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DATA

Katie Akateh

June 6th, 2025

LITERACY

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Goals

Understand what it means to be “data literate” and why it’s important.
Define Data and describe types of data.
Know the steps of working with data in research.
Learn best practices related to data visualization.

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Take the Quiz -

How Data Literate are you?

The Data Literacy Project -

Data Literacy Assessment

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Data literacy & why it’s important

DATA LITERACY = THE ABILITY TO READ, EXPLORE, UNDERSTAND, AND COMMUNICATE DATA TO MAKE DECISIONS AND SOLVE PROBLEMS

"The world’s most valuable resource is no longer oil, but data."

--The Economist, 2017

Data literacy is a continuum

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Data literacy -

Technical and Non Technical Skills

Non-Technical Data Skills

Technical Data Skills

Analysis
Visualization
Data Management
Mathematics/ Statistics
Programming Languages

Critical Thinking
Curiosity
Subject-Area Knowledge
Communication
Problem-Solving Skills

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What is data?

DATA* = FACTUAL INFORMATION THAT IS SYSTEMATICALLY RECORDED AND ANALYZED TO ANSWER A QUESTION

*Definition may vary by discipline

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Data comes in many different forms!

Harvard College Alcohol Survey 2001

Darwin’s finches from the Galapagos Islands (beak adaptation to specific types of foods present on different islands inspired Darwin’s theory of evolution by natural selection)

Rosalind Franklin’s x-ray diffraction image of crystalized DNA (evidence of a double helix structure)

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Types of data

Quantitative data deals with quantities, i.e., information that can be counted, measured, or otherwise expressed using numbers

Summarized and analyzed using traditional statistics or related methods

Mixed data combines quantitative and qualitative data

Analyzed using mixed (quantitative and qualitative) methods

Qualitative data deals with qualities or characteristics, i.e., information that is descriptive nature, and cannot be easily expressed in numbers. Such as Country of Origin, gender, name, hair color.

Sources of qualitative data: text documents, interview transcripts, images, audio and video recordings, other
Requires qualitative summary and analysis (not statistics)

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Activity - What Type of Data

https://www.menti.com/al4f8s8cfkhu

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Activity

Tell us about your project.

What types of data are you planning to use?

Where do you plan to search for them? (Who may have already collected the data, how, and why?)

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Steps of working with data

Research Data Lifecycle. Adapted from UK Data Service Model 2017.| Source Queensland University of Technology, Advanced Information Research Skills

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Plan: Formulate a question or hypothesis

What do we (people) already know about the topic? (literature review)
What do you want to know?
What do you expect to find (e.g., pattern of results, differences between groups or conditions, cause and effect), and why?

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Collect & Capture Data

Two primary approaches:

Collect new data
Find an existing dataset 🡺 OPEN DATA

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Strategies to find data

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3 strategies to find open data

There are many open-access, publicly available datasets online that you can use!

Find an established data repository, and search for a dataset by topic or other attributes.
Find a published research article and locate the original dataset used.
Think about who has an interest in collecting this information.

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Activity

Tell us about your project.

What types of data are you planning to use?

Where do you plan to search for them? (Who may have already collected the data, how, and why?)

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3. Process your Data

Even if you didn’t collect the data, understanding the data is critical to interpreting the results!

How was the data collected? Who collected it? When and where? With what measures or instruments? Using what study design? Primary research question of the study? Source of funding?
Which variables will you look at to answer your question or test your hypothesis? (The Codebook is your friend.)

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Data Wrangling (Cleaning) Tips

Tips: Document all the changes you make to your data files, no matter how small, so you (or someone else) can repeat/ replicate your processing steps, your analyses, and ultimately your results

Save your working data file with a new name; keep the original secure
Consider the tool you will use for data analyses or visualizations – and structure your data for that tool
Check for missing data, and decide how to deal with it
Be careful and consistent at each step to avoid errors

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4. Analyze

This involves applying statistical or mathematical techniques to the data to discover patterns, relationships, or trends.

The goal is to find the right analysis or the right visualization

to answer your question or test your hypothesis.

Things to consider:

Type of data (e.g., quantitative, qualitative, etc.)
Limitations of the data
Tool/s you intend to use (e.g., statistical software)
Be as simple as you can be – but no simpler

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Break

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5. Visualize

After the data is analyzed, the next step is to interpret the results and visualize them in a way that is easy to understand.

Data visualization helps to make complex data more understandable and provides a clear picture of the findings.

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Activity

Share your visualization

What is your visualization describing?

What do you notice?
What do you wonder?

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Visual Design Principles

The visual design of your charts is about emphasis, consistency, and clarity!

