AI Tool Review: Microsoft Copilot FoundationsAI Tool Review: Microsoft Copilot Foundations is a beginner-level course that shows you how to unlock the full potential of Copilot in your work.
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- Discover exactly how AI can support your data analysis and deliver smarter results.
- Use AI for data tasks like cleaning, categorizing, analyzing, forecasting, and building scenarios.
- Avoid the most common AI mistakes in data work and know how to manage the risks.
- Apply a proven data prompting framework to get more specific and actionable results.
- Use prompt templates for each stage of data analysis to save time and drive better outputs.
- Validate your AI results with trusted methods to improve accuracy and confidence.
What’s Included In The Course
- Downloadable Resources
- Completion Certificate
- Live Demonstrations
- Activities to Implement
- Real-world Examples
Resources
-> AI Data Analysis Playbook
-> AI for Data and Analysis Quickstart Guide
AI is a powerful data analysis partner. AI can support multiple aspects of data handling, including data cleaning, analysis, adding context, providing insights, and developing recommendations. In this course, you’ll learn how you can use AI for data analysis across a variety of data inputs and outputs.
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Generative AI: Data Foundations Course Outline
- The benefits of using AI for data tasks
- Why AI is a powerful tool for a range of data tasks and processes
- The different data tasks that AI can support
- How detailed and quick AI is at providing data outputs
- The primary risks when using AI for data tasks
- How to minimize the risks
- AI can understand and interpret data from multiple formats
- How to check if AI can understand your data before starting a data task
- The steps to conducting data analysis with AI
- How AI supports each stage of data analysis
- Why context is important in AI data prompts
- The specific context that helps with AI data analysis
- How business, data, analysis and objective context produce better AI results
- How to effectively prompt for data with AI
- How to apply the B.R.I.E.F. framework to data prompts
- Elements that improve specificity with data prompts
- Tips and best practices for data prompting
- How to effectively prompt for data analysis
- How to apply a prompt layering approach to data analysis
- How to dig deeper with specific, detailed questions
- Types of data prompts and what they do
- Templates you can use for different types of prompts
- Why it is essential to validate AI outputs
- How to apply strategies to validate outputs
Course Description
Generative AI: Data Foundations is an intermediate course that shows you how to use AI to improve the accuracy, speed, and impact of your data analysis. You’ll discover how AI can assist in data cleaning, categorization, analysis, forecasting, scenario planning, and more.
The course begins with an overview of how and why AI is a valuable tool in data work. You’ll explore the core benefits of using AI for data tasks and how it can dramatically reduce the time and effort it takes to turn raw data into actionable insights.
Next, you’ll explore the full range of data tasks that AI supports. Learn how AI can clean messy data, quickly analyze datasets, categorize information, and generate insights or forecasts. You’ll see how AI can handle different types of data and how to check if your data is compatible before getting started.
You’ll then learn how to avoid common pitfalls when using AI for data. Discover the biggest risks in AI-powered data work — like hallucinations, bad outputs, or missing context — and get strategies to manage and minimize those risks.
You’ll walk through the full process of using AI for data analysis, including each stage of analysis and how AI supports that step. Learn how to provide the right context — including business goals, data types, and desired outcomes — to guide AI to deliver more accurate and useful results.
Then, you’ll explore how to prompt AI for better data outputs. Apply the proven B.R.I.E.F. framework to your prompts to increase specificity and clarity. You’ll also get best practices and real-world examples to help you write better prompts every time.
You’ll learn how to go deeper into your data using prompt layering — a method that breaks complex analysis into smaller, more specific steps to uncover deeper insights. This strategy shows you how to iteratively improve your analysis by refining your prompts.
You’ll get a library of prompt templates that you can use for common data tasks. These templates are designed to help you get started faster and get more consistent, higher-quality results from AI.
Finally, you’ll learn how to validate AI outputs to make sure your results are accurate and actionable. This section includes multiple strategies for reviewing and confirming the accuracy of your AI-driven analysis.
At the end of this course, you’ll have a complete framework for using AI in data tasks — from input to output. You’ll know how to prompt, evaluate, and validate your data analysis with confidence.
This course includes the AI Data Analysis Playbook and the AI for Data and Analysis Quickstart Guide to help you implement what you learn and start using AI for data immediately.
Learning Objectives
- Articulate the benefits of using AI for data analytics.
- Use AI for a range of tasks like data cleaning, categorization, analysis, forecasting, scenarios, and more.
- Avoid common pitfalls and risks when using AI for data tasks.
- Apply the steps of data analysis and use AI at each stage.
- Provide AI with data context to get better results from analysis.
- Apply prompting tips and best practices with a proven data prompting framework.
- Use prompt templates for each stage of data prompting.
- Validate AI data outputs with multiple validation strategies.
Resources
- AI Data Analysis Playbook
- AI for Data and Analysis Quickstart Guide
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Krista NeherDigital Marketing Expert & Best-Selling Author
Krista Neher is a six-time bestselling author, international speaker, award-winning entrepreneur, global thought-leader, and an expert in social media, digital marketing, and personal branding, with over 20 years of experience.
She is the CEO of Boot Camp Digital and a contributor on Forbes and Newsweek. She wrote the first textbook on social media and created the first accredited digital marketing certification in the world.
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