Course Plan

Module 1: Data Modelling with DPL Studio

Objective: Teach learners the fundamentals of data modelling, including how to create and visualize data models in DPL Studio.

Topics Covered:

Introduction to Data Modelling: Understanding data entities, attributes, and relationships.
PlantUML Integration: How DPL Studio integrates with PlantUML to visualize data models.
Creating Data Models: Step-by-step guidance on creating models and defining relationships.
Exporting Models: How to export data models for use in DPL Studio or external applications like MCP or Flowise.

SCORM Activity: Deliver content via SCORM on data modelling and PlantUML integration.
Interactivity: Learners interact with data models by creating relationships and viewing visualizations.
Quiz Activity: A quiz to assess learners' understanding of data modelling concepts.
Question Types: Multiple-choice questions on relationships, entities, and data model types.
Forum Activity: A discussion forum where learners can ask questions or share their data models and get feedback from peers or instructors.
Interactivity: Collaborative discussions on best practices for data modelling.
Assignment Activity: A practical task where learners create their own data model using DPL Studio and submit it for review.
Submission: Learners upload a screenshot or file of their data model.

Module 2: ETL/ELT Workflows in DPL Studio

Objective: Help learners understand and implement ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) workflows in DPL Studio.

Topics Covered:

ETL Workflows: How data is extracted, transformed, and loaded into a target system.
ELT Workflows: Extracting and loading data before transforming it.
Data Transformation and Validation: Using DPL Studio to perform data cleaning, validation, and transformation.
Selecting the Right Workflow: Deciding when to use ETL versus ELT.

SCORM Activity: A SCORM package that demonstrates the differences between ETL and ELT workflows and walks learners through creating both types of workflows in DPL Studio.
Interactivity: Learners set up both ETL and ELT workflows and view real-time examples.
Quiz Activity: A quiz to test learners' understanding of ETL and ELT concepts, as well as their ability to select the appropriate workflow.
Question Types: Multiple-choice and scenario-based questions.
Assignment Activity: Learners will be tasked with setting up an ETL or ELT workflow in DPL Studio, then submit a report on the process.
Submission: Learners provide the steps, screenshots, or files from their workflow setup.
Forum Activity: A discussion forum where learners can share their workflows, discuss challenges, and share insights into their approach to ETL/ELT.
Interactivity: Collaboration and problem-solving discussions regarding different data transformation challenges.

Module 3: Intelligent Automation (IA) Flow in DPL Studio

Objective: Teach learners how to automate data workflows using AI and chatbot integration in DPL Studio.

Topics Covered:

Introduction to Intelligent Automation: Overview of AI-driven workflows in DPL Studio.
Creating AI Workflows: Step-by-step guide to building intelligent workflows using drag-and-drop components.
Chatbot Integration: How to integrate chatbots in DPL Studio for automated task management and data interaction.
Using Flowise for Automation: A look at Flowise for no-code, drag-and-drop workflow design.

SCORM Activity: A SCORM package showing how to create intelligent workflows using AI and chatbots.
Interactivity: Learners will build workflows and see real-time automation triggers in action.
Quiz Activity: A quiz to assess learners' understanding of intelligent automation concepts and how to integrate AI workflows in DPL Studio.
Question Types: Multiple-choice and scenario-based questions on automation and chatbot integration.
Assignment Activity: A practical task where learners create a simple AI-based workflow using DPL Studio and submit it for review.
Submission: Learners upload their completed workflows or screenshots.
Forum Activity: A forum where learners can discuss automation best practices, share challenges, and showcase their intelligent workflows.
Interactivity: Peer review and collaboration on automation workflows.

Module 4: Analytics (Dynamic Visualization) in DPL Studio

Objective: Teach learners how to create dynamic visualizations using DPL Studio's data formulator and real-time data insights.

Topics Covered:

Introduction to Dynamic Visualization: How DPL Studio helps generate real-time, interactive visualizations.
Creating Dashboards and Reports: Guide to building interactive dashboards, charts, and reports.
AI-Driven Data Insights: How AI helps manage and transform data to produce meaningful visualizations.
Using Packs and Prompts for Visualization: Feeding out packs and prompts to dynamically generate visualizations.

SCORM Activity: A SCORM package that shows how to create dynamic visualizations within DPL Studio using AI and data packs.
Interactivity: Learners will experiment with feeding out data and viewing how visualizations are dynamically generated in real-time.
Quiz Activity: A quiz to assess learners' understanding of data visualization concepts, including AI-driven analytics and dynamic visualization techniques.
Question Types: Multiple-choice or scenario-based questions on visualization tools and applications.
Forum Activity: A forum where learners can share their dynamic visualizations, ask for feedback, and discuss the best ways to represent data visually.
Interactivity: Collaborative discussions on visualization techniques and best practices.
Assignment Activity: A task where learners create a dynamic dashboard or report in DPL Studio based on a set of given data, then submit it for feedback.
Submission: Learners submit their dashboard or visualization for evaluation.

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