[Coursera] Course 6. Share Data Through the Art of Visualization

WEEK 1 - Data visualization

[Definition]

-Data visualization: The graphic representation and presentation of data

-Data composition: Combining the individual parts in a visualization and displaying them together as a whole

-Design thinking: A process used to solve complex problems in a user-centric way

[The McCandless Method]

• Information: the data you are working with
• Story: a clear and compelling narrative or concept
• Goal: a specific objective or function for the visual
• Visual form: an effective use of metaphor or visual expression

 

source: Coursera Google data analytics certificate course

 

[Kaiser Fung's Junk Charts Trifecta Checkup]

1. What is the practical questioin?
2. What does the data say?
3. What does the visual say?

[Pre-attentive attributes: marks and channels]

-Pre-attentive attributes: Elements of a data visualization that people recognize automatically without conscious effort

-Mark: Basic visual objects like points, lines, and shapes. Every mark can be broken down into four qualities.

1. Position: Where a specific mark is in space in relation to a scale or to other marks
2. Size: How big, small, long, or tall a mark is
3. Shape: Whether a specific object is given a shape that communicates something about it
4. Color: What color the mark is

-Channels: Visual aspects or variables that represent characteristics of the data. Channels are basically marks that have been used to visualize data. Channels will vary in terms of how effectively they are at communicating data based on three elements.

1. Accuracy: Are the channels helpful in accurately estimating the values being represented?
2. Popout: How easy is it to distinguish certain values from others?
3. Grouping: How good is a channel at communicating groups that exist in the data?

[Design principles]

• Choose the right visual
• Optimize the data-ink ratio
• Use orientation effectively
• Numbers of things
• Color

[Common errors to avoid]

• Cutting off the y-axis
• Misleading use of a dual y-axis
• Artificially limiting the scope of the data
• Problematic choices in how data is binned or gouped
• Using part-to-whole visuals when the totals do not sum up appropriately
• Hiding trends in cumulative charts
• Artificially smoothing trends

If a visualization looks confusing then it probably is confusing.

 

 

https://informationisbeautiful.net/

Information is Beautiful

https://informationisbeautiful.net/beautifulnews/

Beautiful News

https://www.amazon.com/Street-Journal-Guide-Information-Graphics/dp/0393072959

Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures: Wong, Dona M.: 9780393072952: Amazon.c

https://visme.co/blog/best-data-visualizations/

The 30 Best Data Visualizations of 2023 [Examples]

 

[Elements of art]

• Line
• Shape
• Color - Hue, Intensity(bright, dull), Value(light, dark)
• Space
• Movement

[Nine basic principles of design]

source: Coursera Google data analytics certificate course

 

There's a fine line between attracting attention and distracting the audience.

[Elements for effective visuals]

• Clear meaning
• Sophisticated use of contrast
• Refined execution

[Five phases of the design process]

• Empathize: Thinking about the emotions and needs of the target audience for the data visualization
• Define: Figuring out exactly what your audience needs from the data
• Ideate: Generating ideas for data visualization
• Prototype: Putting visualizations together for testing and feedback
• Test: Showing prototype visualizations to people before stakeholders see them

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WEEK 2 - Data visualization with Tableau

[Definition]

-Dynamic visualizations: Visualizations that are interactive or change over time

-Diverging color palette: Displays two ranges of values using color intensity to show the magnitude of the number and the actual color to show which range the number is from

 

프로필 - grow.with.google | Tableau Public

Google Career Certificates - Profile | Tableau Public

 

더 알아보기 | Tableau Public

Discover

 

Join Your Data - Tableau

Join Your Data

 

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WEEK 3 - Stories about your data

[Definition]

-Dashboard: A tool that organizes information from multiple datasets into one central location for tracking, analysis, and simple visualization

-Dashboard filter: A tool for showing only the data that meets a specific criteria while hiding the rest

-Data storytelling: Communicating the meaning of a dataset with visuals and a narrative that are customized for each particular audience

-Engagement: Capturing and holding someone's interest and attention

-Spotlighting: Scanning through data to quickly identify the most important insights

[3 data storytelling steps]

1. Engage your audience 
2. Create compelling visuals
3. Tell the story in an interesting narrative

[Essential questions for visualization]

• What role does this audience play?
• What is their stake in the project?
• What do they hope to get from the data insights I deliver?
• Choose your primary messages

[Checklist for slideshow]

• Include a good title and subtitle that describe what you're about to present.
• Include the date of your presentation or the date when your slideshow was last updated.
• Use a font size that lets the audience easily read your slides.
• Showcase what business metrics you used.
• Include effective visuals (like charts and graphs).

[Checklist for presentation]

• Use an attention-grabbing opening.
• Start with broad ideas and later talk about specific details.
• Speak in short sentences.
• Pause for five seconds after showing a data visualization.
• Pause intentionally at certain points.
• Keep the pitch of your voice level.
• Stand still and move with purpose.
• Maintain good posture.
• Look at your audience (or camera) while speaking.
• Keep your message concise.
• End by explaining why the data analysis matters.

Storytelling = visualization + narrative + context

Great visuals don't leave room for interpretation because the meaning is instantly understood.

 

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WEEK 4 - Developing presentations and slideshows

[Definition]

-Hypothesis: The theory you're trying to prove or disprove with data

[The McCandless Method]

1. Introduce the graphic by name.
2. Answer obvious questions before they're asked.
3. State the insight of your graphic.
4. Call out data to support that insight.
5. Tell your audience why it matters.

[Examples of good slide deck]

Demystifying Value Presentation.pptx

2.37MB

 

https://www.failory.com/pitch-deck/airbnb

The Pitch Deck Airbnb Used to Raise $600K

[Tips and tricks to present your data and results]

 

Sharing-your-data-findings-in-presentations_Tips-and-Tricks.pdf

0.13MB

Tip 1: Know your flow

  - Who is my audience?

  - What is the purpose of my presentation?

 

Tip 2: Prepare talking points and limit text on slides

  - Make it easy for your audience to skim read the slides while still paying attention to what you are saying.

  - 5 second rule: your audience should not be spending more than five seconds reading any block of text on a slide.

 

Tip 3: End with your recommendations

  -  Use one slide for your recommendations at the end. Be clear and concise.

  - If you are recommending that something be done, provide next steps and describe potential successful outcome.

 

Tip 4: Allow enough time for the presentation and questions

  

[Slide deck layout]

source: Coursera Google data analytics certificate course

 

1. Agenda: Provide a high-level bulleted list of the topics you will cover and the amount of time you will spend on each.
2. Purpose: Summarize the purpose of the project and why it is important to the business for your audience.
3. Data/Analysis: Have a logical order of slides to fully build the story. Dont' use too much text on the slides.
4. Recommendations: Be ready to communicate how your data backs up your recommendations in different ways.
5. Call to action: Recall the purpose and connect with it.

[Ways to be prepared to consider any limitations of your data]

• Critically analyze the correlations
• Look at the context
• Understand the strengths and weakness of the tools

[Types of objections]

- About the data

• Where you got the data?
• What systems it came from?
• What transformations happened to it?

- About your analysis

• Is your analysis reproducible?
• Who did you get feedback from?

- About your findings

• Do these findings exist in previous time periods?
• Did you control for the differences in your data?

[Responding to possible objections]

• Communicate any assumptions
• Explain why your analysis might be different than expected
• Acknowledge that those objections are valid and take steps to invesigate further

[What to do when answering questions during a presentation]

• Listen to the whole question
• Repeat the question (if necessary)
• Understand the context
• Involve the whole audience
• Keep your responses short and to the point