AICPA Certification : Forecasting and Predictive Analytics Certificate

AICPA Certification : Forecasting and Predictive Analytics Certificate - Eduyush
AICPA Certification : Forecasting and Predictive Analytics Certificate - Eduyush
AICPA Certification : Forecasting and Predictive Analytics Certificate - Eduyush

AICPA Certification : Forecasting and Predictive Analytics Certificate

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There's an MCQ assessment at the end of each online module. You need to score 70% on the evaluation to clear the module. Once you complete all the modules, your certification is granted.

You can retake the assessments as many times as you want

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About the Forecasting and Predictive Analytics Certificate

Earners of the Forecasting and Predictive Analytics Certificate can apply regression, classification, clustering, optimization, and simulation techniques, all used for predictive analytics. Badge earners can forecast and predict future values for increasingly sophisticated data sets and business problems using time-based data. These candidates calculate scenarios based on distance and space, and determine the fit and usefulness for prediction of models.

Why?

This certificate is Part 4 of the Data Analyst Certificates Bundle – a comprehensive five-part program that provides training and practical guidance on the topic of data analytics.

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: It is recommended that you complete the Data Analytics Modeling Certificate, or ensure you have equivalent knowledge and skills, before starting this certificate course.

Learning Labs: This is an interactive learning program that includes bonus simulated learning labs with step-by-step guidance. This is an interactive learning program that includes bonus hands-on learning labs that will expose you to the tools needed to implement an analytics practice in a practical way and equip you to deploy those tools as needed within your organization. You will practice using various technologies for preparing, analyzing and managing datasets in the real world. *Time spent on learning labs does not award CPE and completing learning labs is not a requirement for earning the certificate.

 

Who?

Accounting and finance professionals, especially those interested in learning and applying data analysis techniques to help their organizations make informed, data-driven business decisions.

Other information
CPE Credits
15
Format: Course - Online
Access: This is a digital product. You will have access to the content for a year after purchase date.

Learning Outcomes

  • 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.

Key Topics covered

  • Predictive analytics techniques
  • Forecasting with data models
  • Finding relationships in data
  • Bivariate and multivariate linear regression
  • KNN classification
  • Clustering
  • Decision trees
  • Training models


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