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Data science & statistics: videos Hacker News recommends
Data science and applied statistics: data analysis, pandas, scientific Python, regression, A/B testing, causal inference, Bayesian methods, statistical misconceptions, big data, data engineering. Below: every video in this topic that at least three HN users linked with real commentary, in at least three different months (22 talks, 12 explainers, 5 lectures), best-scored first, with the comments on each video page.
- 15 HN recommenders 14 months 2015–2026HN's go-to recommendation for colormap and color-perception questions; commenters call it funny, brief, clear and a favorite talk.
- 8 HN recommenders 11 months 2017–2021G Gapminder Foundation · lectureHN repeatedly recommends it to counter overpopulation alarm with data on falling birth rates.
- 8 HN recommenders 8 months 2017–2025HN commenters link it whenever a shaky study or the p<0.05 threshold comes up, as a short primer on false positives in science.
- 5 HN recommenders 6 months 2015–2023Recommended as evidence that the world is improving despite negative news, and for how people misjudge global trends.
- 6 HN recommenders 6 months 2018–2025It is the go-to funny, irreverent critique of notebooks, linked whenever HN debates notebooks versus testable code.
- 6 HN recommenders 6 months 2018–2025S Saul Pwanson · demoHN recommends it as the fastest way to understand what VisiData can do, since seeing it work is clearer than reading about it.
- 5 HN recommenders 6 months 2014–2018Repeatedly recommended as the canonical demo of org-mode for literate programming and reproducible research.
- 4 HN recommenders 5 months 2020–2025Whenever notebooks get criticized, HN commenters point to it as the standard rebuttal from a prominent practitioner.
- 6 HN recommenders 4 months 2020–2021HN recommends it as an enthusiastic demo of spreadsheet-like reactive notebooks, which commenters think new notebook projects should copy.
- 7 HN recommenders 6 months 2024–2026C Cool Bug Facts · tutorialHN commenters call it an internet classic that makes you a more proficient Excel user, and note it is still relevant despite predating newer functions like XLOOKUP.
- 5 HN recommenders 5 months 2015–2024M Michigan Engineering · lectureHN points to it as a thoughtful, first-principles take from a Turing award winner that cuts through NoSQL and big data hype.
- 5 HN recommenders 5 months 2017–2025M Michel van Biezen · courseHN repeatedly recommends this series as a clear, step-by-step way to really understand Kalman filtering.
- 4 HN recommenders 5 months 2016–2020C Citus Data · talkHN commenters point to it as a first-hand overview of Heap's Postgres/Citus architecture and its distinctive indexing approach.
- 4 HN recommenders 4 months 2025–2026T The Ezra Klein Show · interviewHN commenters recommend it as a data-driven explanation of the 2024 election and voter trends.
- 3 HN recommenders 4 months 2021–2024Cited in threads about Python's rise as a first-hand, entertaining account of why it won in scientific computing.
- 3 HN recommenders 3 months 2020–2023HN brings it up in any thread about German or European rail as a data-driven, entertaining analysis of why trains are late.
- 4 HN recommenders 4 months 2012–2025L LimeXd · comedy videoHN commenters bring it up whenever a poll or survey result looks suspect, as the classic illustration of biased question framing.
- 3 HN recommenders 3 months 2015–2022HN commenters recommend it for the finding that idea exchange and connectivity drive productivity and wealth, which they apply to cities, offices and online communities.
- 4 HN recommenders 4 months 2011–2017Cited as the classic data-driven argument that the first-world versus third-world split is outdated and the world is improving.
- 4 HN recommenders 3 months 2021–2024P PyCon US · talkHN commenters recommend it as a technically jaw-dropping talk, and it is the standard reference whenever the Night Watch gigapixel images come up.
- 3 HN recommenders 3 months 2014–2025HN commenters link it as the clearest short explanation of regression to the mean, especially for how it misleads people about feedback and statistics.
- 3 HN recommenders 3 months 2015–2025HN recommends it as an excellent programmer-friendly explanation of p-values and statistical thinking.
- 2 HN recommenders 3 months 2020–2026C CMU Database Group · talkHN recommends it as the definitive talk from the implementer on DuckDB's motivation and design.
- 3 HN recommenders 3 months 2019–2020C Coffee Break · commentaryCited in threads on YouTube algorithms and creator complaints as a data-driven look at the trending tab.
- 2 HN recommenders 3 months 2021–2024Recommended as a firsthand history of how Python became the language of scientific computing and ML.
- 4 HN recommenders 3 months 2017–2026C Concordia · talkHN points to it as the company's own pitch, used in debates about data-driven manipulation and extrapolated to today's AI.
- 3 HN recommenders 3 months 2007–2009G GoogleDeveloperDay · talkHN recommends it as the clear explanation of why Google's statistical, data-first approach works.
- 3 HN recommenders 3 months 2017–2024M MLSS Iceland 2014 · lectureHN people return to it as a clear primer on MCMC and HMC and on why high dimensions behave counterintuitively.
- 3 HN recommenders 3 months 2012–2026U UBC Computer Science · lectureRecommended as a favorite tech talk showing how huge data availability changes how problems are approached.
