lookups.
Which early skill do you think will be most useful for accounting work: formulas, lookup, PivotTables, or regression?
The middle of the course moves from spreadsheet analysis to data cleaning, transformation, modeling, dashboards, and the first Alteryx introduction.
Excel What-If Analysis, Solver, and Scenario Analysis.
Power Query: extract, transform, load, aggregate.
Midterm 2 after Power Query data cleaning. This is a major checkpoint for statistics, predictive tools, and Power Query skills.
Power BI data modeling, DAX, and visualization.
Data modeling lab and Alteryx introduction.
Connect this sequence to the analytics workflow: define, clean, model, visualize, interpret. Emphasize that Midterm 2 is the major checkpoint for statistics, Excel predictive tools, and Power Query before the course moves deeper into Power BI and Alteryx.
Key takeaway: Week 8 matters. Midterm 2 checks whether students can apply statistics, predictive Excel tools, and Power Query concepts before later dashboard and workflow work.
Why does data cleaning come before dashboard design?
The final part emphasizes Alteryx cleansing, joins, transformations, project work, credential preparation, and final project submission.
Alteryx data cleansing and Lab 7: Alteryx Foundations.
Alteryx joins and project work start.
Online class: Alteryx Transform. Thanksgiving holiday on Thursday.
Alteryx join/transform demo and applied workflow lab if time allows.
Project work, Alteryx credential preparation, and course wrap-up.
Project due: Section 01 Dec 15 by 5:30 PM; Section 02 Dec 17 by 12:30 PM.
Highlight that the project begins before the last week and that the final project due dates are firm. Encourage teams to organize files, roles, and AI documentation from the beginning.
Professional deliverables are clear, accurate, documented, visually interpretable, and connected to the business question. The final project due date should be treated as a professional client deadline.
What makes a project deliverable professional rather than just complete?
The group project asks students to clean data and develop visualizations that effectively communicate insights using tools learned in class.
Groups of 2–3. Individual work is not permitted.
Clean data and create visualizations that communicate insights.
Peer review helps evaluate contributions.
AI may support brainstorming, code suggestions, and design, but must be documented and cited.
Encourage teams to avoid splitting the project into isolated pieces where no one understands the full story. Require shared checkpoints.
Correct: AI use must be documented and cited. Strong teamwork includes shared understanding, role clarity, and peer accountability.
What is a fair way for a project team to divide work while still understanding the whole project?
The syllabus emphasizes academic integrity, AI verification, privacy, accessibility, respect, non-discrimination, and support for an inclusive learning environment.
Honesty matters in your own work and AI-assisted work.
Do not share confidential, personal, or sensitive information with AI tools.
Verify AI outputs, references, calculations, and assumptions.
Support a learning community where everyone can participate fully.
Connect policies to accounting professionalism: client data, employee data, financial reporting, audit evidence, and reputational risk.
Strong answer: accounting data can affect people, firms, investors, and compliance decisions. Errors or privacy failures can have serious consequences.
Why are privacy and accuracy especially important in accounting analytics?
Use the first week to confirm access, understand the schedule, review Canvas materials, and prepare your technology for hands-on work.
Know your section time, classroom, and major due dates.
Add EY assignments, labs, midterms, and project due dates.
Bring laptop and check Excel, virtual desktop, Power BI and Alteryx instructions.
Understand AI documentation, late platform deadlines, and technology expectations.
Use office hours, TA support, classmates, and instructor communication.
Close with encouragement. Ask students to choose one concrete action they will complete before the next class.
Key takeaway: early setup prevents many avoidable problems later in the semester.
What is one action you can take this week to reduce stress later in the semester?