class: center, middle, inverse, title-slide .title[ # Teaching Business Students Data Visualization and Problem-Solving Skills Using Tableau, R, and Python ] .subtitle[ ## ACME Marketing Conference 2026 ] .author[ ### ‘Jimmy’ Zhenning Xu, Ph.D., Di Wu, Ph.D., Ji Li,Ph.D., and John Tarjan, Ph.D. ] .institute[ ### California State University Bakersfield ] .date[ ### 2026-03-20 ] --- --- class: inverse, center, middle # West Valley Fresh Distribution Company ## Using Data Analytics to Explore Opportunities in the Hass Avocado Markets ### A Teaching Note --- ### Sample Exercises - Exploratory analysis 1 - https://rpubs.com/utjimmyx/avocado1 - Exploratory analysis 2 - https://rpubs.com/utjimmyx/avocadoefa - Predictive analysis 1 - https://rpubs.com/utjimmyx/avocado_sales1 - Predictive analysis 2 - https://rpubs.com/utjimmyx/retailpricing - Dashboard 1 - https://rpubs.com/utjimmyx/dashboard5 - Dashboard 2 - https://rpubs.com/utjimmyx/Avocadosales - Sample **Python** workbook - https://github.com/utjimmyx/4000/blob/main/avocado.ipynb - Sample **Tableau** dashboard - https://public.tableau.com/app/profile/zhenning.xu/viz/Salesdashboard_17052744008670/Sheet8 ### These exercises target managers who want to demonstrate analytics, visualization, critical thinking, and communication skills --- # Introduction > *"Data-driven decision making requires addressing several issues, including **data governance**, **data analytics**, **data visualization**, and **interpretation of results**. This process is essential to businesses and organizations in making optimal decisions and staying competitive."* .pull-left[ ### What This Case Does - Uses **real business transaction data** from the Hass Avocado Board - Simulates business plans requiring data analytics tasks - Questions are **typical of those found in the business world** - Targets managers who must demonstrate analytics, visualization, critical thinking, and communication skills ] .pull-right[ ### Keywords `Data Analytics` · `Data Visualization` `Profitability Analysis` · `Variable Costs` `Fixed Costs` ### Tools Featured | Tool | Role | |------|------| | **Tableau** | Dashboards & interactive maps | | **R** | Statistical analysis & visualization | | **Python** | Data wrangling & automation | | **Excel** | PivotTables & baseline analysis | ] --- # The Problem: Why This Case Is Needed .pull-left[ ### The Industry Reality - Data analytics is listed as a **core qualification** in hiring decisions across nearly all business positions - Managers are expected to employ **advanced analytics tools** in teamwork environments - Managers play essential roles in **critical decision making**: business plans, identifying opportunities and risks ### The Education Gap - Many programs **lag behind** in integrating data analytics and visualization into curricula - **Less than 5%** of published cases in the IMA Education Case Journal (IECJ) relate to data analytics topics - There is an *urgent need* to address this shortfall ] .pull-right[ ### Professional & Regulatory Drivers **American Marketing Association** – AI-powered **analytics** tools enable businesses to predict customer behavior, automate content optimization, and generate actionable insights at unprecedented speeds. Marketers should focus on integrating **AI** into their analytics stack to enhance efficiency and improve customer targeting. **AICPA & NASBA** – CPA Evolution Model Curriculum → Advanced data analytics requirements **across all CPA exam sections** **AACSB** – Accreditation Standard → *"Integrate statistical techniques, clustering, data management, modeling, text analysis and information technologies within the curricula"* These joint efforts prepare accounting graduates for **higher competency** in technology, analytics, visualization, and automation. ] --- # The Case: West Valley Fresh Distribution .pull-left[ ### Company Scenario West Valley Fresh Distribution is a company interested in **entering the Hass avocado market** The case simulates typical business operations around a potential **special order of 20,000 organic avocados** ### The Data - Downloaded from the **Hass Avocado Board** (hassavocadoboard.com) - **Weekly sales data** for 41 U.S. metropolitan cities - Covers **2019–2022** - Variables include: sales volume in units, selling price, avocado type (organic vs. conventional), region ] .pull-right[ ### Analytics Elements Covered 1. **Data collection & exploration** 2. **Descriptive analysis** — statistics and calculations 3. **Regression analysis** — price vs. volume relationships 4. **Time series analysis** — seasonal pattern recognition 5. **Sensitivity analysis** — risk and opportunity assessment ### Case Origins Developed jointly by **university professors** and **local business professionals** in response to needs identified by AMA, IMA, AICPA, and AACSB. ] --- # Intended Course & Audience .pull-left[ ### Target Course - **Managerial Accounting, Business/Marketing Analytics, etc.