Data Literacy: Exploring and Describing Data in an AI World

Introduction

• Make better decisions with your data

1. Think with Data

• The meaning of data fluency
• Data fluency is for everyone
• Data fluency in practice
• Making intuitive thinking explicit
• Thinking about causes
• How to develop data fluency
• Data-driven decision-making
• ROI and the 80/20 rule for data fluency
• Putting data in context
• Data literacy in the age of generative AI and agentic AI

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

3. Adapt Data

• Sort data
• Filter data
• Combine and split categories
• Code text
• Calculate sums and means
• Calculate rates
• Calculate ratios
• Adjust ratios in practice
• AI-assisted data preparation

4. 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
• Data maps

5. Describe Data

• Numerical descriptions
• Describe measures of center
• Describe variability with the range and IQR
• Describe variability with the variance and standard deviation
• Rescale data with z-scores
• Interpret z-scores
• Describe group differences with effect sizes
• Predict scores with regression
• Describe associations with correlations
• Effect size for correlation and regression
• Exploring tables
• AI-assisted data exploration and modeling

6. Probability and Inference

• Basic probability
• Conditional probability
• Expected values
• Sampling variation
• Inference as describing populations
• AI as an additional source of analytical variability

7. Continuing Your Data Fluency Learning Quest

• Next steps and additional resources