Business Analysis for Executives with Microsoft Copilot

Access all the materials and templates that support your learning journey.

This lesson explains how to craft effective prompts for Copilot to get accurate answers, structured insights, and noise-free analysis.

This lesson shows how a top manager and BA define the real business problem and validate it using documents, chats, and data inside Copilot.

This lesson demonstrates how to uncover the true root cause of business decline—not the symptoms—using AI for multilayered analysis.

This lesson explains how to identify key stakeholders based on weak points in the ShopNow case.

This lesson shows how to create a clear stakeholder communication plan in Copilot: messages, risks, dependencies, and influence points.

Learn how to use Udemy's AI Assistant for streamlining your learning.

This lesson explains the Discovery process and shows how Copilot gathers scattered information and turns it into a coherent picture.

This lesson explains how to build AS-IS and TO-BE models and demonstrates how Copilot accelerates change analysis.

This lesson explains how to translate business needs into specific functional and non-functional requirements for a product and system.

This lesson teaches how to build requirement traceability, track changes, and manage Change Requests so the team stays aligned.

In this lesson, you'll get your own AI companion: the book "AI for Business".

This lesson shows how requirements move through their full lifecycle—creation, refinement, approval, and monitoring—and how Copilot supports each stage.

This lesson teaches how to manage changes: assess their impact, prioritize them, and keep the team aligned using Copilot.

This lesson explains how to identify risks, forecast their consequences, and build mitigation plans with the help of Copilot.

This lesson explores how a CEO thinks, what strategic signals matter, and how Copilot helps make decisions grounded in real data.

This lesson shows how a BA evaluates whether implemented changes produced real impact using metrics, reports, and Copilot-based analysis.

This lesson explains how to think with an Agile mindset: adapt quickly, test hypotheses, work with a living business model, and respond effectively to change.

A full-fledged final lesson that closes the entire ShopNow case and the student's entire journey through the modules.

Learn how to generate charts and visuals in Excel using natural language prompts with Copilot.

Discover how Copilot enhances data storytelling through automatically generated, insightful, and interactive visuals.

Master how to customize, filter, and export your visuals for polished presentations and reports with Copilot’s help.

Discover how to create complete project plans in Word with Copilot’s assistance.

Discover how to use Copilot to model project outcomes under different scenarios (e.g., delays, resource changes) and generate actionable insights.

Discover how to quickly turn project goals into actionable step-by-step checklists and task breakdowns using Copilot.

Learn how to prompt Copilot to generate concise and structured project briefs that capture objectives, scope, deliverables, and key success criteria.

Discover how Copilot summarizes meetings and generates follow-up action items directly in Outlook.

Discover how Copilot helps you track overdue tasks and send effective follow-ups to keep projects on track.

See how Copilot can help you track performance indicators, spot bottlenecks, and provide clear summaries of project health.

Learn how Copilot helps you draft clear, professional project emails in Outlook with ease.

See how AI can streamline collaboration and improve communication across different teams.

Discover how Copilot helps you monitor project expenses and keep budgets on track in Excel.

Use Copilot to instantly create structured, professional project status reports in Word.

We’ll explain the difference between narrow AI (task-specific), general AI (human-level), and superintelligence — and where today’s models fit on that scale.

You’ll learn how models actually “think” — not with logic or visuals, but through tokens, probabilities, and associations — and how that affects their responses.

We’ll break down parameters like temperature, max tokens, and top-p — and how they impact the model’s tone, style, and predictability.

This lesson shows how to set roles, formats, constraints, and styles — even if you don’t write code. It’s about “programming with words.”

We’ll introduce PromptOps as a systematic approach to working with prompts — treating them as a repeatable process rather than random one-off requests.

You’ll learn that not all prompts are equal — some are disposable, some reusable, some dynamic with variables, and some become the core "brain" of an AI agent.

In this lesson, we’ll explore the difference between precise instruction (Prompt Engineering) and creative dialogue with the model (Vibe Coding) — and why both are essential for professional AI work.

Understand how memory works in large language models — what information they retain, how they use context, and where their limits are.

Explore how modern AI systems now include built-in memory to personalize and adapt to your needs.

This lesson explores how companies and creative teams integrate PromptOps into their daily workflows — automating research, content creation, and analysis through structured prompt systems.

This lesson explains the basics of prompting — how a model thinks, why the precision of your request determines the quality of the response, and how to build a systematic workflow instead of random interactions.

This lesson shows how the model reacts when you give it a task with no examples or instructions — a pure test that reveals its true level of understanding.

This lesson teaches how to guide a model using examples: you show it a few samples of what you want, and it learns to imitate the structure, tone, and logic to generate consistent results.

This lesson demonstrates how to make the model “think out loud” — explaining its reasoning step by step before giving a final answer, improving accuracy and transparency.

This lesson reveals a method where the model generates several reasoning paths and selects the most consistent one — a kind of “collective intelligence” inside the model.

This lesson shows how a model can generate intermediate knowledge on its own when context is missing — and then use that knowledge to produce a more accurate answer.

This lesson teaches how to break down a complex task into a sequence of smaller prompts that build upon each other, forming a logical workflow.

This lesson introduces a reasoning approach where the model explores multiple thought paths in parallel, pruning weaker ideas and keeping the strongest solutions.

This lesson explains how to combine generation with retrieval — the model doesn’t invent data but searches for factual information in external sources and integrates it into its response.

This lesson shows how a model can create or optimize prompts by itself, testing multiple variations and selecting the most effective one.

This lesson introduces a dynamic prompting technique where prompts evolve during the conversation based on intermediate results, improving output quality in real time.

This lesson explains how to guide the model’s reasoning direction — through subtle hints that orient it toward a particular type of logic, creativity, or emotional tone.

This lesson demonstrates an approach where the model both reasons and acts — analyzing, verifying, taking actions, and adjusting its behavior like a human problem-solver.

This lesson explores how language models can combine natural language with code — generating not just explanations, but executable program fragments that solve tasks automatically.

This lesson teaches models to “learn from themselves”: they review their previous answers, evaluate mistakes, and generate improved responses based on reflection.

This lesson shows how the logic of Chain-of-Thought applies not only to text but also to images, audio, and video — creating true multimodal reasoning.

This lesson explains how models can think non-linearly, using graph structures of nodes and connections between ideas to represent complex systems of knowledge.

This lesson explores the concept of “prompts that create prompts” — teaching the model to design its own instructions for different tasks, becoming a true co-engineer in your workflow.

This lesson explains the basics of prompting — how a model thinks, why the precision of your request determines the quality of the response, and how to build a systematic workflow instead of random interactions.

Understand the core principles for using AI responsibly, ethically, and safely in professional environments.

Learn how to respect intellectual property, give proper credit, and ensure originality when using AI-generated content.

Identify and reduce biases in AI systems to promote fairness, equity, and inclusion in outcomes.

Discover best practices for protecting personal and confidential data when interacting with AI tools.

Develop skills to verify and validate AI-generated information to ensure accuracy and reliability.

Explore global AI regulations and understand how policies like GDPR affect AI usage and compliance.

Learn strategies to balance AI automation with critical human judgment and decision-making.

Stay on the cutting edge with the latest tools, trends, and techniques to maximize your GenAI and ChatGPT skills.

In this video, you will find out how to get a certificate after completing the course.

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