** - Can be adapted for **MBA-level** or other courses - Suitable as a lab exercise or a regular case assignment ### Prerequisite Skills Assumed - Foundational to intermediate Excel skills (PivotTables, charts) - Basic understanding of managerial concepts (CVP, cost analysis) - Some exposure to critical thinking in a business context ] .pull-right[ ### The Managerial Perspective The case follows a structured workflow: ``` Define the business problem ↓ Extract & format data ↓ Explore data using technology ↓ Apply analytics & visualization ↓ Inform managerial decisions ``` *Data, case description, and suggested questions are all based on real data.* ] --- class: inverse, center, middle # Four Student Learning Objectives --- # Learning Objective 1 ## Managerial Approach to Regional Sales & Profitability Students are tasked with **defining business problems** in alignment with the case and applying managerial accounting knowledge to formulate solutions. - Emphasizes **cost analysis and profitability assessment** - Offers a pathway to glean insights into **regional sales** for potential market opportunities - Questions are designed in a **systematic format** to provide structure --- # Learning Objective 2 ## Statistics & Regression Analysis — Pricing and Sales Insights Students engage in the **entire data analysis process**: from extracting and formatting raw data to exploring advanced techniques. - **Regression analysis** — understanding price-volume relationships - **Sorting and ranking** — identifying top-performing cities - **Pattern and trend identification** — recognizing market dynamics --- # Learning Objective 3 ## Data Visualization to Inform Managerial Decisions Students use charts, graphs, and maps to **present results of data analytics** and communicate with decision-makers. - Construct **compelling narratives** — facilitating effective storytelling - Represent **correlations and patterns** (e.g., seasonal effects) - Tools: **Excel** and **Tableau** used for all questions but one > *"This learning objective is pivotal, reinforcing the skills necessary to harness the power of data visualization to facilitate managerial decision-making."* --- # Learning Objective 4 ## Identifying Market Risks & Opportunities Through Sensitivity Analysis Students engage in **sensitivity analysis** — a crucial practice for businesses involved in budgeting, planning, risk management, and opportunity exploration. - Questions **6, 7, and 8** require sensitivity analysis - Students examine **three cost scenarios**: Low Costs, Medium Costs, High Costs - Connects analytics directly to **business decision making under uncertainty** --- class: inverse, center, middle # Questions & Solutions ### Basic Questions (Q1–Q4) · Advanced Questions (Q5–Q8) --- # Q1: Largest Avocado Sales by City & Year **Which city has the largest sales of organic and conventional Hass avocados (dollar amount), for each year (2019–2022)?** .pull-left[ ### How to Solve - Calculate **weekly sales = Price × Volume** - Aggregate to annual totals using **PivotTable (Excel)** or **Dashboard (Tableau)** - Sort by revenue, filter by avocado type and year ### Key Instructional Tip > Instructors can guide students through one year (e.g., 2019) and task them with completing the remaining years. ] .pull-right[ ### Table 1. Cities with Largest Hass Avocado Sales <table class="table table-striped table-hover table-bordered" style="font-size: 15px; color: black; width: auto !important; margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:center;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> Year </th> <th style="text-align:center;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> Conventional </th> <th style="text-align:center;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> Organic </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;"> 2019 </td> <td style="text-align:center;"> Los Angeles </td> <td style="text-align:center;"> New York </td> </tr> <tr> <td style="text-align:center;"> 2020 </td> <td style="text-align:center;"> Los Angeles </td> <td style="text-align:center;"> New York </td> </tr> <tr> <td style="text-align:center;"> 2021 </td> <td style="text-align:center;"> Los Angeles </td> <td style="text-align:center;"> Los Angeles </td> </tr> <tr> <td style="text-align:center;"> 2022 </td> <td style="text-align:center;"> Los Angeles </td> <td style="text-align:center;"> Los Angeles </td> </tr> </tbody> </table> **Visualization using Tableau, R, or Python** - U.S. map shows largest sales concentrated on the **west and east coasts**, implying more market opportunities in these regions. ] --- # Q2: Largest Organic Market Share by City & Year **Which city has the largest percentage of organic Hass avocado sales out of total Hass avocado sales, for each year (2019–2022)?