Learn how to use data analytics to make better decisions and gain competitive advantage as a business professional.
Overview
Syllabus
Introduction
- Making data more useful
- What you should know
- Business leaders and data analytics
- Introduction to WearOne
- Types of data
- Case study one: Performance at Miami locations
- Case study one: Explanation
- Challenge: Calculate descriptives
- Solution: Calculate descriptives
- Predictive analytics
- Challenge: Make predictions
- Solution: Make predictions
- Prescriptive analytics
- Guidelines for formulating questions
- Crafting better questions
- Case study two: What is the right question?
- Role of business acumen
- Data collection issues
- Case study three: Unclean data
- Case study three: Explanation
- Data fail: When data is just wrong
- Nature of averages
- Case study four: Conversion rates and benchmark
- Case study four: Explanation
- Context is everything
- Pros and cons
- Case study five: Social media survey
- Case study five: Explanation
- Case study five: Statistical deep dive
- What is cherry-picking?
- Case study six: Revenue
- Case study six: Explanation
- Hurricane Matthew
- Case study seven: Forecasting customer complaints
- Case study seven: Explanation
- Issues to consider
- Cause and effect
- Case study eight: Boston revenue
- Case study eight: Explanation
- Causal questions
- Next steps
Taught by
John Johnson