Learn how to explore what's available in a database: the tables, relationships between them, and data stored in them.

Data exploration and analysis is at the core of data science. Data scientists require skills in programming languages like Python to explore, visualize, and manipulate data.

Throughout this learning path you explore and configure the Azure Machine Learning workspace. Learn how you can create a workspace and what you can do with it. Explore the various developer tools you can use to interact with the workspace. Configure the workspace for machine learning workloads by creating data assets and compute resources.

Explore the various language models that are available through the Azure AI Studio's model catalog. Understand how to select, deploy, and test a model, and to improve its performance.

Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.

As a data scientist, you can use Azure Machine Learning to train and manage your machine learning models. Learn what Azure Machine Learning is, and get familiar with all its resources and assets.

Microsoft Fabric notebooks serve as a comprehensive tool for data exploration, enabling users to uncover hidden patterns and relationships in their datasets.

Data professionals perform distinct roles in building and managing software solutions, and work with multiple technologies and services to do so.

Learn how you can interact with the Azure Machine Learning workspace. You can use the Azure Machine Learning studio, the Python SDK (v2), or the Azure CLI (v2).

Relational database systems are a common way to store and manage transactional and analytical data in organizations of any size around the world.

Azure Cosmos DB provides a highly scalable store for non-relational data.

Deploy Windows Server Failover Cluster and availability groups in Azure and hybrid environments. Configure temporal tables, geo-replication, and auto-failover groups.