Only 24% of the global workforce is confident in their ability to read, work with, analyze, and argue with data. As a result, corporations and government agencies are scrambling to make their staff data literate, and the organization’s desire to expand their data literacy significantly has never been greater. This webinar will explore the foundations of data literacy, its essential skills, and major challenges.
- 9 Common Data Analysis Mistakes
- Vague or incomplete objectives
- Data is incomplete or inaccurate
- Data is misunderstood
- Bias
- Data dredging
- Unequal comparison
- Correlation may not equal causality
- Relying on summary metrics
- Using the wrong visualization
