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 Duration 21 hours

Course Outline

Foundations of Data Science

  • Definition and Scope of Data Science
  • The Data Science Lifecycle
  • Tools and Methodologies in Data Science
  • Overview of Microsoft Azure Machine Learning

Data Preparation Strategies

  • Identifying Data Sources and Classifications
  • Data Cleansing and Transformation Protocols
  • Advanced Feature Engineering Techniques

Model Development and Training

  • Application of Supervised Learning
  • Application of Unsupervised Learning
  • Model Selection Criteria and Evaluation Frameworks
  • Analysis and Interpretation of Model Outputs

Model Deployment Procedures

  • Deploying Solutions to Microsoft Azure
  • Optimizing Scalability and System Performance
  • Operational Management of Deployed Models

Performance Assessment

  • Standard Metrics for Model Evaluation
  • Strategies for Tuning Model Performance
  • Governance of Model Versions

Summary and Certification Readiness

  • Recap of Core Concepts
  • Effective Strategies for Exam Preparation
  • Simulated Practical Assessment

Requirements

  • A foundational grasp of machine learning concepts and practical experience with data analytics tools
  • Proficiency in basic programming and data manipulation is strongly advised

Intended Audience

  • Professional Data Scientists
  • Data Analysts
  • Individuals seeking to develop machine learning competencies and prepare for the DP-100 certification examination

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