** .pull-left[ ### How to Solve - Compute organic revenue and total revenue per city - Calculate: `% Organic = Organic Revenue / Total Revenue` - Sort descending to identify leader per year ### Key Finding **Seattle** leads every year, but the percentage is **declining** — signaling a maturing market or shifting consumer preferences. ] .pull-right[ ### Table 2. Cities with Largest % Organic Sales <table class="table table-striped table-hover table-bordered" style="font-size: 15px; color: black; width: auto !important; margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:center;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> Year </th> <th style="text-align:center;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> City </th> <th style="text-align:center;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> % Organic Sales </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;"> 2019 </td> <td style="text-align:center;"> Seattle </td> <td style="text-align:center;"> 15.93% </td> </tr> <tr> <td style="text-align:center;"> 2020 </td> <td style="text-align:center;"> Seattle </td> <td style="text-align:center;"> 13.52% </td> </tr> <tr> <td style="text-align:center;"> 2021 </td> <td style="text-align:center;"> Seattle </td> <td style="text-align:center;"> 13.03% </td> </tr> <tr> <td style="text-align:center;"> 2022 </td> <td style="text-align:center;"> Seattle </td> <td style="text-align:center;"> 11.95% </td> </tr> </tbody> </table> > *"Tableau is as effective as Excel for this type of analysis, with the added advantage of providing enhanced interactivity features for viewers."* ] --- # Q3: Price Volatility by City & Avocado Type **Which city has the largest volatility in average weekly prices — for conventional and organic avocados — over 2019–2022?** .pull-left[ ### How to Solve - Measure volatility using **standard deviation** of weekly prices - Use Excel PivotTable or Tableau dashboard to compute STDEV by city and type - Sort descending to identify the most volatile markets ### Results from the Document - **Chicago** → largest price volatility for **conventional** avocados - **Seattle** → largest price volatility for **organic** avocados - Seattle has large volatility in **both** markets ### Instructional Tip Initiate a brief discussion on measuring volatility before students start — standard deviation is one method students can explore. ] .pull-right[ <img src="teaching_dataviz_presentation_final_files/figure-html/q3-chart-1.png" width="396" style="display: block; margin: auto;" /> ] --- .pull-left[ ``` ## ## Call: ## lm(formula = Total_Volume ~ AveragePrice, data = avocado) ## ## Residuals: ## Min 1Q Median 3Q Max ## -813191 -278360 -103248 121529 4919221 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 1115069 14653 76.10 <2e-16 *** ## AveragePrice -558319 9980 -55.95 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 479800 on 13876 degrees of freedom ## Multiple R-squared: 0.184, Adjusted R-squared: 0.184 ## F-statistic: 3130 on 1 and 13876 DF, p-value: < 2.2e-16 ``` ] .pull-right[ <img src="teaching_dataviz_presentation_final_files/figure-html/q4-chart-1.png" width="576" style="display: block; margin: auto;" /> ] --- # Q4: Does Price Affect Sales Volume? .pull-left[ ### Approach Apply **simple linear regression**: `$$\text{Sales Volume} = \beta_0 + \beta_1 \times \text{Price} + \varepsilon$$` **Result from the document:** `$$\widehat{\text{Sales Volume}} = 1{,}179{,}544.8 - 628{,}686.6 \times \text{Price}$$` ### Interpretation - The **coefficient is negative** → higher prices lead to lower sales volume - A **$0.01 increase** in price → approximately **6,287 fewer units** sold - **Excel, Tableau, R, and Python** all produce identical results; However, **R and Python (with the assistance of AI or AI agents)** offer more benefits for reproducible analyses and automation. ] .pull-right[ ### R Code — Running the Regression ```r # Load data avocado <- read_csv("avocado.csv") |> mutate(Date = as.Date(date), Revenue = AveragePrice * `Total Volume`) # Fit simple linear regression model <- lm(`Total Volume` ~ AveragePrice, data = avocado) summary(model) # Visualize with trendline library(ggplot2) ggplot(avocado, aes(x = AveragePrice, y = `Total Volume`)) + geom_point(alpha = 0.2, color = "#2c3e50") + geom_smooth(method = "lm", color = "#e74c3c", se = TRUE) + labs(title = "Price vs. Sales Volume", x = "Average Price ($)", y = "Total Volume (units)") + theme_minimal() ``` ### Instructional Tip > Ask students to discuss in groups: *"What measures can be used to explore the relationship between two variables?"* ] --- # Q4 (continued): Python Regression Example ```python # Python: Linear Regression on Avocado Price vs. Volume / Link to the dashboard: https://github.com/utjimmyx/4000/blob/main/avocado.ipynb import pandas as pd from sklearn.linear_model import LinearRegression import matplotlib.pyplot as plt import numpy as np df = pd.read_csv("avocado.csv", parse_dates=["date"]) X = df[["AveragePrice"]].values # predictor y = df["Total_Volume"].values # outcome model = LinearRegression().fit(X, y) print(f"Intercept (β₀): {model.intercept_:,.1f}") print(f"Coefficient (β₁): {model.coef_[0]:,.1f}") print(f"R² Score: {model.score(X, y):.4f}") # Plot x_line = np.linspace(X.min(), X.max(), 200).reshape(-1, 1) plt.figure(figsize=(8, 5)) plt.scatter(X, y, alpha=0.15, s=8, color="#2c3e50") plt.plot(x_line, model.predict(x_line), color="#e74c3c", lw=2) plt.xlabel("Average Price ($)") plt.ylabel("Total Volume (units)") plt.title("Hass Avocado: Price vs. Sales Volume — Linear Regression") plt.tight_layout() plt.show() ``` --- # Q5: Factors Impacting Demand (Advanced — Open-ended questions) **What factors might impact demand for Hass avocados in the cities identified in Q1?** .pull-left[ ### From the Document This is an **open-ended question** drawing on marketing and economics perspectives. Students can argue that cities with the following characteristics tend to consume more Hass avocados: - **Larger young population** - **Larger percentage of Hispanic population** - **More educated population** - **Higher household income** Organic avocados are more expensive → **household income level** may play a particularly important role. ] .pull-right[ ### Instructional Guidance > *"At this stage, students do not need to validate a hypothesis. Instructors can encourage students to think about what additional data may need to be collected to study the listed reasons."* ### Instructor Actions - Let students recognize potential factors independently - Encourage exploration of external references and studies - Prompt students to identify **what additional data** is needed to validate their hypotheses ### Discussion Prompt *"What data sources could you access to test whether Hispanic population share predicts organic avocado sales per capita?"* ] --- # Q8: Seasonal Factors in Avocado Prices & Sales (Advanced) **Are there seasonal factors affecting avocado prices and sales? How should stakeholders respond?** .pull-left[ ### Part 1: Identifying Seasonal Patterns - Visualize total sales **by month, quarter, and year** - Use trend lines to detect **seasonal variation** ### Pattern Found in the Document - Sales **rise from Q1**, reach a **peak in Q2 or Q3** - Then **drop to the lowest in Q4** - Consistent with Hass avocado **summer harvest** characteristics - Observed consistently **across all four years (2019–2022)** ### Part 2: Recommendations (Open-Ended) - **Store** avocados during low seasons - **Import** from reliable suppliers during peak seasons - Be **proactive** in reaching out to retailers before peak demand ] .pull-right[ <img src="teaching_dataviz_presentation_final_files/figure-html/q8-chart-1.png" width="396" style="display: block; margin: auto;" /> ] --- class: inverse, center, middle # Teaching Plan --- # Teaching Plan Overview The comprehensive teaching plan is designed for the **second managerial accounting course** at junior or senior level, after introductory managerial accounting. <table class="table table-striped table-hover table-bordered" style="font-size: 13px; color: black; margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> Phase </th> <th style="text-align:center;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> Duration </th> <th style="text-align:left;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> Content </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;width: 22%; font-weight: bold;"> i. Technology