Accelerate Your Tech Career with Pega Training @ FPT

Course content:

1. Introduction and Overview:

   - Introduction to PEGA Decisioning, NBA (Next Best Action), and Prediction Studio.

   - Real-world use cases and applications of NBA.

   - Overview of Decisioning Studios, including CDH (Customer Decision Hub) and Prediction Studio.

2. Application Architecture and Setup:

   - Understanding PEGA's DM (Decision Management) sample application.

   - Creating a new Decisioning application and its structure.

   - Exploring NBA class structures and data types.

   - Walkthrough of the ADM (Adaptive Decision Manager) service infrastructure and DDS (Decision Data Store) configuration.

3. Proposition Management:

   - Introduction to Proposition Management in PEGA.

   - Adding business issues, business groups, and properties.

   - Uploading propositions from CSV files.

4. Proposition Data and Decision Data:

   - Working with versioned and un versioned proposition data.

   - Getting started with decision data and its management.

   - Copying proposition groups for data organization.

5. Data Sets and Data Flows:

   - Understanding data sets and their types in PEGA.

   - Configuring incoming data and data flow actions.

   - Applying data flow actions: Compose, Convert, Merge, Strategy, Data Transforms, Filters, and Abstracts.

   - External data flows and their integration.

6. Event Strategy and Strategy Shapes:

   - Introduction to Event Strategy and its differences from regular Strategy.

   - Event Strategy actions: Filter, Lookup, Split, Split & Join, Window, and Aggregate.

   - Strategy shapes: Sub Strategy, Prediction, Import, Business Rules, Decision Analytics, Enrichment, Arbitration, Selection, and Aggregation.

7. Modelling Decisions - Adaptive Models:

   - Overview of Adaptive Models in PEGA.

   - Adaptive Models components: Predictors, Context, Outcomes, and Monitoring.

   - Interaction History and Table, Customer Responses, and Self-Learning Models configuration.

8. Modelling Decisions - Predictive Models:

   - Introduction to Predictive Models and PMML (Predictive Model Markup Language).

   - Predictive Models components: Monitor, Model, Input Mapping, and Parameters.

9. Modelling Decisions - Text Analytics:

   - Introduction to Text Analytics and its role in PEGA.

   - Text Analytics and Extraction techniques.

   - Analyzing text files and extracting relevant information.

10. Prediction Studio and Performance Analysis:

    - Using Prediction Studio to analyze model performance.

    - Analyzing Adaptive Model, Predictive Model, and Text Extraction performance.

    - Calculating Propensity Scores.

11. Treatments, Actions, and Presentations:

    - Defining and managing customer actions and treatments.

    - Understanding different treatment types.

    - Presenting offers via treatments and integrating with campaigns.

12. Engagement, Contact Policy, and Constraints:

    - Creating Engagement Strategies and Contact Policies.

    - Volume Constraints and Proposition Eligibility Rules.

    - Simulations and Segmentations for targeted engagement.

13. Containers and Real-time Execution:

    - Types of artifacts and containers in PEGA.

    - Using the Realtime Container for dynamic decision-making.

    - Action Arbitration and Intelligence in real-time scenarios.

Note: This is a general outline to give you an idea of the key concepts covered in Pega Training course.