4 Data Literacy

Welcome to our Module on Data Literacy!

Estimates vary, but here are a couple interesting findings:

  • 28% of us are data literate in the US
  • About 74% of us feel overwhelmed or unhappy when working with data

(Read more here)

Let’s do something about that!

In this module, we will learn about data literacy, including:

  • Definitions & Buzzwords
  • Data pitfalls
  • Tidy data
  • A bit about statistics and charts/visualizations

We also have a bit about math fear & feelings. We believe in having a growth mindset and we believe you can feel more confident about working with your data.

 

We encourage you to do as many of the activities as you can. If you are short on time, remember that your requirements for this module are:

  • Submit your reflection on data literacy
  • Read/watch some of the resources provided

 

Learning Goal

At the end of this module, you will be able to :

  • Discuss data literacy terminology, skills and pitfalls
  • Use the principles of tidy data, descriptive statistics, and charts/visualizations

 

Let’s dig in!

 

What Do We Mean by Data Literacy?

Gartner (a respected research firm) says:

Data literacy is the ability to read, write and communicate data in context, with an understanding of the data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case application and resulting business value or outcome. (https://www.gartner.com/en/information-technology/glossary/data-literacy)

 

Also, skim this nice overview here:

Data Literacy for the Data-phobic: 7 Things Beginners Need to Know – https://venngage.com/blog/data-literacy/

And if you want more: 10 Data Literacy Skills to Become a Data Citizen – https://quanthub.com/dataliteracyskills/

(Note that there are some typos and grammar problems in this resource. Please just focus on what is useful in the content.)

 

Reflect on the following:

  • Did anything surprise you about the definition and/or the 7 things article?
  • Would you call yourself data literate? Reflect on your strengths and weaknesses with data. Where do you shine? Where could you use some practice?

 

Homework for our First Session

We recognize that we are all at different places with our data literacy, so you get to pick what resources seem most relevant to you.

Please read or watch at least two of the following sections in preparation for our live session:

  • Data Buzzwords (optional)
  • Math fear & feelings -> Growth mindset (optional)
  • Avoiding data pitfalls (recommended)
  • Tidy data (recommended)
  • Do I need to know statistics? (recommended)
  • Back of the Napkin (recommended)
  • A few more related topics (optional)

 

Data Buzzwords (optional)

Skim through:

Consider these questions:

  • Which terms are the most relevant to our work in libraries?
  • Which make you want to learn more?

Bonus – These are terms that might not be familiar to librarians and examples of how they show up related to Ex Libris . . .

1 – AI – https://cdn2.hubspot.net/hubfs/2909474/Ex%20Libris%20Artificial%20Intelligence%20White%20Paper.pdf

2 – SQL – https://developers.exlibrisgroup.com/blog/alma-analytics-sql-filter-examples/

3 – XML

4 – JSON

 

Math Fear & Feelings -> Growth Mindset (optional)

Read/watch:

Consider these questions:

  • How would you rate your feelings about math? (Where 1 is severe math anxiety and 10 is loving math)
  • What do you fear most when it comes to math and data?

In our discussions, we had ups and downs in our experiences with math and saw these two things:

  1. We can have strengths and weaknesses in different types of math. (Johnna loved geometry, but not advanced algebra. Jill rocks it with statistics, but not calculus.)
  2. The attitudes of our teachers and family members mattered. If we had a champion, that really helped.

Do you find these to be true for you as well?

 

Avoiding Data Pitfalls (recommended)

Here is a checklist you could use to look for possible problems in a data project: https://dataliteracy.com/avoiding-data-pitfalls/

Consider these questions:

  • What pitfalls have you experienced?
  • How will you handle it if/when you make a mistake?

You might like this book as a reference: Jones, B. (2020). Avoiding data pitfalls: How to steer clear of common blunders when working with data and presenting analysis and visualizations. Wiley.

 

Tidy Data (recommended)

Read:

  • The 7 sections of Tidy Data for Librarians: https://librarycarpentry.org/lc-spreadsheets/ (Bonus points if you do the exercises within the module!) We admit Tidy Data is long, but it is important! Please do skim through the 7 sections.

Bonus – Have you seen this quote? “80% of a data analyst’s time is spent on data cleaning.” While there is debate about the percentage, it is true that a lot of time is spent on data cleaning.

Tidy Data Top 10 Key Points:

  1. Don’t modify the original data.
  2. Keep a log of what you did step by step.
  3. Each row is an observation.
  4. Each column is a variable.
  5. Each cell has only one piece of data. Great picture summarizing the last three points found in Figure 12.1 here: https://byuidatascience.github.io/python4ds/tidy-data.html
  6. CSV
  7. Don’t use formatting to convey info.
  8. Don’t use spaces in names.
  9. Bad values often sort to the top and bottom.
  10. Pay special attention to dates. Often the best strategy is year, month, and day in separate columns.

Consider these questions:

  • How comfortable do you feel with spreadsheets?
  • What is one way you are going to make/keep your data tidier?

 

Do I Need to Know Statistics? (recommended)

Read/watch:

 

Consider these questions:

  • What stats have you used?
  • What will you use next?

 

Back of the Napkin (optional)

Read the book, The Back of the Napkin: Solving Problems and Selling Ideas with Pictures by Dan Roam or watch this video: https://youtu.be/XEnrQqOHx3I

Consider these questions:

  • Which kinds of visualizations are most useful in libraries?
  • What would appeal most to your administrators?

 

A Few More Related Topics (optional)

Read:

 

Consider these questions:

  • How many decimal places would we use to report results from a library survey?
  • What other research/data terms have you heard where you aren’t sure of the meaning or if it applies to our work with data in libraries?

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Data Literacy Intensive for Librarians Copyright © by PALS is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted.

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