Data Fluency: Exploring and Describing Data (2019)

Learn how anyone, in any industry, can speak the language of data analysis. Find out how to prepare data, explore it visually, and describe it using statistical methods.

Introduction

• Gather greater insight and make better decisions with your data

1. Think with Data

• The meaning of data fluency
• Data fluency is for everyone
• Data fluency in practice
• Make intuitive thinking explicit
• Think about causes
• How to develop data fluency
• Data-driven decision-making
• ROI and the 80/20 rule for data fluency
• Put data in context

2. Prepare Data

• Data ethics
• Use in-house data
• Use open data
• Gather new data
• Use third-party data
• Assess the quality of data
• Assess the generalizability of data
• Assess the meaning of data
• Assess the ambiguities in data
• Adapt data: Coding text
• Adapt data: Sums and means
• Adapt data: Rates
• Adapt data: Ratios
• Adjust ratios in practice

3. Explore Data

• Visual primacy: The importance of starting with pictures
• Bar charts
• Grouped bar charts
• Pie charts
• Dot plots
• Box plots
• Histograms
• Line charts
• Sparklines
• Scatterplots

4. Describe Data

• Numerical descriptions
• Describe measures of center
• Describe variability with the range and interquartile range (IQR)
• Describe variability with the variance and standard deviation
• Rescale data with z-scores
• Interpret z-scores
• Describe group differences with effect sizes
• Interpret effect sizes
• Predict scores with regression
• Describe associations with correlations
• Effect size for correlation and regression

Conclusion

• Next steps