Learn how to hire and manage data science professionals and transform your business with effectively deployed advanced analytics.
Overview
Syllabus
Introduction
- Speak the language of data scientists
- Analytics is about making decisions
- Propensity scores and business problems
- The unintended consequences of proof of concept projects
- Why deployment, not insight, is the primary goal
- Analytics as a profit center
- Data science job requirements and problems they can create
- Growing a data science team organically
- Data scientists both with and without vertical industry experience
- The importance of subject matter expertise to modeling
- CRISP-DM: Established process of producing predictive models
- Traits of top performing data scientists
- Analytics and machine learning software options
- Specific data prep for each project
- Citizen data scientists and self service analytics
- AutoML and self-service analytics: Emerging technologies
- Explainable AI and interpretable machine learning
- Analytics project management
- The career path of the data scientist
- Who data scientists should report to
- The CAO: Organizational structure from a senior executive POV
- Next steps
Taught by
Keith McCormick