Data Analytics for Business Professionals

Introduction

• Making data more useful
• What you should know

1. Data Analytics in the Business World

• 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

2. Predictive and Prescriptive Analytics

• Predictive analytics
• Challenge: Make predictions
• Solution: Make predictions
• Prescriptive analytics

3. Asking the Right Question

• Guidelines for formulating questions
• Crafting better questions
• Case study two: What is the right question?
• Role of business acumen

4. Unlocking the Data Within

• Data collection issues
• Case study three: Unclean data
• Case study three: Explanation
• Data fail: When data is just wrong

5. Understanding Averages

• Nature of averages
• Case study four: Conversion rates and benchmark
• Case study four: Explanation
• Context is everything

6. Sampling

• Pros and cons
• Case study five: Social media survey
• Case study five: Explanation
• Case study five: Statistical deep dive

7. Cherry-Picking

• What is cherry-picking?
• Case study six: Revenue
• Case study six: Explanation

8. Forecasting

• Hurricane Matthew
• Case study seven: Forecasting customer complaints
• Case study seven: Explanation
• Issues to consider

9. Correlation versus Causation

• Cause and effect
• Case study eight: Boston revenue
• Case study eight: Explanation
• Causal questions

Conclusion

• Next steps