This no-code course provides a structured, practical introduction to three pillars of modern generative AI: Google Gemini, IBM Watson Analytics, and the Model Context Protocol (MCP). Designed for all levels, it consists of 20 concise lessons (~7 minutes each) that progress from fundamentals to real-world applications. You will learn how to set up tools, craft effective prompts, explore and visualize data, and plan integrations—without any programming.
What you’ll learn across 20 topics:
Introduction to Google Gemini: Understand Gemini’s capabilities, core features, and where it excels.
Getting started with Gemini: Simple setup steps, account options, and navigating key interfaces.
Prompt engineering for Gemini: Clear frameworks for writing, refining, and testing prompts that deliver consistent results.
Practical automation with Gemini: Time-saving workflows for drafting content, summarizing, and task optimization.
IBM Watson Analytics overview: Orientation to the interface, terminology, and analytics workflow.
Navigation and sections in Watson: How to import data, organize assets, and use dashboards efficiently.
Data discovery and visualization: Exploratory analysis, visual best practices, and insight communication in Watson.
Hands-on analytics (no code): Guided exercises using sample datasets to create charts, summaries, and shareable outputs.
MCP fundamentals: What the Model Context Protocol is, why it matters, and common use cases.
MCP principles and patterns: Core concepts, roles, and design patterns explained in plain language.
MCP implementation (no code): Practical, tool-based approaches to configure and use MCP-enabled integrations.
Standards and best practices: Governance, security basics, and emerging conventions around MCP.
Choosing the right tool: Clear comparison of Gemini and Watson strengths to match the right tool to each task.
MCP Gemini: High-level integration scenarios to extend Gemini with tools and data safely and effectively.
MCP Watson: Practical ways to enhance analytics workflows and insights using MCP-enabled connections.
Iterative content generation: Structured methods to draft, review, refine, and finalize AI outputs.
Intelligent workflows for efficiency: Building repeatable, business-friendly processes that save time and reduce errors.
Common mistakes to avoid: Practical guidance on prompt quality, data hygiene, and integration pitfalls.
Advanced use cases: Realistic scenarios across content, analytics, research, and operations—no coding required.
Capstone and next steps: A guided mini-project plan to combine Gemini, Watson, and MCP, plus checklists and resources.
Who this course is for:
Professionals, students, creators, and managers looking to apply AI in content, analytics, and automation
Anyone seeking a clear, no-code path to practical AI skills
How you’ll learn:
Short, focused lessons with step-by-step guidance
Repeatable templates, checklists, and workflows
Realistic examples you can adapt to your context
Outcomes:
By the end of the course, you will be able to:
Set up and navigate Gemini and Watson confidently
Write effective prompts and build iterative content workflows
Explore and visualize data to communicate insights clearly
Understand MCP and plan practical, no-code integrations
Select the right tool for the job and avoid common mistakes
No coding experience is required. The course emphasizes clarity, decision-making, and dependable outcomes.
