This is a Self-Study / On-Demand event. In the event title, "Webcast" indicates the event has a self-paced webcast to view with the materials to complete the self-study. "Download" indicates the user downloads only the materials to complete the self-study.
|Full time Accounting Educator:||None|
The Forecasting and Predictive Analytics Certificate will teach you fundamental techniques used for predictive analytics: regression, classification, clustering, optimization, and simulation. Beginning with basic models for revealing and establishing relationships, you will learn to apply increasingly sophisticated modeling techniques for practical data analysis, as well as commonly encountered problems so you can determine the fit and usefulness for prediction of your models, and apply them to typical business problems.
As you develop your understanding of applied predictive analytics, you'll learn how to perform basic forecasting using time-based data to predict future values from a model. You will also learn how to model and calculate scenarios based on distance and space. You will get practice with classification, including naive Bayesian classification; create basic decision trees; and use various techniques for clustering and linear optimization to solve common business problems; as well as learn techniques for assessing the effectiveness of your solutions.
Note: This is an on-demand/self-study course offered by a 3rd party vendor and will not be accessible in the CPE Tracker section of the OSCPA website. Course access information will be emailed directly to you by AICPA. The course expires one year from the purchase date.
- Identify the different techniques of predictive analytics: regression, classification, clustering, optimization, and simulation
- Calculate varying types of regressions using R and Excel
- Apply classification and clustering algorithms
- Apply business process optimization to problems by identifying goals and constraints
- Analyze the various probabilities of outcomes by applying Monte Carlo simulation
- Calculate performance of predictive analytic algorithms
- Predictive analytics techniques
- Forecasting with data models
- Finding relationships in data
- Bivariate and multivariate linear regression
- KNN classification
- Decision trees
- Training models