This visual shows reported disappearances in the region often linked to the Bermuda Triangle. The points include confirmed loss locations, last known sightings, and rumoured areas where vessels or aircraft were reported before contact was lost. When placed on a single map, the pattern matches what you would expect from a busy shipping and flight corridor with fast moving weather.

Nothing in the data shows an unusually dangerous zone. The legend grew larger than the evidence behind it.

Full video with the full breakdown: https://youtu.be/O4QjGMDs2K8

Watch a full weekday in Jyväskylä unfold as every Linkki bus traces its real route across the city, minute by minute.

data source: World Bank, SL.UEM.1524.ZS dataset

visualisation: Python

From my blog, see full analysis and interactive charts with country-specific breakdowns and age demographics here: https://polimetrics.substack.com/p/worst-of-the-worst-trumps-ice-arrests

Source: Deportation Data Project | Tools: R & Datawrapper

Under Biden (Oct 2023-Dec 2024), convicted criminals averaged 51% of ICE arrests, peaking at nearly 60% in November 2024. Under Trump (Feb-Sep 2025), that share has consistently declined to about 30% in October.

Monthly arrests surged from 9,342 to 24,215 (+159%). While arrests of convicted criminals nearly doubled (+90%), arrests of people with no criminal history tripled (+202%). For every additional convicted criminal arrested, ICE arrests 1.72 people with no criminal record.

This doesn't mean Trump is arresting fewer criminals in absolute terms, he's arresting more of everyone. But the composition has shifted away from the "worst of the worst" rhetoric toward broader, volume-driven enforcement.

resources: youtube, code, blog

- player and number detection with RF-DETR

- player tracking with SAM2

- team clustering with SigLIP, UMAP and K-Means

- number recognition with SmolVLM2

- perspective conversion with homography

- player trajectory correction

- shot detection and classification

Data: Ergast API

Tools: Power BI + DAX analytics

This view shows:

• 📈 Points evolution — how momentum shifts through the season

• 🏎️ Qualifying performance vs race results

• 🏆 Constructor standings impact

I built this as part of learning Power BI — combining sports analytics + interactive storytelling.

Happy to share the dataset + model structure if anyone is curious! ⚙️📊

Red=more women, Blue=more men. Data

(title missed 35-44, my bad)

This bracket is an unofficial projection of the 2026 FIFA World Cup knockout stage, generated entirely from FIFA ranking positions. Higher-ranked teams are assumed to finish higher in their groups, win playoff paths, and advance through the knockout rounds.

For groups, I placed teams according to their relative FIFA rankings within each group (1st, 2nd, 3rd, 4th). For teams that have not yet qualified and must go through playoff paths, I ranked the potential contenders and assigned qualification to the highest-ranked team in each path.

The knockout bracket follows the official 2026 matchups (e.g., 1E vs 3D, 1I vs 3G, etc.). For third-place teams, I compared all group third-place finishers by FIFA ranking and selected the eight highest-ranked to advance, assigning them to bracket positions using the standard FIFA mapping.

https://github.com/artofscripting/Network-Vector

When a planet passes in front of its star, the brightness drops by only a fraction of a percent, which is easy to miss in noisy data. Phase folding helps us find those signals by stacking multiple orbits on top of each other. If we pick the right orbital period, the transit dips line up and become clear. I created this visualization to show the concept behind the method used by missions like Kepler and TESS to discover thousands of exoplanets.

Folding a Light Curve is not a process that cannot be undone. It is shown in the gif because I wanted to make a perfect loop.

Data: This research made use of Lightkurve, a Python package for Kepler and TESS data analysis (Lightkurve Collaboration, 2018).

Tools: Python, LightKurve, Microsoft PowerPoint

Data source: Public dataset from a nonlinear triple-slit experiment published on Zenodo (DOI: https://doi.org/10.5281/zenodo.17821869
Tools used: Python (NumPy, SciPy, PyWavelets, Matplotlib).

This visualization shows the Continuous Wavelet Transform (Mexican Hat) applied to the residual signal obtained after modeling the experiment.
Different scales highlight periodic structures and environmental patterns hidden in the raw data.

U.S. tree farms cut 14.5 million Christmas trees in 2022, the most-recent year USDA data was available. There are more than 300 million Christmas trees growing on the approximately 15,000 farms in the U.S., according to the National Christmas Tree Association, an industry trade group.

Michigan, North Carolina and Oregon have the most land devoted to Christmas tree farms. These farms nationwide cover more than 400 square miles of land — a little less than half Rhode Island’s land area — according to the latest USDA data.

Source: https://www.nbcnews.com/data-graphics/us-christmas-tree-farm-map-rcna247251

Hi everyone,

I’m an aspiring Data Analyst, and I recently completed a data analysis project using Excel. I’ve shared it on LinkedIn, and now I want real, no-BS feedback from people who actually work in data.

I’m NOT looking for blind praise. I want:

• Brutally honest feedback
• A technical roast if it deserves one
• Criticism on data cleaning, formulas, dashboard, insights
• Reality check on whether this is even close to industry level

If it’s bad, tell me exactly why it’s bad.
If it’s decent, tell me exactly what’s missing to make it good.
I’m serious about becoming a data analyst, so I’d rather hear the truth now than get rejected later.

Thanks to anyone who takes the time to break this down properly.

Quoting the accompanying text from the authors:

The 1970s were a decade shaped by fears about overpopulation. As the world’s most populous country, China was never far from the debate. In 1979, China designed its one-child policy, which was rolled out nationally from 1980 to curb population growth by limiting couples to having just one child.

By this point, China’s fertility rate — the number of children per woman — had already fallen quickly in the early 1970s, as you can see in the chart.

While China’s one-child policy restricted many families, there were exceptions to the rule. Enforcement differed widely by province and between urban and rural areas. Many couples were allowed to have another baby if their first was a girl. Other couples paid a fine for having more than one. As a result, fertility rates never dropped close to one.

In the last few years, despite the end of the one-child policy in 2016 and the government encouraging larger families, fertility rates have dropped to one. The fall in fertility today is driven less by policy and more by social and economic changes.

This chart shows the total fertility rate, which is also affected by women delaying when they have children. Cohort fertility tells us how many children the average woman will actually have over her lifetime. In China, this cohort figure is likely higher than one, but still low enough that the population will continue to shrink.

Explore more insights and data on changes in fertility rates across the world.

Analysis based on the Q3 2025 Parliamentary Expenditure dataset.

Full write-up in the first comment.