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Introduction to Accounting Data Analytics and Visualization

Approx. 19 hours to complete

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Course Summary

This course introduces the basics of accounting and data analytics, with a focus on visual representation of financial data. Students will learn how to analyze financial statements, create visualizations, and interpret data to make business decisions.

Key Learning Points

Learn the basics of accounting and financial statements
Understand how to use data analytics to make business decisions
Create effective visualizations to represent financial data

Learning Outcomes

Analyze financial statements using accounting principles
Create effective visualizations to represent financial data
Use data analytics to make informed business decisions

Prerequisites or good to have knowledge before taking this course

Basic understanding of accounting principles
Familiarity with Excel or similar spreadsheet software

Course Difficulty Level

Intermediate

Course Format

Self-paced
Online

Similar Courses

Financial Accounting Fundamentals
Data Visualization with Tableau
Business Analytics Fundamentals

Related Education Paths

Notable People in This Field

Founder of FiveThirtyEight
Professor of Economics at Stony Brook University

Related Books

Description

Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R.

We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. In the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE).

Outline

INTRODUCTION TO THE COURSE
Course Introduction
About Ronald Guymon
Learn on Your Terms
Syllabus
Glossary
About the Discussion Forums
ePub
Learn More About Flexible Learning Paths
Update Your Profile

MODULE 1: INTRODUCTION TO ACCOUNTANCY ANALYTICS
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 1 Overview and Resources

MODULE 2: ACCOUNTING ANALYSIS AND AN ANALYTICS MINDSET
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 2 Overview and Resources
Lesson 2.2 Knowledge Check
Lesson 2.3 Knowledge Check

MODULE 3: DATA AND ITS PROPERTIES
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 3 Overview and Resources
Lesson 3.2 Knowledge Check
Lesson 3.4 Knowledge Check

MODULE 4: DATA VISUALIZATION 1
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 4 Overview and Resources

MODULE 5: DATA VISUALIZATION 2
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 5 Overview and Resources

MODULE 6: ANALYTIC TOOLS IN EXCEL 1
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 6 Overview and Resources

MODULE 7: ANALYTIC TOOLS IN EXCEL 2
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 7 Overview and Resources

MODULE 8: AUTOMATION IN EXCEL
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
Gies Online Programs
Module 8 Overview and Resources
Congratulations!
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Summary of User Reviews

Learn the basics of accounting data analytics and visualization with this course on Coursera. Students have praised the course for its comprehensive introduction and hands-on approach.

Key Aspect Users Liked About This Course

The course provides a thorough understanding of accounting data analytics and visualization.

Pros from User Reviews

Hands-on approach to learning
Comprehensive introduction to accounting data analytics
Great for beginners
Flexible schedule
Engaging instructors

Cons from User Reviews

Some technical issues with the platform
Not enough advanced material for experienced professionals
Course may be too basic for some
Limited interaction with instructors
Lack of real-world examples

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