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CoursePlan

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.

Moodle Activities:​

  1. SCORM Activity: Deliver content via SCORM on data modelling and PlantUML integration.

    • Interactivity: Learners interact with data models by creating relationships and viewing visualizations.

  2. Quiz Activity: A quiz to assess learners' understanding of data modelling concepts.

    • Question Types: Multiple-choice questions on relationships, entities, and data model types.

  3. 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.

  4. 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.

Moodle Activities:​

  1. 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.

  2. 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.

  3. 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.

  4. 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.

Moodle Activities:​

  1. 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.

  2. 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.

  3. 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.

  4. 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.

Moodle Activities:​

  1. 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.

  2. 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.

  3. 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.

  4. 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.