Preparation </td> <td style="text-align:center;width: 14%; "> 30–40 minutes </td> <td style="text-align:left;width: 64%; "> Lab session: refresh PivotTable, sorting, regression, charts (Excel); dashboard &amp; visualizations (Tableau). Use a small data subset. </td> </tr> <tr> <td style="text-align:left;width: 22%; font-weight: bold;"> ii. Basic Questions (Q1–Q4) </td> <td style="text-align:center;width: 14%; "> 50 minutes </td> <td style="text-align:left;width: 64%; "> Q1: Largest sales by city/year (10 min) · Q2: Organic market share (10 min) · Q3: Price volatility (15 min) · Q4: Regression analysis (15 min) </td> </tr> <tr> <td style="text-align:left;width: 22%; font-weight: bold;"> iii. Advanced Questions (Q5–Q8) </td> <td style="text-align:center;width: 14%; "> 50 minutes </td> <td style="text-align:left;width: 64%; "> Q5: Demand factors discussion (10 min) · Q6: Special order incremental analysis (10 min) · Q7: Sensitivity analysis (15 min) · Q8: Seasonal factors (15 min) </td> </tr> </tbody> </table> --- class: inverse, center, middle # Strategies for Using the Case --- # Three Implementation Options <table class="table table-striped table-hover table-bordered" style="font-size: 12px; color: black; margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> Option </th> <th style="text-align:left;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> Focus </th> <th style="text-align:left;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> Data Setup </th> <th style="text-align:left;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> Deliverable </th> <th style="text-align:left;font-weight: bold;color: white !important;background-color: rgba(44, 62, 80, 255) !important;"> Best For </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;font-weight: bold;"> Option 1 </td> <td style="text-align:left;"> Data Analytics &amp; Visualization </td> <td style="text-align:left;"> Clean data file provided by instructor </td> <td style="text-align:left;"> Charts, dashboards; focus on exploration, analysis, and visualization </td> <td style="text-align:left;"> Marketing Analytics; Intermediate managerial accounting; lab exercise </td> </tr> <tr> <td style="text-align:left;font-weight: bold;"> Option 2 </td> <td style="text-align:left;"> Business Communication &amp; Decision Making </td> <td style="text-align:left;"> Students download raw data from the Hass Avocado Board </td> <td style="text-align:left;"> Full analysis + business recommendations; students define problems &amp; risks </td> <td style="text-align:left;"> Upper-division accounting or business analytics </td> </tr> <tr> <td style="text-align:left;font-weight: bold;"> Option 3 </td> <td style="text-align:left;"> Realistic Business Simulation </td> <td style="text-align:left;"> Students independently source all data </td> <td style="text-align:left;"> Business memorandum report + formal presentation in a virtual business meeting </td> <td style="text-align:left;"> MBA-level or advanced courses </td> </tr> </tbody> </table> --- class: inverse, center, middle # Evidence of Efficacy --- # How the Case Has Been Used .pull-left[ ### Deployment History - First used in **2021** in: - Marketing Analytics and Managerial accounting classes - MBA-level classes - A **university-level business analytics case competition** (30+ students) ### Who Participated in the Competition - MBA students - Upper-level undergraduate accounting and marketing students - Judging panel: **CFOs and marketing specialists** from industry ### Fall 2023–2024 - Used again in a junior-senior level managerial accounting class - **43 students** completed the case and provided feedback ] .pull-right[ ### Top Benefits Cited by Students (Most → Least Mentioned) <img src="teaching_dataviz_presentation_final_files/figure-html/benefits-chart-1.png" width="396" style="display: block; margin: auto;" /> ] --- # Student Survey Results — Fall 2023 (n = 43) *Scale: 1 (Strongly Disagree) → 5 (Strongly Agree)* <img src="teaching_dataviz_presentation_final_files/figure-html/survey-chart-1.png" width="720" style="display: block; margin: auto;" /> --- # Student Voices .pull-left[ > *"It was not a textbook example in which Company XYZ sells 1,000 widgets a week. It was a product that we were familiar with and the data was real-world. We had to determine how to clean the data and manipulate it to provide the answers we were looking for."* > *"I liked how large the data sample actually is. It shows a large amount of data when as a student we usually only get snips of data sets."