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Data Literacy: Cultivating Skills to Engage with Data

The ability to find, analyze and utilize existing data helps us interpret, engage with and critique the world around us. We are living in a time when our behaviors and actions are increasingly viewed as data points, and when recent court rulings and laws have ignited larger conversations about the ways in which the most personal of data might be utilized for prosecution. Data is valuable, and profitable, and can be illuminating. But data is also imperfect, and can be biased, and used as a weapon against vulnerable populations.

In this class, we will take a critical approach to learning about data literacy. Data literacy refers to the skills needed to find, read, curate, analyze, and communicate with data. This includes self-reported data (like demographics, hate crime statistics and responses within focus group), observational and trace data (like web searches and street traffic patterns), and experimental data (like health outcomes in vaccine clinical trials).  

This class aims to equip students from a variety of non-technical backgrounds with the necessary skills to think critically about quantitative and qualitative data. The class approaches data literacy as part of a broader process of inquiry into the world – not from a math or statistics-centric point of view. Students in this course will end the semester with a better understanding of the various ways that data is used- and perhaps in some cases, shouldn’t be used- to inform advocacy, science, civics, and policy.

Upon completing the course, students will be able to:

  1. Describe the strengths and limitations of the following data types: 1) self-reported data, 2) observational or trace data, and 3) experimental data.
  2. List at least two real-world examples that demonstrate the harmful consequences of “data-driven decision-making” and describe solutions to mitigate bias and harm within such decision-making.
  3. Identify three sources for locating open and reusable datasets and locate one dataset based on student interest.
  4. Apply FAIR data principles to evaluate a dataset.
  5. Utilize open-source, web-based tools for simple data cleanup & analysis.


ALS 1210 information and registration: