SCMA 691 Special Topic:
Accounting Analytics
Fall 2021Dr. Mi (Jamie) Zhou
Wednesdays, 7:00p to 9:40p
Modality: RONA Accounting Analytics is the application of data analytics and data visualization to the field of accounting. In this course, you will learn how to collect, prepare, and translate accounting-related data (financial and non-financial) into insights and visualizations for effective decision making. This course uses real-world examples and explores many areas in which accounting data can be used to answer business questions, extract patterns/relationships, identify potential fraud, evaluate corporate strategies, forecast financial performance/trends, and so on. This course will cover all the types of analytics based on the Gartner model shown below. These analytics together can help organizations understand and learn from past patterns and performance to improve predictions and actions for the future.
Descriptive Analytics (what happened): Descriptive analytics is usually used to understand data and generate routine reports for operations, finance, or sales. You will learn different skills of data manipulation, including extraction, transformation, and loading (ETL). You will also learn how to calculate commonly used KPIs, how to summarize data, and how to present results in different formats such as tables, charts, and advanced visualizations.
Diagnostic Analytics (why did it happen): You will learn what-if analysis and root cause analysis to create variance reports to show differences between/among different plans and situations. You will learn skills and techniques such as drill-down, outlier discovery, pattern identification, correlation analysis, analysis of variance (ANOVA), etc.
Predictive Analytics (what will happen): Predictive analytics goes beyond knowing what has happened to provide the best assessment of what will happen in the future. You will learn sensitive analysis, simulation analysis, and regression analysis to predict future results (sales, income, return, etc.) by using historical data. You will also learn the strength and weaknesses of these analyses, how to evaluate the predicting accuracy, and which one to use in certain situations.
Prescriptive Analytics (what should I do): Prescriptive analytics combines all the three types of analytics listed above to analyze the outcomes of what-if analysis, assess the trade-offs, and suggest the best course of action. You will learn heuristics and optimization analysis that combine the results from other types of analysis to make recommendations or decisions.
Data mining and machine learning algorithms are often employed to support prescriptive analytics by helping to make specific suggestions based on nuanced patterns, influencing factors, and future predictions. You will learn some basic machine learning and data mining techniques such as textual analysis and classification to enhance or replace certain human reasoning for decision automation. Please note that those skills mentioned above can be used in multiple types of analytics. Also, though not explicitly mentioned, data visualization is an integrated part of all types of analytics.
This course would be appropriate for any business major who is interested in learning data analytics. Though some examples in this course are more accounting specific (e.g., audit and tax), students from other business majors should also find the course valuable as the skills covered in this course are widely applicable, and data and example are all business-related (asset allocation, employee scheduling, portfolio optimization, financial planning, etc.).
If you have questions, feel free to reach out to Dr. Mi (Jamie) Zhou: mizhou@vcu.edu
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