All Data Are Local

Introduction to Data Literacy

This course introduces data literacy for Data Design + Art. It starts from a simple idea: data are not given. People make data through observation, selection, classification, measurement, storage, and design.

NoteThe three questions of this course
  • What do we gain when we turn the world into data?
  • What do we lose?
  • Who makes these decisions?

The course in one look

The course runs on two days, with self-study in between.

flowchart TD
    A["<b>Day 1</b><br/>5 October 2026<br/>How does the world become data?"] --> B["<b>Self-study</b><br/>Statistics reading<br/>Bring your own dataset"]
    B --> C["<b>Day 2</b><br/>12 October 2026<br/>How can we work with data critically?"]
    C --> D["<b>After Day 2</b><br/>Describe your dataset"]

Both days hold six sessions of 60 minutes, so the course gives 12 hours of contact time. It belongs to the module PLAY 1 / Data Literacy in Data Design + Art. Dr. sc. Moritz Mähr teaches it.

The core text is Yanni Alexander Loukissas’s All Data Are Local (2019). We read the Introduction and Chapters 1 and 2 on Day 1. We read Chapters 6 and 7 on Day 2.

Between the two course days, you study selected sections of Introduction to Modern Statistics (2e) (2024). These sections give you the basic statistical language for observations, variables, samples, distributions, and common charts. Day 2 starts with a short multiple-choice quiz on this material.

You also bring your own dataset. You use it to connect statistical description with critical data practice.

The course counts three forms of evidence of learning: your work in close reading and discussion, the quiz, and a written description of your own dataset.

Learning outcomes

After the course, you can:

  • distinguish a dataset from its data setting;
  • explain how observations and categories become data;
  • identify the origin, purpose, limits, and missing context of a dataset;
  • use basic statistical terms to describe a dataset;
  • explain how place, institutions, tools, and interfaces shape data;
  • describe a dataset in a clear, critical, and responsible way.

How to use this site

  1. Read the course description before Day 1.
  2. Follow the syllabus. Each session has its own page with the reading and the output.
  3. Complete the assignments between the two days.
  4. Use the statistics cheat sheet while you study.
ImportantBefore 12 October 2026

Complete two tasks before Day 2.

  1. Read the ten required sections of Introduction to Modern Statistics (2e).
  2. Bring one dataset that you can inspect in class.

Day 2 starts with a short multiple-choice quiz on the required sections. The quiz checks concepts, not complex calculations. See the assignments for the full list.

License

Course content is available under CC BY 4.0. Code is available under AGPL 3.0. Cite this course as described in CITATION.cff.

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