Earnix Academy
Course Catalog

Develop the foundational skills required to leverage the core functionality of Earnix Price-It, including data management, modeling, pricing, and pricing execution.

Create and analyze a GBM model in Earnix Price-It, import and analyze models from H2O and DataRobot, and use Machine Learning models as input for other models.

Export modeling documentation, model comparison documentation, segmented graphs, pricing documentation, pricing comparison documentation, and an Efficient Frontier from Earnix Price-It.

Create a Geo-Structure, use a Geo-Parameter for visualization, and create and analyze a model using a Geo-Parameter.

Create and analyze an optimized pricing version, develop a pricing series, and create an Efficient Frontier.

Create and analyze an optimized pricing version, develop a pricing series, and create an Efficient Frontier.

Develop essential skills for unlocking the power of Earnix Automation, providing the ability to easily automate any tasks using Python and its Earnix library.

Design and create advanced pricing reports, including customized formatting, adding new attributes by formula, adding data filters, and creating re-usable pricing report templates.

Develop essential techniques for leveraging advanced modeling capabilities in Earnix Price-It, including spline specifications, advanced model comparison features, and advanced techniques for model validation.

Develop foundational skills for targeting the Earnix Price-It REST API, including GET requests and POST requests of different kinds, and understanding error messages.

Learn how to create shared pricing projects, perform analysis using shared parameters, and analyze the impact of updating parameters in shared and linked projects.

Design and configure the Pricing Behavior of Variables to control the flow of data into Earnix, develop multi-term pricing, and take advantage of structured data in modeling.

Develop and implement advanced model selection algorithms using Earnix AGLM, including feature selection and optimized binning of numeric and nominal variables.

* For Earnix Price-It versions up to Winter 2024 Release (12.2)

In this course, students will learn how to implement advanced non-linear modeling techniques using Generalized Additive Models (GAMs) in Earnix Price-It, to leverage GAMs for more accurate and flexible modeling, particularly in scenarios where traditional linear or polynomial approaches may fall short.