Predictive Analytics Essential Training for Executives

Introduction

• Speak the language of data scientists

1. Getting Serious about Analytics

• 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

2. Hiring for Analytics

• 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

3. What to Consider before Buying Software

• 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

4. Organizational Structure

• 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

Conclusion

• Next steps