Lecture 10: Data Literacy & Communication

Autumn 2026

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Sli.do

#cse163

Questions?

Icebreaker

Favorite snack?

CSE 163

CSE 163: Data Literacy & Communication

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Announcements

• Homework 3 now available, due Thursday, October 29 at 11:59pm on Gradescope
• Programming Practice 3 due Sunday October 25 at 11:59pm on Gradescope
• Project Part 1: Proposal is due Tuesday, October 27 at 11:59pm on Gradescope
• Information now on the course website here.

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Questions?

CSE 163: Data Literacy & Communication

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Does code tell us anything about data?

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

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data = pd.read_csv('home/data.csv')

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data = data[['name', 'fin_length', 'age']]

data = data.dropna()

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sns.relplot(data=data, kind='line', x='age’, y='fin_length', hue='name')

plt.title('Shark Ages vs. Fin Length')

plt.xlabel('Age (months)')

plt.ylabel('Fin Length (in)')

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plt.savefig('/home/plot.png')

CSE 163: Data Literacy & Communication

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Data Context Guiding Questions

• Who: the people represented in the data or responsible for its collection

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• What: the information that is represented in the data such as demographics and measurements

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• When: the time frame associated with data collection or represented in the data

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• Where: the location of the data, virtual or otherwise

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• Why: the stated (or unstated) purpose of collecting the data

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• How: the methods used for data collection and storage

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CSE 163: Data Literacy & Communication

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Data context can reveal other explanations

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Where do we find this context?

• Data dictionaries
• README.md or CHANGELOG.md files
• Meta datasheets
• Contacting the researchers/data collectors

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Data Storytelling: Let’s talk about Multiverses

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CSE 163: Data Literacy & Communication

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Shark Multiverse…

• A set of bar graphs illustrating different categorical qualities of different shark species
• A YouTube tutorial using this dataset to demonstrate how to drop or replace missing values
• A neural network using all columns to predict whether the shark is endangered or not
• A neural network using all columns to predict shark tail size
• A single string representing the most common shark species in the dataset

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CSE 163: Data Literacy & Communication

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Narrative Plot Mountain

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Exposition

Rising Action

Climax

Falling action

Resolution

CSE 163: Data Literacy & Communication

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Data Story Plot Mountain

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Finding data

Posing research

questions

Pre-processing

Coding

Analysis to answer questions

Testing

Visualizations

Writing

reports

Interpreting results

Presenting

findings

Communications

CSE 163: Data Literacy & Communication

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Finding Main Characters

• Make sure the questions are relevant to your data and the data is relevant to your questions
• Ask questions that do not have simple answers
• Use your domain knowledge to come up with interesting questions
• Pacing matters!

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Writing Tips

• We care more about what you have to say rather than how you say it!
• Keep your writing relevant and to the point.
• Some strategies for answering your research question:
• Answer a question, then explain that answer
• Answer the question, think about a counterexample/counterargument, then refute it
• Answer the question, pose a follow-up question, then answer the follow up.

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Avoiding the Vacuum

• Interpret any numbers and/or trends in context
• Do NOT leave free-floating numbers
• Don’t assume that your readers knows your code as well as you do! You may assume your reader knows general definitions.
• Based on which venue or setting you are presenting your work, you may tweak what you focus on or how you phrase things.
• Think about explaining your project to someone who is not in this class.

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Sentence Level Details

• A few mechanical things to think about:
• Writing in the first-person (“I/We did X for my/our analysis to find Y” as opposed to “X was used to find Y”)
• Active voice vs. passive voice (“The result of A proved B” vs. “B was proven by A”)
• Present vs. past-tense.
• Using abbreviations or shorthand instead of full names.

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Key Takeaways

• Think of how your analysis might be used or interpreted
• Consider biases in your data and analysis – even the ones that might come from you!
• How do these impact the generalizations of your data analysis?
• Consider the impacts, ethics, and consequences of your analysis.
• Data science tells a story – who is our “main character” and what do we want to focus or highlight?

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