Challenge:

Create a story-driven infographic (not a dashboard) based on real Everest expedition data, submitted for the #MavenEverestChallenge. The goal was to visualize the scale, danger, and evolution of Everest climbs in a single, cohesive visual.

Tools Used:

• Excel: Initial data exploration, cleaning
• Tableau: Visualization and layout
• Google Maps & Gemini: Exact peak coordinates
• Figma: Planning layout & annotations

My Approach: Step-by-Step

1. Understanding the Data

The dataset contained 100K+ rows across:

• Members - 89001 R x 28 C

• Expeditions - 11426 R x 23 C

• Peaks - 481 C x 15 R

• consisting data like Expedition years, seasons, roles, Summit success, deaths, oxygen use, Nationality, sherpa status, death causes

Challenge: The data was huge and wide, but needed to be distilled into a clean story across just one static visual.

2. Data Cleaning & Transformation

Goals: Make data analysis-ready, handle nulls, and prepare insight-driving columns.

Key Steps:

• Filtered irrelevant/null-heavy rows
• Cleaned date formats and fixed inconsistent season labels
• Grouped lesser-known nationalities into “Others”
• Binned age and elevation levels
• Resolved role/oxygen inconsistencies

3. New Fields Created

Climbers The full name of each climber

Death Count Numerical count of deaths

Successful climber Successful climber?

Fatality Rate Fatalities per 100 climbers

Success Rate Summits per 100 expeditions

Survival Rate Survived members per 100 expeditions

4. Design Strategy

Objective: Create a National Geographic-style data story—not a dashboard—with:

• No filters or interactivity
• Clean visual pacing
• Strong titles & clear narratives

Design Principles:

• Color Palette: Snowy whites + icy blues = match the Everest theme
• Typography: Emphasis with bold blue headers, light body text
• Icons & Charts: Minimalist, yet powerful
• Layout: Clean vertical rhythm

Final Visual – Section Breakdown

🔹 Top Section (Scene Setting)

• Hero Title: “Survival and Glory” with snowy background
• Intro Paragraph: Sets emotional and contextual tone
• Peak Map: Shows the geographic stretch of climbs
• Historical Timeline Chart: Expedition trends + annotated key events (first summit, oxygen-free climbs, Covid halt)

🔹 Middle Left – Summit Success

Header: On Top of the World

• Summit by Month: Shows April–May peak activity
• Summit Trend Line: Rising over decades, with peaks post-2000
• Oxygen Use vs Summit %: Explores dependence on oxygen
• Top Nationalities: USA, India, UK, etc. (excluding Nepal)

🔹 Middle Right – Deaths & Risks

Header: Frozen in Time

• Deaths Over Time: Bar chart with timeline
• Causes of Death: Pie chart (Exposure, Falls, Altitude illness)
• Fatality Rate by Altitude: Shows base camp vs summit risk
• Any Specific Time? Circular chart revealing peak death hours
• Death Map: Choropleth of climbers who died, by home country

🔹 Bottom Left – Sherpas Highlight

• Are Sherpas Safer? YES/NO stat breakdown
• Sherpa Success vs Death Rate: (69% vs 35%)
• 8848m Leaderboard: Top repeat summiteers, many Sherpas

Key Insights Derived

• May is the dominant month for summits AND deaths.
• Oxygen use improves summit chances—but is not a guarantee.
• 52% of deaths occur between 9 AM and 12 PM—possibly due to late descents.
• Sherpas have significantly higher summit rates, but also a considerable death toll.
• The death toll dropped significantly post-2015 due to improved gear, forecasts, and crowd control.

Impact & Outcome

• Distilled a 100K+ row dataset into a single visual that feels like a magazine spread.
• Showcased skills in data storytelling, design, and analytical insight.
• Received positive feedback for clarity, aesthetics, and storytelling on public forums.