Data Literacy: Exploring and Describing Data

Course details

Data analysis isn’t just for specialists who need to make sense of massive datasets. Decision-makers in every industry can benefit from a basic understanding of the goals and concepts of applied data analysis. In this course, Barton Poulson focuses on the fundamentals of data fluency, or the ability to work with data to extract insights and determine your next steps. Barton shows how exploring data with graphs and describing data with statistics can help you reach your goals and make better decisions. Instead of focusing on particular tools, he concentrates on general procedures that can help you solve specific problems. Barton covers how to prepare and adapt data, explore it visually, and use statistical methods to describe it. He goes in depth on probability and interference and also touches on data ethics and explainable AI.
Learning objectives
Prepare data for analysis.
Adapt data to help extract insights.
Use data to solve problems and make better decisions.
Describe data to others by using visualizations.
Define probability and inference.

Concepts

Introduction

Make better decisions with your data

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

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

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

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

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
Describe associations with correlations
Effect size for correlation and regression
Exploring tables
AI-assisted data exploration and modeling
Predict scores with regression

Probability and Inference

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

Continuing Your Data Fluency Learning Quest

Next steps and additional resources