Course Overview

Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift. This course demonstrates how to ingest, store, and transform data in the data warehouse. Topics covered include: the purpose of Amazon Redshift, how Amazon Redshift addresses business and technical challenges, features and capabilities of Amazon Redshift, designing a Data Warehousing Solution on AWS by applying best practices based on the Well-Architected Framework, integration with AWS and non-AWS products and services, performance tuning, orchestration, and securing and monitoring Amazon Redshift.

Course level: Advanced

Duration: 3 days


Activities

This course includes presentations, hands-on labs, and demonstrations.

Course Objectives

In this course, you will learn to:

  • Describe Amazon Redshift architecture and its roles in a modern data architecture
  • Design and implement a data warehouse in the cloud using Amazon Redshift
  • Identify and load data into an Amazon Redshift data warehouse from a variety of sources
  • Analyze data using SQL QEV2 notebooks
  • Design and implement a disaster recovery strategy for an Amazon Redshift data warehouse
  • Perform maintenance and performance tuning on an Amazon Redshift data warehouse
  • Secure and manage access to an Amazon Redshift data warehouse
  • Share data between multiple Redshift clusters in an organization
  • Orchestrate workflows in the data warehouse using AWS Step Functions state machines
  • Create an ML model and configure predictors using Amazon Redshift ML

Course Content

Day 1

Module 1: Data Warehouse Concepts

  • Modern data architecture
  • Introduction to the course story
  • Data warehousing with Amazon Redshift
  • Amazon Redshift Serverless architecture
  • Hands-On Lab: Launch and Configure an Amazon Redshift Serverless Data Warehouse

Module 2: Setting up Amazon Redshift

  • Data models for Amazon Redshift
  • Data management in Amazon Redshift
  • Managing permissions in Amazon Redshift
  • Hands-On Lab: Setting up a Data Warehouse using Amazon Redshift Serverless

Module 3: Loading Data

  • Overview of data sources
  • Loading data from Amazon Simple Storage Service (Amazon S3)
  • Extract, transform, and load (ETL) and extract, load, and transform (ELT)
  • Loading streaming data
  • Loading data from relational databases
  • Hands-On Lab: Populating the data warehouse

Day 2

Module 4: Deep Dive into SQL Query Editor v2 and Notebooks

  • Features of Amazon Redshift Query Editor v2
  • Demonstration: Using Amazon Redshift Query Editor v2
  • Advanced queries
  • Hands-On Lab: Data Wrangling on AWS

Module 5: Backup and Recovery

  • Disaster recovery
  • Backing up and restoring Amazon Redshift provisioned
  • Backing up and restoring Amazon Redshift Serverless

Module 6: Amazon Redshift Performance Tuning

  • Factors that impact query performance
  • Table maintenance and materialized views
  • Query analysis
  • Workload management
  • Tuning guidance
  • Amazon Redshift monitoring
  • Hands-On Lab: Performance Tuning the Data Warehouse

Module 7: Securing Amazon Redshift

  • Introduction to Amazon Redshift security and compliance
  • Authentication with Amazon Redshift
  • Access control with Amazon Redshift
  • Data encryption with Amazon Redshift
  • Auditing and compliance with Amazon Redshift
  • Hands-On Lab: Securing Amazon Redshift

Day 3

Module 8: Orchestration

  • Overview of data orchestration
  • Orchestration with AWS Step Functions
  • Orchestration with Amazon Managed Workflows for Apache Airflow (MWAA)
  • Hands-On Lab: Orchestrating the Data Warehouse Pipeline

Module 9: Amazon Redshift ML

  • Machine Learning Overview
  • Getting started with Amazon Redshift ML
  • Amazon Redshift ML workflow scenarios
  • Amazon Redshift ML Usage
  • Hands-On Lab: Predicting customer churn with Amazon Redshift ML

Module 10: Amazon Redshift Data Sharing

  • Overview of data sharing in Amazon Redshift
  • Amazon DataZone for Data as a service

Module 11: Wrap-Up

  • Hands-On Lab: End of course challenge lab

Course Overview

Learn how to design and build data processing systems.

This four-day instructor-led class provides you with a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, you will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.

Virtual Learning

This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.

