Course: Introduction to Accounting Data Analytics and Visualization
Modules:
Course Introduction, About Ronald Guymon
Module 1 Introduction, 1.1.1 History and Future of Accounting, 1.1.2 The Importance of Data and Analytics in Accounting, 1.1.3 Humans' Relationship with Data, 1.1.4 Accountants' Role in Shaping How Data Is Used, 1.1.5 Data Analytics Tools: Spreadsheets vs. Data Science Languages, 1.2.1 Advanced Data Analytics in Managerial Accounting Overview, 1.2.2 Advanced Data Analytics in Auditing Overview, 1.2.3 Advanced Data Analytics in Financial Accounting Overview, 1.2.4 Advanced Data Analytics in Taxes Overview, 1.2.5 Advanced Data Analytics in Systems Accounting Overview, Module 1 Conclusion
Module 2 Introduction, 2.1.1 Making Room for Empirical Enquiry, 2.1.2 System 1 vs. System 2 Mindset, 2.2.1 Linking Core Courses to Analytical Thinking, 2.2.2 Inductive and Deductive Reasoning, 2.2.3 Advanced Analytics and the Art of Persuasion, 2.3.1 FACT Framework: Frame the Question, 2.3.2 FACT Framework: Assemble the Data, 2.3.3 FACT Framework: Calculate Results, 2.3.4 FACT Framework: Tell Others About the Results, 2.3.5 FACT Framework Review, Module 2 Conclusion
Module 3 Introduction: What is Data?, 3.1.1 Characteristics that Make Data Useful for Decision Making, 3.2.1 Structured vs. Unstructured Data, 3.2.2 Properties of a Tidy Dataframe, 3.2.3 Data Types, 3.2.4 Data Dictionaries, 3.3.1 Wide Data vs. Long Data, 3.3.2 Merging Data, 3.3.3 Data Automation, 3.4.1 Visualization Distributions, 3.4.2 Visualizing Data Relationships, Module 3 Conclusion
Module 4 Introduction, 4.1.1 Why Visualize Data?, 4.1.2 Visual Perception Principles, 4.1.3 Data Visualization Building Blocks, 4.2.1 Basic Chart Data, 4.2.2 Scatter Plots, 4.2.3 Bar Charts, 4.2.4 Box and Whisker Plots, 4.2.5 Line Charts, 4.2.6 Maps, 4.3.1 Financial Chart Data, 4.3.2 Waterfall Charts, 4.3.3 Candlestick Charts, 4.3.4 Treemaps and Sunburst Charts, 4.3.5 Sparklines and Facets, 4.3.6 Charts to Use Sparingly, Module 4 Conclusion
Module 5 Introduction, 5.1.1 Getting Started with Tableau, 5.1.2 Scatter Plots in Tableau - 1, 5.1.3 Scatter Plots in Tableau - 2, 5.1.4 Bar Charts and Histograms in Tableau, 5.1.5 Box Plots and Line Charts in Tableau, 5.2.1 Adding Dimensions in Tableau, 5.2.2 Facets and Groups in Tableau, 5.3.1 Data Joins in Tableau, 5.3.2 Tableau Analytics - Forecasts, 5.3.3 Tableau Analytics - Clusters and Confidence Intervals, 5.4.1 Communicating Tableau Analyses, Module 5 Conclusion
Module 6 Introduction, 6.1.1 Framing a Question: Larry's Commissary, 6.1.2 Assembling Data, 6.1.3 Data Analysis ToolPak and Descriptive Statistics, 6.1.4 Correlation, 6.2.1 Linear Models, 6.2.2 Simple Regression, 6.2.3 Regression Diagnostics 1: Regression Summary, ANOVA, and Coefficient Estimates, 6.3.1 Multiple Regression, 6.3.2 Regression Diagnostics 2: Predicted Values, Residuals, and Standardized Residuals, 6.3.3 Regression Diagnostics 3: Line Fit Plots, Adjusted R Square, and Heat Maps for P-Values, 6.4.1 Making a Forecast with a Linear Model, Module 6 Conclusion
Module 7 Introduction, 7.1.1 Polynomial Regression Models, 7.1.2 Categorical Variables, 7.1.3 Multiple Indicator Variables, 7.1.4 Interaction Terms, 7.1.5 Regression Summary, 7.2.1 Optimization with Excel Solver, 7.2.2 Solver Constraints and Reports, 7.3.1 Logit Transformation, 7.3.2 Simple Logistic Regression, 7.3.3 Logistic Regression Accuracy, Module 7 Conclusion
Module 8 Introduction, 8.1.1 Recording Macros, 8.1.2 Basics of VB Editor, 8.1.3 Basics of VBA, 8.2.1 For Loops, Variables, Index Numbers, and Last Rows, 8.2.2 Programming Hints, 8.2.3 Conditional Statements, 8.3.1 Macro for Creating Multiple Histograms, 8.3.2 Clustering Overview, 8.3.3 K-Means Clustering in Excel, 8.3.4 K-Means Clustering Macro, 8.3.5 Clustering On a Larger Scale, Module 8 Conclusion, Learn on Your Terms