MSIA 423: Analytics Value Chain

Quarter Offered

Spring ; Chloe Mawer and Fausto Inestroza


Getting value from data science requires more than developing the perfect algorithm. In fact, analysis is only one step in the analytics value chain. This class will teach data scientists how to move a machine learning based solution from POC to production as well as design of experiments to ensure the deployed solution is having its intended impact on key business metrics.
The course objectives are to learn how to:
  • Write production-ready code
  • Apply best practices for code and model testing and quality assurance
  • Apply the principles of data infrastructure that enable data science and machine learning
  • Use cloud services to develop and deploy machine learning models
  • Maximize value at each step of the model lifecycle
  • Develop reproducible machine learning models
  • Build effective data pipelines to support model development and deployment
  • Design effective experiments for testing new software features and machine learning models