This module is meant to serve as the introductory class(es) in a data literacy course. It provides motivation for why data literacy matters, introduces key concepts such as measurements, variables, models, etc., and defines some basic descriptive statistics and data visualization techniques.

Module 2: Deep Dive into Data “Set”

Introduces students to the idea of a data “set” as well as some basic terms and statistical measures (e.g., mean, standard deviation, etc.) while highlighting important caveats and takeaways when drawing conclusions from these measures.

Module 3: Relating Data “Sets”

Introduces students to the idea of related data sets and describes how to merge data sets into one large data collection. Real data sets are analyzed in depth to demonstrate the benefits of building a nuanced, complete picture of the world.

Module 4: Plotting/Graphing

Introduces students to some common plots and graphs and discusses when to use each depending on the data and the context. It also defines and explores various distributions with analysis of their properties and examples from the real world.

Module 5: Correlations

Introduces students to the idea of correlation between two variables and describes how to observe, quantify, and label correlations, with a focus on linear correlations. Strategies for distinguishing real correlations from noise are provided and common pitfalls, such as correlation vs causation, are discussed in depth.

Module 6: Mapping

Introduces students to the idea of geographic data and discusses how to visualize and aggregate geographic data.

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