This certificate program equips CPAs and financial professionals with a structured learning path through the three stages of data analytics’descriptive, predictive, and prescriptive. Each course builds on the last, ensuring you gain both foundational knowledge and advanced skills to transform data into actionable insights that drive smarter financial decisions. You’ll begin with Descriptive Analytics, learning how to evaluate analytics maturity and apply techniques such as text mining, clustering, and outlier detection to uncover patterns and anomalies. Next, you’ll advance to Predictive Analytics, where you will apply forecasting methods and modeling techniques’including statistical forecasting and CART trees’to anticipate future outcomes such as staffing needs, revenue trends, and market segmentation. Finally, you complete the journey with Prescriptive Analytics, focusing on optimization strategies to recommend the best course of action. You’ll solve decision problems involving capital investment and inventory management, gaining hands-on experience in applying prescriptive methods to real-world financial challenges. By completing this certificate, you’ll move through the full analytics lifecycle, developing the ability to benchmark analytics capabilities, forecast with confidence, and optimize decisions for greater business value.
Learning Objectives
• Determine the level of analytics maturity within client organizations to benchmark capabilities and identify growth opportunities.
• Recognize and use forecasting methods to make and evaluate forecasts that support data-driven decision-making.
• Identify when and how optimization techniques can enhance financial decision-making in areas such as capital investment and inventory management.
Major Topics
• Introduction to Analytics
• What is analytics
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• The three types of analytics: descriptive, predictive, prescriptive
• Analytics maturity benchmarking for CPAs and client organizations
• Descriptive Analytics
• Identifying insights with text mining
• Using multidimensional clustering to uncover patterns
• Detecting anomalies with statistical outlier methods
• Predictive Analytics
• Fundamentals of forecasting: making and evaluating forecasts statistically
• Forecasting simple time series (e.g., staffing needs for an office)
• Forecasting complex time series (e.g., product revenue trends)
• Forecasting nonlinear targets with CART trees for market segmentation
• Prescriptive Analytics
• Understanding data analytics optimization
• Formulating and solving optimization problems
• Optimizing capital investment decisions with decision trees
• Improving inventory management using reorder points and safety stock
