Data_Viz_CMU

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Class Work

Assignment

Final Project I

Final Project II

Final Project III

Class Work

This page documents my in class work, group sessions, and activities from Telling Stories with Data

Data visualization

Challenge Exercise: Data visualizations are everywhere!

This sculpture looks like a vertical dot plot a simple data visualization where dots are placed along a vertical line to show values from low to high. Each human figure on the pole represents a data point, and as they climb higher, they show an upward trend. Just like in a chart where higher position means a bigger value, the figures climbing toward the sky represent growth and progress over time.

Controlling Color - Week One (Discussion/Workbook)

Version 1

My first sketch uses a different color for each year of Afghanistan’s GDP growth data. While colorful, this approach creates visual noise without meaning. The eye jumps between 10 different colors with no clear takeaway. There is no story just data dressed up in a rainbow. This is exactly the problem the workbook warns against: color used for decoration rather than communication.

Version 2

For my redesign, I chose to tell one specific story: when the Taliban took over in August 2021, women lost their jobs at nearly double the rate of men. I used red to highlight women’s employment decline (-75%) and gray for men (-48%). Gray is not neutral by accident it pushes men’s data into the background so the focus stays on the more alarming number. I chose a horizontal bar chart because it makes the comparison immediate and easy to read for a non-expert audience. The title does the storytelling, the color confirms it, and the context note at the bottom connects it back to the broader GDP collapse.

In-class critique: Data visualization critique #1 (C4)

Group 3: We discussed about the map. The critiques are outlined in the sticky notes. Team members : Drucella Garcia, Emma Hoppough, Crystal Bayat.

Crafting for Clarity - Week Two (Discussion/Workbook)

chose “The simple, unclear bar chart” I noticed the original chart was too simple in a way that created confusion rather than clarity. The missing axis labels and unexplained colors made it impossible to understand without extra context. My redesign adds a clear headline, proper axis labels, and horizontal bars for easier reading.

Y-axis now clearly says (% deviation from historical average) Colors are explained orange = above average (bubble risk), blue = below average Country names are horizontal and easy to read Headline and subtitle tell the full story clearly

Challenge Exercise: Visualize the Gapminder demo data

This is my first time using tableau, very fun and excited to learn more about!!!!

Part 1: Recreate an existing data visualization

Trust in news organizations

Challenge Exercise: Data viz redesign! (C4)

1. I decided to use a line chart because it is the best chart type for showing how rankings change over time, making it easy for the development team to see trends across 2013 to 2024.

2. I decided to highlight the top three chart types (Area and line charts, Bar charts, and Tables) in bold colors and set all other lines to gray because these three show the most important story how line charts overtook bar charts, and how tables recently rose to number one.

3. I decided to reverse the Y-axis so that rank 1 appears at the top because this makes the chart more intuitive the higher a line is on the chart, the more popular that visualization type

Part I: In-class critique

Group Members: Shane, Coraline, Crystal

Today in class, our group discussed which project we want to work on. Our main challenges were narrowing down our ideas, deciding on a clear call to action, finding the right dataset, and choosing the best tools to present our work.

Team Challenge Exercise: Visualize passengers from the Titanic

I chose this chart because it clearly shows how different factors are connected and how they influence the final outcome. Unlike simple charts, this flow-style visualization (Sankey diagram) allows us to track relationships across multiple variables at the same time.

This chart illustrates how passengers are distributed across class, gender, and age, and how these characteristics relate to survival outcomes. The width of each flow represents the number of people, making it easy to see patterns. For example, we can observe that a larger proportion of males are associated with the “no” survival outcome, while certain groups (like females or higher-class passengers) have stronger flows toward survival. Overall, the chart helps highlight how different attributes combine to impact survival in a clear and visual way.