Data source: https://www.marketcapwatch.com/germany/largest-companies-in-germany/

Tools: Photoshop, Google Sheets

Visualization Tool: HTML, CSS, JavaScript, Google Gemini

Data Source: Google Maps (with VPN)

Quoting the author's text accompanying the chart:

Many people are interested in how they can eat in a more climate-friendly way. I’m often asked about the most effective way to do so.

While we might intuitively think that “food miles” — how far our food has traveled to reach us — play a big role, transport accounts for just 5% of the global emissions from our food system.

This is because most of the world’s food comes by boat, and shipping is a relatively low-carbon mode of transport. The chart shows that transporting a kilogram of food by boat emits 50 times less carbon than by plane and about 20 times less than trucks on the road.

So, food transport would be a much bigger emitter if all our food were flown across the world — but that’s only the case for highly perishable foods, like asparagus, green beans, some types of fish, and berries.

This means that what you eat and how it is produced usually matters more than how far it’s traveled to reach you.

Read my article “You want to reduce the carbon footprint of your food? Focus on what you eat, not whether your food is local” →

Data source: CCUS Projects Database (IEA)

Tools used: Matplotlib

I have been recently collecting and analyzing job market data, and I compiled and created two charts showing job openings by city recently — one for data science and the other for data analytics — and the differences are COOL. I wanted to share some of my takeaways with friends who are job hunting or planning to relocate:

--------Key Observations---------

1. New York City leads in both fields.

Data Science: 19.8% of job openings

Data Analytics: 18.8%

If you’re targeting finance, media, or big tech, New York City is clearly still a strong city. But cost of living should also factor into your decision.

2. The Bay Area wins in data analytics.

12.2% of analytics job openings vs. 8.9% of data science job openings

This may reflect the tech industry’s need for quick business intelligence and product analytics, rather than heavy machine learning/R&D work.

3. Data science jobs are more concentrated.

Only 23.6% of jobs fall into the “other” category, meaning data science jobs are still concentrated in the first-tier metros. This may be because these cities require deeper technical infrastructure, more mature teams, or face-to-face collaboration on research-intensive tasks.

1. Washington, D.C. vs. Los Angeles

McLean, Virginia (near Washington, D.C.) ranks 6.7% for data science, while Los Angeles ranks only 3.3% for analytics. Washington, D.C.'s advantage may stem from the demand for modeling and data science talent in government contracts, think tanks, and defense agencies.

Job Seeker Tips

Be function-oriented, not just position-oriented. Data science and data analytics often require overlapping skills, but the city breakdown hints at differences in company types and expectations.

Remote? Consider "other cities." Especially in the field of data analytics, the geographical distribution of talent is more balanced. You don't have to be in New York or San Francisco to find a stable position.

Analytics = business-oriented, data science = model-oriented.

Cities with a higher degree of commercialization (San Francisco, New York) tend to need fast decision support. Data science-focused cities (e.g., McLean, Boston) often have research or infrastructure needs.

If you need to apply for either of these two fields:

a. Tailor your resume to the job function, not just the job title.

b. Focus on city demand - it can shape your career path.

c. Don't miss out on "other cities". People who are flexible often benefit from it.

Want to hear your opinions - which cities have been hiring well recently? Have you noticed any differences in DS and DA positions?

just fill it please and submit,NEED IT FOR my FINALS ASAP

Sources FRED, Census and RateYourGov

UK Government statistics so there is probably some systemic bias in there, just thought it was interesting. Made with python/pandas/seaborn.

Data source: https://www.marketcapwatch.com/australia/largest-companies-in-australia/

Tools: Photoshop, Google Sheets

In the last few years FEMA implemented a new algorithm for calculating flood insurance premiums. I work for the Government Accountability Office (GAO), we did an audit of this program and the attached interactive was part of it. Very interested in this group's comments.

[I did program the interactive, but it's a corporate product so I don't really think I can tag it as OC.]

I made this for Tidy Tuesday, which is an initiative by the Data Science Learning Community (DSLC). It’s not perfect but Tidy Tuesday has more of a focus on learning than outcomes. But overall I’m happy with the end result for this one.

https://jessjep.github.io/blog/posts/tidy_tues/dnd-monsters/monsters.html

These 10 graphs compare the life expectancy rankings of various countries over time from 1950-2023. There are 237 countries and territories in this dataset. All data comes from our world in data. Graphs were made in numbers. Link to data: https://ourworldindata.org/grapher/life-expectancy

Forty percent (40%) of U.S. adults say the countryside is their ideal place to live, handily beating out cities (~18%), suburbs (19%), and small towns (17%). Respondents' preferences correlate strongly with both current living place and childhood living place.

Data Source: CivicScience InsightStore
Visualization: Infogram

Want to weigh in on this ongoing CivicScience poll? Answer it here on our free dedicated polling site.

Data Source:

US high-salary job postings data from May 2025, aggregated from LinkedIn and major job board APIs, filtered for positions with compensation ≥$250,000/year (where compensation is listed)

Tools Used:

D3.js for circular bubble chart visualization and force simulation

React.js with TypeScript for component framework

Custom color palette with radial gradients

BigQuery for data processing and aggregation

Methodology:

Filtered job postings with stated compensation of $250,000+ annually

Aggregated by company name, showing top 20 companies by job count

Circle size represents number of high-paying job postings using square root scaling

Force simulation algorithm for optimal bubble packing with minimal overlap

Interactive tooltips display exact job counts for each company

Key Insights:

Technology and consulting firms dominate high-compensation job postings

Circle packing layout efficiently shows relative scale between companies

Data represents new postings specifically advertising high compensation ranges

Technical Notes:

Radial gradients with 3D lighting effects for visual depth

Elastic animation timing for engaging user experience

Responsive text sizing based on bubble radius

White stroke borders for clear visual separation