DP-100 – Microsoft Azure Data Scientist

Training Title: DP-100 – Microsoft Azure Data Scientist

Course Summary

The DP-100: Microsoft Azure Data Scientist training is designed for data professionals and data scientists who want to develop machine learning solutions on the Microsoft Azure platform. This course prepares you to design, train, deploy, and manage machine learning models in a cloud environment. You will explore advanced tools such as Azure Machine Learning, Azure Databricks, and Azure AI to solve complex problems with massive data sets, and learn to work on data science projects on Azure.

This training also prepares you for the DP-100 exam, leading to the Microsoft Certified: Azure Data Scientist Associate certification.

Training Objectives

• Design machine learning (ML) solutions with Azure Machine Learning.
• Build, train, and evaluate machine learning models on Azure.
• Deploy ML models in a production environment and monitor them using Azure Monitor.
• Use tools like Azure Databricks for large-scale data processing and analysis.
• Set up data pipelines and automate machine learning workflows.
• Integrate machine learning solutions with other Azure services, including Azure Databricks and Azure Synapse Analytics.
• Prepare for the DP-100 exam and earn the Microsoft Certified: Azure Data Scientist Associate certification.

Training Program

Day 1: Introduction to Machine Learning with Azure

1. Overview of Azure Machine Learning and Cloud Services
   • Introduction to Azure Machine Learning and its cloud services for machine learning projects.
   • Understand the machine learning lifecycle (data acquisition, data preparation, training, validation, and deployment).
   • Roles and responsibilities of a Data Scientist and an Azure Data Scientist in an ML project.
2. Preparing Data for Machine Learning
   • Use Azure Databricks and Azure Data Lake for cleaning, transforming, and preparing data.
   • Data preprocessing techniques: handling missing values, normalization, encoding categorical variables.
   • Feature selection and dimensionality reduction.

Day 2: Building and Training Machine Learning Models

3. Creating and Training Machine Learning Models
   • Use supervised and unsupervised machine learning algorithms available in Azure Machine Learning.
   • Build and train regression, classification, and clustering models.
   • Hyperparameter optimization and model selection with cross-validation.
   • Implement advanced algorithms, such as neural networks and deep learning.
4. Deploying Machine Learning Models
   • Deploy models in Azure environments, including containers and web applications.
   • Use Azure Kubernetes Service (AKS) to deploy and manage models at scale.
   • Monitor and track deployed models with Azure Monitor and Azure Application Insights.

Day 3: Creating Machine Learning Pipelines with Azure

5. Building Machine Learning Pipelines with Azure Machine Learning
   • Automate machine learning workflows with Azure Machine Learning pipelines.
   • Set up training, validation, deployment, and model monitoring processes.
   • Integrate pipelines with versioning and model management tools.
6. Optimizing and Improving Machine Learning Models
   • Strategies for model optimization and performance evaluation.
   • Advanced techniques like Bayesian optimization and ensemble methods to improve model performance.
   • Manage overfitting and underfitting in machine learning models.

Day 4: Managing Data Science Solutions with Azure

7. Managing Data Science Solutions with Azure
   • Secure data and ML models within Azure.
   • Manage Azure resources associated with ML projects, including development environments and computing infrastructure.
   • Integrate AI with Azure services like Azure Cognitive Services and Azure Synapse Analytics for end-to-end solutions.

Day 5: Exam Preparation

8. Preparing for the DP-100 Exam
   • Review of key concepts and technical skills covered in the training.
   • Practice questions and mock exams to prepare effectively for the certification.
   • Tips and best practices for passing the DP-100 exam and achieving the Microsoft Certified: Azure Data Scientist Associate certification.

Training Details

• Duration: 5 days (40 hours), including hands-on demonstrations, interactive exercises, and real-world case studies to help you master the required skills.
• Prerequisites:
  • Prior experience in data, machine learning, or data analysis is highly recommended.
  • Basic programming skills, ideally in Python, and familiarity with libraries such as pandas, numpy, and scikit-learn.
  • Familiarity with machine learning and data analytics concepts.
• Target Audience:
  • Data scientists and data analysts who want to specialize in machine learning on the Microsoft Azure platform.
  • IT professionals, developers, or data engineers seeking to deepen their knowledge of Azure Machine Learning.
  • Anyone seeking to earn the Azure Data Scientist Associate certification.

Certification

This training prepares you for the DP-100 exam and the Microsoft Certified: Azure Data Scientist Associate certification.

Take your data science career to the next level with Azure and leverage powerful machine learning capabilities to solve complex problems!