In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage.

Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts and experience in general data science and machine learning tools and techniques. 

Specifically:

Creating cloud resources in Microsoft Azure.

Using Python to explore and visualize data.

Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.

Working with containers gain these prerequisite skills, and take the following free online training before attending the course:

For Course outline visit: https://learn.microsoft.com/en-us/training/courses/dp-203t00