Course Objectives

In this course you will learn:

  • Design and build data processing systems on Google Cloud Platform
  • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
  • Derive business insights from extremely large
  • datasets using Google BigQuery
  • Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
  • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
  • Enable instant insights from streaming data

Course Content

1. Serverless Data Analysis with BigQuery

  • What is BigQuery
  • Advanced Capabilities
  • Performance and pricing

2. Serverless, Autoscaling Data Pipelines with Dataflow

3. Getting Started with Machine Learning

  • What is machine learning (ML)
  • Effective ML: concepts, types
  • Evaluating ML
  • ML datasets: generalization

4. Building ML Models with Tensorflow

  • Getting started with TensorFlow
  • TensorFlow graphs and loops + lab
  • Monitoring ML training

5. Scaling ML Models with CloudML

  • Why Cloud ML?
  • Packaging up a TensorFlow model
  • End-to-end training

6. Feature Engineering

  • Creating good features
  • Transforming inputs
  • Synthetic features
  • Preprocessing with Cloud ML

7. ML Architectures

  • Wide and deep
  • Image analysis
  • Embeddings and sequences
  • Recommendation systems

8. Google Cloud Dataproc Overview

  • Introducing Google Cloud Dataproc
  • Creating and managing clusters
  • Defining master and worker nodes
  • Leveraging custom machine types and preemptible worker nodes
  • Creating clusters with the Web Console
  • Scripting clusters with the CLI
  • Using the Dataproc REST API
  • Dataproc pricing
  • Scaling and deleting Clusters

9. Running Dataproc Jobs

  • Controlling application versions
  • Submitting jobs
  • Accessing HDFS and GCS
  • Hadoop
  • Spark and PySpark
  • Pig and Hive
  • Logging and monitoring jobs
  • Accessing onto master and worker nodes with SSH
  • Working with PySpark REPL (command-line interpreter)

10. Integrating Dataproc with Google Cloud Platform

  • Initialization actions
  • Programming Jupyter/Datalab notebooks
  • Accessing Google Cloud Storage
  • Leveraging relational data with Google Cloud SQL
  • Reading and writing streaming Data with Google BigTable
  • Querying Data from Google BigQuery
  • Making Google API Calls from notebooks

11. Making Sense of Unstructured Data with Google’s Machine Learning APIs

  • Google’s Machine Learning APIs
  • Common ML Use Cases
  • Vision API
  • Natural Language API
  • Translate
  • Speech API

12. Need for Real-Time Streaming Analytics

  • What is Streaming Analytics?
  • Use-cases
  • Batch vs. Streaming (Real-time)
  • Related terminologies
  • GCP products that help build for high availability, resiliency, high-throughput, real-timestreaming analytics (review of Pub/Sub and Dataflow)

13. Architecture of Streaming Pipelines

  • Streaming architectures and considerations
  • Choosing the right components
  • Windowing
  • Streaming aggregation
  • Events, triggers

14. Stream Data and Events into PubSub

  • Topics and Subscriptions
  • Publishing events into Pub/Sub
  • Subscribing options: Push vs Pull
  • Alerts

15. Build a Stream Processing Pipeline

  • Pipelines, PCollections and Transforms
  • Windows, Events, and Triggers
  • Aggregation statistics
  • Streaming analytics with BigQuery
  • Low-volume alerts

16. High Throughput and Low-Latency with Bigtable

  • Latency considerations
  • What is Bigtable
  • Designing row keys
  • Performance considerations

17. High Throughput and Low-Latency with Bigtable

  • What is Google Data Studio?
  • From data to decisions

Course Overview

Course Description

Creating and Configuring Production ROSA Clusters (CS220) teaches how to configure ROSA clusters as part of pre-existing AWS environments and how to integrate ROSA with AWS services commonly used by IT operations teams, such as Amazon CloudWatch.


Note: This course is offered as a two day in person class, a three day virtual class or is self-paced. Durations may vary based on the delivery. For full course details, scheduling, and pricing, select your location then “get started” on the right hand menu.

Course Content Summary

– Create ROSA STS PrivateLink clusters

– Connect PrivateLink ROSA clusters to existing VPCs and enable administrators and developers to access those clusters

– Configure dedicated machine pools and node/pod autoscaling

– Configure node, cluster, and audit log forwarding to Amazon CloudWatch

– Configure authentication and group sync with Amazon Cognito

Virtual Learning

This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.

