Forecasting and Predictive Analytics Certificate

The Forecasting and Predictive Analytics Certificate (15.0 CPE Credits) 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.

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.

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 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 WILL BENEFIT

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

KEY TOPICS

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

LEARNING OBJECTIVES

• 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...read more...