Introduction:

This program is designed to prepare participants for the certification exam only.

Microsoft Azure offers a powerful platform for building and managing data science solutions, providing a comprehensive set of tools for data storage, processing, machine learning, and deployment. Understanding Azure's ecosystem is essential for professionals aiming to create scalable and efficient data science workflows. This training program focuses on equipping participants with the knowledge and skills to design and implement end-to-end data science solutions on Azure.

Program Objectives:

By the end of this program, participants will be able to:

Explore the components and services of Azure for data science.
Manage data workflows, including storage, preprocessing, and transformation.
Design machine learning models using Azure Machine Learning.
Deploy and monitor data science models in production environments.
Prepare effectively for the Microsoft Certified: Azure Data Scientist Associate (DP-100) exam.

Target Audience:

Data scientists and machine learning engineers.
Cloud architects and Azure administrators.
IT professionals interested in data science workflows on Azure.
Professionals preparing for the DP-100 certification exam.
Professionals seeking to integrate Azure into their data science practices.

Program Outline:

Unit 1:

Introduction to Azure for Data Science:

Overview of Azure’s data science services and tools.
Understanding Azure Machine Learning and its capabilities.
Role of Azure Data Factory in data workflows.
Key features of Azure Synapse Analytics for large-scale data processing.
Integration of Azure with open-source data science tools.

Unit 2:

Data Management and Preparation:

Data storage options: Azure Blob Storage and Data Lake.
Data preprocessing and transformation in Azure Databricks.
How to handle structured and unstructured data in Azure.
Ensuring data quality and consistency across workflows.
Leveraging Azure Data Factory for data integration and pipelines.

Unit 3:

Building and Training Machine Learning Models:

How to create machine learning models using Azure Machine Learning.
Automating model development with Azure AutoML.
Managing experiments and tracking metrics.
Tools for evaluating and selecting the best model for deployment.
Principles of reproducibility and scalability in model training.

Unit 4:

Model Deployment and Monitoring:

Deploying machine learning models with Azure Kubernetes Service (AKS).
Monitoring model performance and addressing drift.
Updating models in production environments.
Tools for ensuring compliance and security in deployed solutions.

Unit 5:

Exam Preparation for DP-100:

Overview of the DP-100 exam structure and content areas.
Reviewing key topics and concepts covered in the exam.
Sample exam questions and their potential answers.
Resources and materials for further study.

Note: This program is designed to prepare participants for the certification exam only.

Microsoft Certified Azure Data Scientist Associate DP 100