Course Objectives

Impact on the Organization

  • Red Hat OpenShift Service on AWS (ROSA) is a turnkey application platform that provides a managed Red Hat OpenShift service that runs natively on Amazon Web Services (AWS) to enable organizations to increase operational efficiency, refocus on innovation, and quickly build, deploy, and scale applications. Red Hat OpenShift is the hybrid cloud platform that brings operational consistency to on-premise and different cloud environments.
  • Organizations adopting ROSA are typically existing AWS customers with skills on using AWS services for a variety of business scenarios and need to integrate managed OpenShift clusters with their pre-existing AWS environments. These organizations are usually very security-conscious and require strong access controls and network security for all of their AWS services, including their ROSA clusters.


Impact on the Individual

  • After completing CS220, students can create private ROSA clusters which are integrated with AWS infrastructure services typically employed by IT operations teams and ready to start onboarding applications and developers.

Course Content

Private Red Hat OpenShift on AWS (ROSA) Clusters

Create a PrivateLink ROSA cluster with STS and enable developers or administrators to access the API and router endpoints of the cluster.


Node and Pod Autoscaling

Configure a ROSA cluster and a workload to dynamically scale the number of cluster nodes and application pods according to load.


Monitor ROSA Clusters with Amazon CloudWatch

Configure ROSA clusters to forward logs to Amazon CloudWatch for long-term storage, aggregation, and analysis, and to authenticate OpenShift users by using Amazon Cognito.

Course Overview

In this learning path, you’ll see how Azure AI Document Intelligence solutions can enable you to capture data from typed or hand-written forms. You’ll also learn how to build a solution for your custom form types and integrate that solution into an Azure Cognitive Search pipeline. You’ll learn how to:

  • Design a solution that analyzes your business forms by using Azure AI Document Intelligence.
  • Create a solution that analyzes common documents by using Document Intelligence.
  • Create a solution that analyses different custom form types by using Document Intelligence.
  • Include an Azure AI Document Intelligence service as a custom skill in an Azure Cognitive Search pipeline.

Course Content

Module 1 Plan an Azure AI Document Intelligence solution

Learn how to use Azure AI Document Intelligence to build solutions that analyze forms and output data for storage or further processing.

  • Describe the components of an Azure AI Document Intelligence solution.
  • Create and connect to Azure AI Document Intelligence resources in Azure.
  • Choose whether to use a prebuilt, custom, or composed model.

Module 2 Use prebuilt Form Recognizer models

Learn what data you can analyze by choosing prebuilt Forms Analyzer models and how to deploy these models in a Form Analyzer solution.

  • Identify business problems that you can solve by using prebuilt models in Forms Analyzer.
  • Analyze forms by using the General Document, Read, and Layout models.
  • Analyze forms by using financial, ID, and tax prebuilt models.

Module 3 Extract data from forms with Azure Document Intelligence

Azure Document Intelligence uses machine learning technology to identify and extract key-value pairs and table data from form documents with accuracy, at scale. This module teaches you how to use the Azure Document Intelligence Azure AI service.

  • Identify how Document intelligence’s layout service, prebuilt models, and custom models can automate processes.
  • Use Document intelligence’s capabilities with SDKs, REST API, and Document Intelligence Studio.
  • Develop and test custom models.

Module 4 Create a composed Form Recognizer model

Learn how to assemble custom models into composed solutions that can analyze different types of your own documents.

  • Describe business problems that you would use custom models and composed models to solve.
  • Train a custom model to obtain data from forms with unusual structures.
  • Create a composed model that can analyze forms in multiple formats.

Module 5 Build a Document Intelligence custom skill for Azure AI Search

Learn how to use an Azure Document Intelligence solution as a custom skill to enrich content in an Azure AI Search pipeline.

  • Describe how a custom skill can enrich content passed through an Azure AI Search pipeline.
  • Build a custom skill that calls an Azure Forms Analyzer solution to obtain data from forms.

Course Overview

In this learning path, you practice building journeys using Dynamics 365 Customer Insights. The skills validated include creating marketing assets like emails, creating a segment, creating a journey, adding elements to a journey, and publishing the journey.

