Jargon Busting

Requirements

  • Boards/pads
  • Pens
  • Sticky notes
  • Internet (optional)

Objectives

  • Icebreaker
  • Finding confidence level
  • Explain terms, phrases, and concepts associated with terms, phrases, or ideas around data
  • Compare knowledge of these terms, phrases, and concepts
  • Differentiate between these terms, phrases, and concepts

Task

This group task is an opportunity for you to get help understanding terms, phrases, or ideas around code or software development in libraries that you might have come across and perhaps feel you should know better. This is a safe place to ask these kinds of questions, and to get a firmer grasp on the concepts we might be discussing later.

Depending on numbers and layout, we will be separating into groups of 4-6.

Part A: 25 minutes

  • Start by getting into pairs.
  • Talk for 3 minutes (your instructor will be timing you!) on any terms, phrases, or ideas around code or software development in libraries that you’ve come across and perhaps feel you should know better.
  • Get into groups of 4-6.
  • Make a list of all the problematic terms, phrases, and ideas each pair came up. Retain duplicates. Then - taking common words as a starting point - spend 15 minutes working together to try to explain what the terms, phrases, or ideas means (note: use both each other and the internet as a resource!). Make a note of those your group resolves and those you are still struggling with.

The instructor will collate these on a whiteboard and facilitate a discussion about what we will cover today and where you can go for help on those things we won’t cover.

If stuck for ideas, you could choose from the following set of terms to trigger discussions:

Terms Terms Terms
Dataset Pivot table Data science
Group Formula Average
Algorithm Big data Outlier
Open data Aggregation Anonymisation
Regular expression Data mining Data protection

B: Feedback 10 mins

Each group then reports back on one issue resolved by their group and one issue not by their group.

Key points

  • It helps to share what you know and don’t know about data and data-science jargon.

Source Material

Library Carpentry Data Intro for Librarians

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