- 4 HN recommenders 3 months 2025–2026HN commenters recommend it for changing how they think about distributions and for showing how criticality shows up across earthquakes, wildfires and other fields.
- 3 HN recommenders 3 months 2017–2023J John O'Neill · talkHN points to it as the best explanation of why R is so hard to optimize, often paired with talks on Python and used to explain what makes Julia fast.
- 3 HN recommenders 3 months 2017–2025It is recommended to programmers who want to learn statistics through simulation rather than memorized formulas.
- 2 HN recommenders 2 months 2011–2015G Geoff Cumming · explainerRecommended as a vivid visual demonstration of how unreliable p-values are.
- 3 HN recommenders 3 months 2014–2015HN commenters point newcomers to it as a solid starting point for getting to know Julia.
- 2 HN recommenders 2 months 2022–2026Recommended in threads on German rail as the data-backed explanation of how reported punctuality understates real delays.
- 2 HN recommenders 2 months 2014–2022N New Relic · talkHN recommends it as an elegant argument for simple Unix tools replacing heavyweight Hadoop-style pipelines.
- 2 HN recommenders 2 months 2013–2022C Cambridge University · explainerRecommended as a short, fun introduction to micromorts and thinking about risk.
- 2 HN recommenders 2 months 2018–2023HN points to it as an accessible case that diets work differently for different people, partly due to gut bacteria.
- 2 HN recommenders 2 months 2017–2021N N N Taleb's Probability Questions · commentaryHN cites it when arguing that different risks follow different distributions and should be judged by growth rates and tails rather than current counts.
- 2 HN recommenders 2 months 2017–2025HN points to it as a clear explanation of what flood return periods really mean.
- 2 HN recommenders 2 months 2017–2024HN points to it as an accessible introduction to Bayesian reasoning and as inspiration for personal projects.
- 2 HN recommenders 2 months 2023–2026HN links it in data-visualization threads as an entertaining case against violin plots.
- 4 HN recommenders 2 months 2020–2021HN commenters cite it as a clear debunking of election-fraud statistics and as a warning against comparing percentages without context.
- 2 HN recommenders 2 months 2020–2024Commenters cite it as a visual debunking of Benford-based election fraud claims and as a guide to when Benford's Law does and does not apply.
- 2 HN recommenders 2 months 2020–2022Linked as the clearest technical explainer of differential privacy and the census, and of why aggregate data can still compromise privacy.
- 2 HN recommenders 2 months 2017–2024L London Mathematical Society · lectureUsed to illustrate that humans pattern-match and find spurious patterns in enough data points.
- 2 HN recommenders 2 months 2021–2023Cited again and again as the concrete case study behind the claim that NASA got a 15,000x speedup moving from Simulink to Julia.
- 2 HN recommenders 2 months 2022–2026P Palantir · demoPointed to as the clearest explanation of what Foundry actually is, and for its polished software, though one commenter calls it dated now.
- 1 HN recommenders 1 months 2016–20222 2veritasium · explainerHN uses it as a handy reference whenever a thread needs to point at survivorship bias.
- 1 HN recommenders 1 months 2017–2022HN people link it as a short, accessible summary of Judea Pearl-style causal inference when someone repeats that correlation cannot imply causation.
- 1 HN recommenders 1 months 2022–2026E ewecylinder · music videoIt is mostly cited as a joke or a wry reference in threads about dubious statistics.
- 1 HN recommenders 1 months 2016–2025T The Deming Institute · demoHN cites it as a classic illustration of dysfunctional management, gamed targets, and blaming workers for systemic outcomes.
- 2 HN recommenders 1 months 2024–2025W Web of Stories - Life Stories of Remarkable People · interviewCited in modeling and machine learning threads as the classic story on overfitting and justifying parameters.
- cited 10× 0 months 2022–2026Linked whenever surveys or polls are suspected of manipulation through question wording.
- cited 3× 0 months 2016–2023A Adam Staab · videoInvoked as shorthand for hiring and investing by gut feel and appearances instead of what actually predicts results.
Questions
What is the most recommended data science & statistics video on Hacker News?
A Better Default Colormap for Matplotlib | SciPy 2015 | Nathaniel Smith and Stéfan van der Walt by Enthought: HN's go-to recommendation for colormap and color-perception questions; commenters call it funny, brief, clear and a favorite talk.
How is this data science & statistics list ranked?
By HackerTube's score: the number of distinct months in which an HN comment recommended the video, plus half a point for every month with a strong recommendation. One viral thread counts less than steady recommendations over years.
Where do the videos come from?
From every Hacker News comment between 2007 and 2026 that linked a YouTube video, labeled by an AI model as a recommendation, a citation, a joke or a criticism. The list is published by HackerTube under ODC-By 1.0; this edition adds indexable pages and the comment context.
Video list and Hacker News comment labels from HackerTube (prompted by @karpathy), released under ODC-By 1.0, built on the open-index/hacker-news archive. Hacker News data is provided by Y Combinator; comments remain the work of their authors. Video titles, channel names and thumbnails belong to their creators and are shown via YouTube. HackerTube Watch is an unofficial edition. Data export of 2026-10-31.