Course Content

Create emails in Dynamics 365 Customer Insights – Journeys

This module covers emails, assets, and personalization in the real-time work area of Dynamics 365 Customer Insights – Journeys.

  • Introduction
  • Manage assets
  • Create marketing emails
  • Personalize content
  • Use brand profiles
  • Use Copilot to generate email content ideas
  • Style emails with AI-assisted themes
  • Create content blocks
  • Preview and test emails
  • Check your message for errors and publish
  • Send emails without building a journey
  • Edit a live email
  • Prevent sending emails to duplicated email addresses
  • Check your knowledge
  • Summary

Build journeys with Dynamics 365 Customer Insights – Journeys

This module covers segments, triggers, and journeys within the real-time work area of Dynamics 365 Customer Insights – Journeys.

  • Introduction
  • Create and manage segments
  • Work with triggers
  • Create a journey
  • Add messages to the journey
  • Add activities to the journey
  • Add other elements to the journey
  • Publish journeys
  • Check your knowledge
  • Summary

Guided project – Create and manage journeys with Dynamics 365 Customer Insights

In this module, practice building journeys in Customer Insights, including creating assets, building a segment, and creating a segment-based or trigger-based journey. This lab offers interactive practice with real-world scenarios for business-specific challenges.

  • Introduction
  • Prepare for the guided project
  • Exercise – Create emails
  • Exercise – Create a segment
  • Exercise – Create a segment-based journey
  • Exercise – Create a trigger-based journey
  • Knowledge check
  • Summary

Course Overview

Create your own data model and canvas app to support a scenario for a fictional company. You’re provided high-level specifications on the custom tables, columns and canvas app needed to complete this project.

Course Content

Get started with Power Apps canvas apps

This module introduces the learner to Power Apps. It starts with an introduction video briefly describing the “why” (case for Power Apps) and the “what” for what users can do with Power Apps. The units then take users through the “how” instilling in them the confidence that they can use Power Apps to interact with their data.

  • Introduction to Power Apps
  • Start Power Apps
  • Exercise – Create your first app in Power Apps
  • Power Apps data sources
  • Exercise – Create an app from Excel using Copilot
  • Use Power Apps with Power Automate and Power BI
  • Designing a Power Apps app
  • Check your knowledge
  • Summary

Customize a canvas app in Power Apps

In this module, we’ll show learners how to customize their app, a necessary skill for taking advantage of the capabilities of Power Apps. This unit builds upon the app produced in the first unit.

  • Improve your app by making basic customizations
  • Explore controls and screens in canvas apps
  • Exercise – Introduction to formulas in canvas apps
  • Exercise – Create basic screen navigation for a canvas app
  • Check your knowledge
  • Summary

How to build the User Interface in a canvas app in Power Apps

In this module, learners will learn how to build UI for their app including theming, icons, images, personalization, form factors and controls. In their learning path, thus far, learners have used basic controls with little to no customization. This unit shows how to make an app more personal and help it fit branding or personal requirements.

  • Use themes to quickly change the appearance of your app5 min
  • Brand a control
  • Icons
  • Images
  • Personalization
  • Build for phones or tablets
  • Exercise – Create and adjust UI for a new canvas app
  • Check your knowledge
  • Summary

Work with external data in a Power Apps canvas app

Do you need to connect an app to access data? Then this module is for you. It focuses on connecting your app to a data source.

  • Introduction
  • Data-source overview
  • Add a data source
  • Add Office 365 users to your application
  • Display and interact with your data in a gallery
  • Move data between collections and data sources by using Collect
  • Exercise – Work with external data in a canvas app
  • Check your knowledge
  • Summary

Write data in a Power Apps canvas app

Forms can be used to view, edit, and create records. This module demonstrates how to use forms to write data to your data source. Topics will include form setup, the different form modes, and how to configure a submit button.

  • Introduction to forms
  • Form modes
  • Adding and customizing an Edit form
  • Submit your form
  • Special properties
  • Exercise- Working with forms
  • Check your knowledge
  • Summary

Publish, share, and maintain a canvas app

You’ve built your first app. Now, it’s time to publish, share it with others, and maintain subsequent versions of the app.

