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
• Why learn data analytics in an AI world?
• Introduction to Red30 Tech
• Types of data
• Case study 1: Performance at a Miami location
• Case study 1: 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 Questions

• Guidelines for formulating questions
• Crafting better questions
• Case study 2: What is the right question?
• Role of business acumen

4. Unlocking the Data Within

• Data-collection issues
• Case study 3: Unclean data
• Case study 3: Explanation
• Data failure: When data is just wrong

5. Understanding Averages

• Nature of averages
• Case study 4: Conversion rates and benchmark
• Case study 4: Explanation
• Context is everything

6. Sampling

• Pros and cons
• Case study 5: Social media survey
• Case study 5: Explanation
• Case study 5: Statistical deep dive

7. What Is Cherry Picking?

• What is cherry picking?
• Case study 6: Revenue
• Case study 6: Explanation

8. Forecasting

• Hurricane Matthew
• Case study 7: Forecasting customer complaints
• Case study 7: Explanation
• Issues to consider

9. Correlation versus Causation

• Cause and effect
• Case study 8: Boston revenue
• Case study 8: Explanation
• Causal questions

10. Continuing Your Learning Journey in Data Analytics

• Continuing your learning journey in data analytics