Module 1: Introduction to data analytics on Azure
This module explores key concepts of data analytics, including types of analytics, data, and storage. Students will explore the analytics process and tools used to discover insights and learn about the responsibilities of an enterprise data analyst and what tools are available to build scalable solutions.
Lessons:
- Explore Azure data services for modern analytics.
- Understand concepts of data analytics.
- Explore data analytics at scale.
After completing this module, students will be able to:
- Describe types of data analytics.
- Understand the data analytics process.
- Define data job roles in analytics.
- Understand tools for scaling analytics solutions.
Module 2: Govern data across an enterprise
This module explores the role of an enterprise data analyst in organizational data governance. Students will explore the use of Microsoft Purview to register and catalog data assets, to discover trusted assets for reporting, and to scan a Power BI environment.
Lessons:
- Introduction to Microsoft Purview.
- Discover trusted data using Microsoft Purview.
- Catalog data artifacts by using Microsoft Purview.
- Manage Power BI artifacts by using Microsoft Purview.
After completing this module, students will be able to:
- Browse, search, and manage data catalog assets.
- Use data catalog assets with Power BI.
- Use Microsoft Purview in Azure Synapse Studio.
- Register and scan a Power BI environment using Microsoft Purview.
Module 3: Model, query, and explore data in Azure Synapse
This module explores the use of Azure Synapse Analytics for exploratory data analysis. Students will explore the capabilities of Azure Synapse Analytics including the basics of data warehouse design, querying data using T-SQL, and exploring data using Spark notebooks.
Lessons:
- Introduction to Azure Synapse Analytics.
- Use Azure Synapse serverless SQL pool to query files in a data lake.
- Analyze data with Apache Spark in Azure Synapse Analytics.
- Analyze data in a relational data warehouse.
Lab: Query data in Azure
Lab: Explore data in Spark notebooks
Lab: Create a star schema model
After completing this module, students will be able to:
- Understand when to use Azure Synapse Analytics in reporting solutions.
- Query data with SQL.
- Query data with Spark.
Module 4: Prepare data for tabular models in Power BI
This module explores the fundamental elements of preparing data for scalable analytics solutions using Power BI. Students will explore model frameworks, considerations for building data models that will scale, Power Query optimization techniques, and the implementation of Power BI dataflows.
Lessons:
- Choose a Power BI model framework.
- Understand scalability in Power BI.
- Optimize Power Query for scalable solutions.
- Create and manage scalable Power BI dataflows.
Lab: Create a dataflow
After completing this module, students will be able to:
- Choose an appropriate Power BI model framework.
- Optimize Power Query.
- Create and manage scalable Power BI dataflows.
Module 5: Design and build scalable tabular models
This module explores the critical underlying aspects of tabular modeling for building Power BI models that can scale. Students will learn about model relationships and model security, working with direct query, and using calculation groups.
Lessons:
- Create Power BI model relationships.
- Enforce model security.
- Implement DirectQuery.
- Create calculation groups.
- Use tools to optimize Power BI performance.
Lab: Create model relationships
Lab: Enforce model security
Lab: Design and build tabular models
Lab: Create calculation groups
Lab: Use tools to optimize Power BI performance
After completing this module, students will be able to:
- Understand and create Power BI model relationships.
- Design and enforce Power BI model security.
- Design and build scalable tabular models.
- Create calculation groups.
Module 6: Implement advanced data visualization techniques by using Power BI
This module explores data visualization concepts including accessibility, customization of core data models, real-time data visualization, and paginated reporting.
Lessons:
- Understand advanced data visualization concepts.
- Customize core data models.
- Monitor data in real-time with Power BI.
- Create and distribute paginated reports in Power BI report builder.
Lab: Monitor data in real-time with Power BI
Lab: Create and distribute paginated reports in Power BI Report Builder
After completing this module, students will be able to:
- Understand and apply advanced data visualization concepts including accessibility.
- Troubleshoot report performance issues.
- Use real-time visuals in Power BI.
- Create and distribute paginated reports.
Module 7: Implement and manage an analytics environment
This module explores key considerations for implementing and managing Power BI. Students will understand key recommendations for administration and monitoring of Power BI, including configuration and management of Power BI capacity.
Lessons:
- Provide governance in a Power BI environment.
- Facilitate collaboration and sharing in Power BI.
- Monitor and audit usage.
- Provision Premium capacity in Power BI.
- Establish a data access infrastructure in Power BI.
- Broaden the reach of Power BI.
- Automate Power BI administration.
- Build reports using Power BI within Azure Synapse Analytics.
After completing this module, students will be able to:
- Recommend Power BI administration settings.
- Recommend a monitoring and auditing solution for a data analytics environment.
- Configure and manage Power BI capacity.
Module 8: Manage the analytics development lifecycle
This module explores considerations for deployment, source control, and application lifecycle management of analytics solutions. Students will understand what to recommend and will be able to deploy and manage automated and reusable Power BI assets.
Lessons:
- Design a Power BI application lifecycle management strategy.
- Create and manage a Power BI deployment pipeline.
- Create and manage Power BI assets.
Lab : Create reusable Power BI assets
After completing this module, students will be able to:
- Recommend strategies for Power BI deployment and source control.
- Recommend automation solutions for the analytics development lifecycle.