  • Introduction
  • Exercise – Publish your app
  • Exercise – Share your app
  • Exercise – Maintain your app
  • Application lifecycle management
  • Check your knowledge
  • Summary

Guided Project – Create and manage canvas apps with Power Apps

Create your own data model and canvas app to support a scenario for a fictional company. You’re provided high-level specifications on the custom tables, columns and canvas app needed to complete this project.

  • Introduction
  • Prepare
  • Exercise – Create a canvas app that connects to a data source
  • Knowledge check
  • Summary

Course Overview

Get started with Power Automate by create and manage automated processes with Power Automate. Including creating triggers for cloud flows, configuring actions, implementing conditional logic for a cloud flow, testing a cloud flow, creating and configuring approvals by using Power Automate, and sharing cloud flows.

Course Content

Module 1 Get started with Power Automate

Power Automate is an online workflow service that automates actions across the most common apps and services.

Module 2 Build approval flows with Power Automate

Power Automate is an online workflow service that automates actions across the most common apps and services. In this module, you build approval flows to streamline your business, save time, and work more efficiently.

Module 3 Build flows to manage user information

Power Automate is an online workflow service that automates actions across the most common apps and services. In this module, you build more flows to manage user information.

Module 4 Power Automate’s deep integration across multiple data sources

Power Automate is an online workflow service that automates actions across the most common apps and services. In this module, you’ll build flows across multiple data sources.

Module 5 Guided Project – Create and manage automated processes with Power Automate

Create your own data model and flow to support a scenario for a fictional company. You’ll be provided with high-level specifications on the custom tables, columns, and flow needed to complete this project.


Course Overview

Copilots are quickly becoming a popular way to use with AI models, automate tasks, and improve productivity. In this learning path, you’ll explore options for adopting, using, and creating copilots

Course Objectives

By the end of this course, you are able to:

  • Understand generative AI’s place in the development of artificial intelligence.
  • Understand language models and their role in intelligent applications.
  • Describe examples of copilots and good prompts.
  • Create copilots.
  • Test copilots.
  • Analyze performance.
  • Describe core features and capabilities of Azure AI Studio
  • Use Azure AI Studio to provision and manage an Azure AI resource
  • Use Azure AI Studio to create and manage an AI project
  • Understand when to use Azure AI Studio
  • Identify the need to ground your language model with Retrieval Augmented Generation (RAG)
  • Index your data with Azure AI Search to make it searchable for language models
  • Build a copilot using RAG on your own data in the Azure AI Studio

Course Content

Fundamentals of Generative AI

In this module, you explore the way in which language models enable AI applications and services to generate original content based on natural language input. You also learn how generative AI enables the creation of copilots that can assist humans in creative tasks.

Get started with Microsoft Copilot Studio

Microsoft Copilot Studio allows organizations to quickly create copilots based on business scenarios their customers and employees can easily interact with as needed. In this module, you’re introduced to key concepts for copilots.

Introduction to Azure AI Studio

Microsoft Azure offers multiple services that enable developers to build amazing AI-powered solutions. Azure AI Studio brings these services together in a single unified experience for AI development on the Azure cloud platform.


Build a RAG-based copilot solution with your own data using Azure AI Studio

Copilots can work alongside you to provide suggestions, generate content, or help you make decisions. Copilots use language models as a form of generative artificial intelligence (AI) and will answer your questions using the data they were trained on. To ensure a copilot retrieves information from a specific source, you can add your own data when building a copilot with the Azure AI Studio

Course Overview

Discover how to successfully drive adoption of Microsoft Copilot for Microsoft 365 in your organization.

Learn how to drive adoption of Microsoft Copilot for Microsoft 365 using the Microsoft 365 adoption framework to create and implement a robust adoption plan.

This course has been created by Microsoft as a “Bring Your Own Environment” course, allowing individuals to use their personal devices and settings.  If a student wants to complete the hands-on activities, the individual must bring their own CoPilot environment.  Otherwise, demonstrations and lectures will be conducted by the instructor

Course Objectives

Students will learn to,

  • Explore adoption methods and strategies for the adoption of Microsoft Copilot for Microsoft 365
  • Envision a successful adoption of Copilot for Microsoft 365
  • Onboard and empower your employees to use Copilot for Microsoft 365 efficiently
  • Drive value and maximize the impact of Copilot for Microsoft 365 within your organization

Course Content

Module 1: Explore adoption methods and strategies for the adoption of Microsoft Copilot for Microsoft 365

  • Describe the People First strategy and the three key principles within its framework.
  • Describe the Microsoft 365 adoption framework.
  • Describe the rapid adoption method.
  • Discover insights from an organization who adopted Copilot for Microsoft 365 to transform their business.