* > *"I enjoyed the case because like the real world, there is neither a 'right' or 'wrong' answer when analyzing the questions. It is simply using the tools at your disposal to make the most informed decision you can make."* ] .pull-right[ > *"Tableau can be very beneficial for any company... it does not require any coding at all. With the data, you can use Tableau to make very useful charts that anyone can understand."* > *"I learned how to research and analyze data using R to find out the effects of prices in the avocado industry. I had never worked or heard of RStudio before this class, and it was exciting to learn to use it."* > *"We learned how Tableau allows us to convert textual and numerical data into visual dashboards that enable customers to see and understand their data without the need for technical skills or coding knowledge."* ] --- # Educator & Professional Feedback .pull-left[ > *"I really enjoyed the case! It presents a scenario that allows students the flexibility of incorporating different tools to arrive at a visually pleasing and thorough answer for the supervisor. Application of data analysis and presentation aptitude are strong benefits."* > *"It looks like an interesting project that will allow students to use their critical thinking skills to apply their understanding of managerial accounting concepts to a simulated real-life business situation."* ] .pull-right[ > *"This is a case study where students can clearly develop their data analytics and critical thinking skills. The type of scenario this study demonstrates is a common occurrence where leadership is exploring their options for growth and market expansion."* > *"Overall, this case study provides a valuable learning opportunity for students to develop their skills in data analytics, critical thinking, and decision-making in a managerial accounting context."* ] ### Summary The case was reviewed by **nearly two dozen professionals and educators** across the nation — all responses were positive. --- class: inverse, center, middle # Conclusion --- # Conclusion The case continues to be **adaptable** — modifiable for Excel-only, Tableau, R, Python, or any combination. .pull-left[ ### What This Case Achieves The case has **proven to effectively meet its intended learning outcomes** in: - Undergraduate classroom settings - Graduate (MBA) classroom settings - University student case competitions ### Skills Developed Students hone not only **data analytics and visualization** but also: - Critical thinking & problem-solving - Business writing - Oral presentation - Teamwork & leadership ] .pull-right[ ### Why It Matters > *"More and more accounting and business-related jobs list 'being familiar with a certain platform in data analytics and data visualization' as one of the preferred or required qualifications."* ### A Joint Effort This case is a product of collaboration among: - University professors in **accounting and marketing analytics** - Community professionals (**CFOs and marketing consultants**) - Student users who provided essential feedback ] --- # References .small[ 1. Aydiner, A. S., et al. (2019). Business analytics and firm performance: The mediating role of business process performance. *Journal of Business Research*, *96*, 228–237. 2. Verma, A., et al. (2019). An investigation of skill requirements for business and data analytics positions. *Journal of Education for Business*, *94*(4), 243–250. 3. Frigo, M. L., & Krumwiede, P. H. D. (2020). Strategic analysis and the management accountant. *Strategic Finance*, *101*(11), 48–52. 4. Mishra, B. K., et al. (2019). A framework for enterprise risk identification and management. *Managerial Auditing Journal*. 5. King, A. Z. (2021). Data analytics in AACSB-accredited US university accounting programs. *Journal of Education for Business*, 1–9. 6. IMA (2019). IMA management accounting competency framework. https://www.imanet.org 7. Anders, S. B. (2021). CPA Evolution Resources. *The CPA Journal*, *91*(8/9), 76–77. 8. 2018 AACSB Standards for Accounting Accreditation. https://www.aacsb.edu 9. Rebele, J. E., & Pierre, E. K. S. (2019). Learning objectives for accounting education programs. *Journal of Accounting Education*, *48*, 71–79. 10. Hoelscher, J., & Mortimer, A. (2018). Using Tableau to visualize data and drive decision-making. *Journal of Accounting Education*, *44*, 49–59. 11. Indeed Editorial Team (2022). An In-Depth Guide for Finding a Career in Business Analytics. https://www.indeed.com ] --- class: inverse, center, middle # Thank You ### Questions & Discussion ---