Module 5: Library data literacy workshop

This workshop is a general-audience introduction to reading and questioning a real dataset, designed for a public or academic library setting rather than a credit-bearing course. Participants need no background in finance, statistics, or computers beyond comfort using a web browser. Working entirely in a no-code sandbox, they explore a dataset of auction and resale transactions for watches, jewelry, handbags, and other luxury goods, drawn from over 100 auction houses with history to the late 1990s, and practice the basic moves of data literacy: asking a question, finding the right table and fields, running a filter, and reading the result honestly, including its limits.

Target course(s) and level

Public library workshop series, academic library continuing-education session, or community data literacy program. Appropriate for adult patrons and lifelong learners with no assumed background; also usable as a library instruction session for undergraduates outside a formal course.

Learning objectives

By the end of this session, participants will be able to:

  1. Navigate a real, professionally maintained dataset using a point-and-click browser tool, with no coding required.
  2. Explain what a data dictionary is and use one to understand what a field in a dataset actually measures.
  3. Filter a dataset to answer a simple, self-generated question (for example, what has a particular brand sold for recently).
  4. Read a chart built from filtered data and describe, in plain language, what it does and does not show.
  5. Identify at least one reason to be cautious about a data-driven conclusion, such as an incomplete category or an unstable recent period.

Prerequisites

None. Comfort using a web browser and typing into a search box is sufficient. No prior exposure to spreadsheets, databases, or SQL is assumed; the sandbox's pre-built charts and filters are used in place of any query language.

Materials and access needed

  • Sandbox access at sandbox.altfndata.com. Each participant self-registers with an email address (a personal email is acceptable for this general-audience session, though a work or school email speeds automatic approval); library staff should confirm in advance whether the venue's guest wifi and registration flow work smoothly together.
  • A projector or large screen for the facilitator demo, plus library computers or personal laptops for participants to follow along.
  • The coverage browser and data dictionary tabs, which are the primary tools used in this workshop rather than the SQL editor.
  • No instructor class API key or coding tools are needed for this module.

Session outline (60 minutes)

  • 0 to 10 min: Welcome and framing. What is a dataset, and why would anyone want to look at auction records for watches or handbags.
  • 10 to 20 min: Sandbox orientation. Everyone registers and opens the sandbox together; facilitator walks through the homepage, coverage browser, and data dictionary.
  • 20 to 40 min: Guided demo. Facilitator walks the group through filtering one brand's sold items and building a chart.
  • 40 to 50 min: Participant exploration. Each person picks a brand or category they are curious about and filters to it themselves, with facilitator circulating to help.
  • 50 to 60 min: Group share-out and discussion questions.

In-class demo (sandbox-first, no code)

  1. Open sandbox.altfndata.com and register with an email address. Approval happens automatically, so everyone should be in within a minute or two.
  2. Click the coverage browser tab. Type a familiar brand name, such as a well-known watch or handbag maker, into the search box and note how many records and vendors appear.
  3. Click the data dictionary tab and find the entries for sale_date, usd_price_decimal, and status. Read the plain-language description of each aloud, so participants understand exactly what they are about to see.
  4. Return to the coverage browser or filter panel and narrow the dataset to that same brand, restricting to sold items only.
  5. Look at the resulting list of items: note the item title, the sale date, and the realized price in US dollars for a handful of entries.
  6. Open the pre-built charts tab and select a chart that shows realized price over time for the filtered brand.
  7. Ask the group: what does this chart tell you, and what does it not tell you? Point out that the chart shows what buyers paid at auction, not the brand's retail price or overall popularity.
  8. Use the CSV export button to show participants how they could save this filtered view for their own reference, and note that the same filtered view is available any time they log back into the sandbox.

Datasets and queries used

Dataset: handbags data or watches data, accessed through the sandbox's coverage browser and filter panel rather than the SQL editor. For facilitators who prefer to demonstrate the underlying SQL editor as a bonus (optional, not required for this audience), the equivalent filter is:

SELECT designer, item_title, sale_date, usd_price_decimal
FROM all_handbags_data
WHERE designer LIKE '%Hermès%'
  AND status = 'sold'
ORDER BY sale_date DESC
LIMIT 25;

Fields referenced during the workshop (all shown through the coverage browser and data dictionary, in plain language): designer, item_title, sale_date, usd_price_decimal, status, vendor.

Discussion questions

  1. What surprised you about what this dataset actually contains, compared to what you expected before we opened it?
  2. Why does it matter whether a listed price was what an item actually sold for, versus what a seller merely asked for it?
  3. What questions would you want to ask about how this data was collected before trusting a conclusion drawn from it?
  4. If you saw a chart showing prices rising for a brand over the last few months, what would make you trust that trend, and what would make you doubt it?
  5. How is looking at real auction records different from reading a news article that summarizes a market trend?
  6. What is one everyday decision, unrelated to finance, where being able to filter and read a real dataset yourself would be useful?

Homework assignment

This module does not carry a graded homework assignment, since it is designed for a general-audience library setting. Facilitators who want an optional take-home activity can ask participants to return to the sandbox on their own, pick a category they did not explore in the session (for example, fine art or wine and whisky if the session covered handbags), and bring one observation and one question about the data to a follow-up session or a brief written note left with library staff. There is no formal deliverable, and the activity is intended to reinforce independent, unsupervised comfort with the sandbox rather than to produce a graded product.