Course Outline

Introduction to Data Science

  • What is Data Science?
  • The Data Science Process
  • Data Science Tools and Techniques
  • Microsoft Azure Machine Learning

Preparing Data

  • Data Sources and Types
  • Data Cleaning and Transformation
  • Feature Engineering

Building and Training Models

  • Supervised Learning
  • Unsupervised Learning
  • Model Selection and Evaluation
  • Interpreting Model Outputs

Deploying Models

  • Deploying Models to Azure
  • Scalability and Performance
  • Managing Deployed Models

Evaluating Model Performance

  • Model Evaluation Metrics
  • Tuning Model Performance
  • Managing Model Versions

Summary and Exam Preparation

  • Review of Key Concepts
  • Exam Preparation Tips and Strategies
  • Hands-on Practice Exam


  • A fundamental understanding of machine learning concepts and experience working with data analytics
  • Familiarity with the basics of programming and data manipulation is also recommended


  • Data scientists
  • Data analysts
  • Anyone who wants to learn about machine learning and prepare for the DP-100 exam
  21 Hours


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