Module 2: Envision a successful adoption of Copilot for Microsoft 365

  • Understand the key roles needed to assemble the appropriate technology enablement team.
  • Define a user experience strategy.
  • Identify and prioritize scenarios.
  • Establish success measures and reporting plan.
  • Assess your organization’s readiness.

Module 3: Onboard and empower your employees to use Copilot for Microsoft 365 efficiently

  • Prepare your technical environment for Copilot for Microsoft 365.
  • Build your own Microsoft Copilot Center of Excellence.
  • Build a Champion and an Early Adopters Program.
  • Build an engagement strategy.
  • Build a training strategy and implement rapid adoption motions.

Module 4: Drive value and maximize the impact of Copilot for Microsoft 365 within your organization

  • Gather feedback from users and stakeholders.
  • Track end user adoption through distribution of surveys.
  • Review and track adoption score and usage reports in the Microsoft 365 admin center.
  • Encourage ongoing engagement of Copilot for Microsoft 365.

Course Overview

Microsoft Copilot for Microsoft 365 applies the power of AI to boost productivity, unlock creativity, and help users understand information better with a simple chat. By integrating large language models (LLMs) with tenant data from Microsoft Graph and the Microsoft 365 apps, Copilot for Microsoft 365 helps turn natural language words into a powerful productivity tool.

Copilot for Microsoft 365 transforms work in three meaningful ways:

  • Unleash creativity. Copilot helps you create content faster and more efficiently.
  • Unlock productivity. Copilot helps you focus on what matters most.
  • Up-level skills. Copilot makes you better at what you’re good at and helps you quickly master what you have yet to learn.

This course has been created by Microsoft as a “Bring Your Own Environment” course, allowing individuals to use their personal devices and settings.  If a student wants to complete the hands-on activities, the individual must bring their own CoPilot environment.  Otherwise, demonstrations and lectures will be conducted by the instructor

Course Objectives

  • Introduction to Copilot for Microsoft 365
  • An executive’s guide to creating effective prompts in Copilot for Microsoft 365
  • Transform executive productivity with Copilot for Microsoft 365

This course is focused on :

  • Empowers executives to leverage Microsoft Copilot to boost productivity and decision-making.
  • Enhances strategic and operational capabilities using AI within Microsoft 365.
  • Provides interactive learning experiences for real-world applications of Copilot.
  • Focuses on streamlining workflows and making data-driven decisions.
  • Prepares leaders to optimize strategic initiatives with Copilot’s AI features.

Course Content

Module 1: Introduction to Copilot for Microsoft 365

This module explores the intricacies of Copilot for Microsoft 365, offering insights into its functionality and Microsoft’s dedication to implementing AI responsibly and ethically.

  • Describe the purpose and functionalities of Copilot for Microsoft 365.
  • Outline the working principles behind Copilot for Microsoft 365.
  • Identify the core components integral to Copilot for Microsoft 365.
  • Articulate Microsoft’s dedication to responsible AI practices.

Module 2: An executive’s guide to crafting effective prompts in Copilot for Microsoft 365

Master the art of crafting effective prompts for Microsoft Copilot for Microsoft 365. This Module is designed to enhance your skills in creating prompts that generate precise and useful results across various Microsoft 365 apps, including Word, PowerPoint, Teams, and Outlook.

  • Understand and apply the principles of crafting effective prompts.
  • Use Copilot Lab to discover and employ pre-made prompts.
  • Efficiently manage your email communications and weekly planning with Copilot in Outlook.
  • Enhance meeting summaries in Teams by highlighting key decisions and action items.

Module 3: Transform executive productivity with Copilot for Microsoft 365

Interact with and explore the capabilities of Copilot for Microsoft 365, and discover how generative AI can enhance your workflow and productivity.

  • Use Microsoft Copilot to synthesize your emails and chats.
  • Use Copilot in Word to create and review documents.
  • Use Copilot in PowerPoint to create and refine presentations.