Selecting Chart type (and some to avoid)
Color
Text
Accessibility

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"Data visualization is part art and part science. The challenge is to get the art right without getting the science wrong and vice versa." -- Claus Wilke

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Know Your Purpose

Are You?
Comparing categories?	Compare variables across categories.
Showing part to whole?	Relate the part of a variable to the total.
Explaining distribution?	Showing values in the dataset and how often they occur.
Describing relationships?	Show correlations among two or more variables.
Displaying change over time?	Emphasize changing trends. Can be short or long time periods.
Visualizing spatial data?	Relates data to geographies. Use when geographic locations are most important to audience.

Selecting a Chart: Consider your Purpose

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United States Transgender Survey, 2015 - National Center for Transgender Equality

Some Chart Types are difficult to Interpret

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Some Chart Types are difficult to Interpret

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Chart Type: Lengths are easier to interpret

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Color

Should have a purpose for the data
Consider your audience

sequential

diverging

qualitative

less

more

hot

cold

neutral

group 1

group 2

group 3

group 4

group 5

Color has a huge impact on how your visualization is interpreted. You can use color as the main way of representing your data, or as a highlight to showcase a particular data point or category.

There are several main ways colors is harnessed for visualization listed on this slide.

Make sure that as you choose colors, you consider your audience and the cultural implications of the colors you're choosing. Colors are tied to specific emotions, cultural events, and activities, but the associated colors are not the same everywhere. Make sure you are making intentional choices about your colors and what they mean. For example, in the US our political parties have well-established colors associated with them (red and blue). It could be misleading to use different colors to represent those two groups, so you should go with the cultural expectations. If you are representing gender, you might associate male and female with pink and blue. But is that the case in every culture? Who is your audience? Is this a place where you want to go with cultural expectations, or consciously choose different colors so that you don't accidentally create associations that aren't there?

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US Social Media Usage by Ann Pregler

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Text

Use brief, descriptive titles
Text should be horizontal, not vertical
Use callouts or icons for context
Use a large font size
Proportional, accurate axes

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Accessibility: Color Contrast

Color contrast: text colors should stand out against the background (at least 4.5:1 contrast ratio in a contrast checker)

2:1 ratio

4.6:1 ratio

14.2:1 ratio

US Social Media Usage by Ann Pregler

Your text color should stand out against your background color in every part of the graph, especially text. This is typically measured using a contrast ratio, showing the difference between the two colors. The Web Content Accessibility Guidelines (WCAG) 2.2 are the standard reference for what is acceptable for accessibility purposes. The WCAG guidelines say that contrast ratio should be at least 4.5:1 for text, and 3:1 for large text and visual elements, so large text and graphical elements can have a medium contrast ratio, but your text should always have high contrast. High contrast combination would be black text on a white background or white text on a navy blue background. Low contrast would be yellow text on a white background, or white text on a light grey background. It's very difficult to tell by looking if colors meet the 4.5:1 guidelines, so you can use a tool like the WebAIM contrast checker.

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Accessibility: Color Combinations

Accessibility: Color

Color combinations: avoid combinations that will appear too similar to color-blind users:

Red and green
Blue and green
Yellow and red
Purple and red
Yellow and pink

and/or use more than just color to mark things (pattern, shade, saturation, labels).

Not everyone sees color the same way, so you also want to avoid color combinations that will not display correctly for viewers who are colorblind. The most common types of color-blindness are red-green and blue-yellow, but there are many variations that will make colors appear more or less similar to one another (National Eye Institute: Types of Color Deficiency). It's difficult to account for every combination when you're choosing colors, so the best way to check for this is to view your visualization in black and white (no colors) and see if you can still read the data. This is also a good test to make sure your visualization is readable if it's ever printed in black & white.

The best way to avoid color combinations is to use more than just color to show important information. For example, in a bar chart you could add data labels to the bars, add a pattern that corresponds to each color, or add shapes to tell the difference between different elements (WCAG guidelines on using color)

Learn more about color theory.

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The Dude Map by Jack Grieve and Diansheng Guo

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Accessibility: Alternative Text (alt text)

Accessibility: Alt Text

All images need descriptive alt text, including visualizations.

Provides information about images to people using screen readers.
Be concise but descriptive.
Include text from the image.
Context is important!
What does your audience need to know?

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Alt Text: example 1

A pie chart titled, "What are 5th Graders Reading?" that shows that 96.5% are reading fiction and 3.5% are reading non-fiction.

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A Nerd’s Guide To The 2,229 Paintings At MoMA By Oliver Roeder

A scatter plot

A scatter plot showing that MoMA keeps its collection current.

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Activity

Cat/Dog Data Visualization

Start asking questions of the data.
Construct a data visualization that

Answers a question.
Aligns with best practices.

Write alt text for your visual.

Let’s practice these data viz ideas!

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6. Share and Communicate Results

Go back to your initial research question or hypothesis – and now answer it with data

Consider your audience, their needs, interests, and level of knowledge, and how they will use the results

The goal is to tell a clear, accurate, logical, and compelling story with your data

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Thank you!