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Course Outline

Overview of Data Science Principles

  • Defining Data Science
  • The Data Science Lifecycle
  • Analytical Tools and Methodologies
  • Microsoft Azure Machine Learning Platform

Data Preparation Procedures

  • Data Origins and Classifications
  • Data Sanitization and Transformation
  • Feature Construction Strategies

Model Development and Training

  • Supervised Learning Approaches
  • Unsupervised Learning Approaches
  • Algorithm Selection and Validation
  • Analysis of Model Outputs

Model Deployment Operations

  • Deployment to Azure Environments for government applications where appropriate
  • System Scalability and Efficiency
  • Lifecycle Management of Deployed Models

Performance Assessment and Optimization

  • Evaluation Criteria and Metrics
  • Model Tuning and Optimization
  • Version Control and Management

Concept Review and Certification Readiness

  • Synopsis of Core Concepts
  • Examination Strategies and Best Practices
  • Simulated Practice Assessment

Requirements

  • Demonstrates foundational knowledge of machine learning principles alongside practical experience in data analytics
  • Proficiency in basic programming and data manipulation techniques is also advisable

Intended Audience

  • Data scientists
  • Data analysts
  • Professionals seeking to acquire machine learning competencies and prepare for the DP-100 certification exam
 21 Hours

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