Description

Course Details:

• 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.

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.

Digital Badge:

• On completion of this certificate you will receive a digital badge to share with your online professional network, which is available as a downloadable PDF certificate suitable for printing and framing.

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 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.