- Tools and Practices for Working Virtually — a detailed explanation of how the RedMonk team works virtually.
- Twitter Accounts for All Stack Overflow Users by Reputation (Brian Bondy) — superawesome list of clueful people.
- The Wonderful World of Early Computing — from bones to the ENIAC, some surprising and interesting historical computation devices. (via John D. Cook)
- Overlapping Experiment Infrastructure (PDF) — they can’t run just one test at a time, so they have infrastructure to comprehensively test all features against all features and in real time pull out statistical conclusions from the resulting data. (via Greg Linden)
ENTRIES TAGGED "statistics"
IA Ventures success, MathJax display engine, statistical literacy, and making big data more human
IA Ventures raises a huge first-time fund; MathJax provides an open source mathematical display engine; Kevin Drum shares 10 statistics pitfalls; and Paul Bradshaw explains how to bring big data down to a human scale.
Trading platforms, truth in graphs, European financial stats, and Mandelbrot's passing.
In this edition of Strata Week: The London Stock Exchange moves from .Net to open source; learn how graphical scales can lie; the Euroean Central Bank president calls for better financial statistics; and we bid farewell to the father of fractals.
Logic-less Templates, Amazon Story, Visualizing Time Data, and Statistics Primers
- Mustache — templates without the if/then/loop control structures that mangle your separation of logic. (via the technology behind #newtwitter)
- The Visionary’s Lament (Eric Ries) — love the possibly apocryphal Amazon story about the invention of one-click.
- TimeFlow — helps you analyze temporal data. Timeline, Calendar, Bar Chart, Table, and List views. From the legendary team of Viegas and Wattenberg
- Basic Statistical Literacy — the UK government has some good introductions to statistics. (via Flowing Data)
Narrative and Structure, Teaching Science, Time-Series Statistics, and Who Benefits from Open Source
- Why Narrative and Structure are Important (Ed Yong) — Ed looks at how Atul Gawande’s piece on death and dying, which is 12,000 words long, is an easy and fascinating read despite the length.
- Understanding Science (Berkeley) — simple teaching materials to help students understand the process of science. (via BoingBoing comments)
- Sax: Symbolic Aggregate approXimation — SAX is the first symbolic representation for time series that allows for dimensionality reduction and indexing with a lower-bounding distance measure. In classic data mining tasks such as clustering, classification, index, etc., SAX is as good as well-known representations such as Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT), while requiring less storage space. In addition, the representation allows researchers to avail of the wealth of data structures and algorithms in bioinformatics or text mining, and also provides solutions to many challenges associated with current data mining tasks. One example is motif discovery, a problem which we recently defined for time series data. There is great potential for extending and applying the discrete representation on a wide class of data mining tasks. Source code has “non-commercial” license. (via rdamodharan on Delicious)
- Open Source OSCON (RedMonk) — The business of selling open source software, remember, is dwarfed by the business of using open source software to produce and sell other services. And yet historically, most of the focus on open source software has accrued to those who sold it. Today, attention and traction is shifting to those who are not in the business of selling software, but rather share their assets via a variety of open source mechanisms. (via Simon Phipps)
Statistical Jeopardy Wins, Mobile Taxonomy, Geodata Mystery, and Machine Learning Blog
- What is IBM’s Watson? (NY Times) — IBM joining the big data machine learning race, and hatching a Blue Gene system that can answer Jeopardy questions. Does good, not great, and is getting better.
- Google Lays Out its Mobile Strategy (InformationWeek) — notable to me for Rechis said that Google breaks down mobile users into three behavior groups: A. “Repetitive now” B. “Bored now” C. “Urgent now”, a useful way to look at it. (via Tim)
- BP GIS and the Mysteriously Vanishing Letter — intrigue in the geodata world. This post makes it sound as though cleanup data is going into a box behind BP’s firewall, and the folks who said “um, the government should be the depot, because it needs to know it has a guaranteed-untampered and guaranteed-able-to-access copy of this data” were fired. For more info, including on the data that is available, see the geowanking thread.
- Streamhacker — a blog talking about text mining and other good things, with nltk code you can run. (via heraldxchaos on Delicious)
Google Docs APIs, Wikileaks Founder Profile, DNA Hacking, and Abusing the Numbers
- Appscale — open source implementation of Google App engine’s APIs built on top of Amazon’s APIs, from UCSB. You can deploy on Amazon or on any Amazon API-compliant cloud such as Eucalyptus.
- Information Pioneers — the Chartered Institute for IT has a pile of video clips about famous IT pioneers (Lovelace, Turing, Lamarr, Berners-Lee, etc.).
- This Week in Law — podcast from Denise Howell, covering IT law and policy. E.g., this week’s episode covers “Google Books, Elena Kagen, owning virtual land, double-dipping game developers, Facebook tips, forced follow bug and fragile egos, embedding tweets, Star Trek Universe liability, and more.”
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GMail CRM, Django Best Practices, Stats-Think, and WoW Number Crunching
- Rapportive — a simple social CRM built into Gmail. They replace the ads in Gmail with photos, bio, and info from social media sites. (via ReadWrite Web)
- Best Practices in Web Development with Django and Python — great set of recommendations. (via Jon Udell‘s article on checklists)
- Think Like a Statistician Without The Math (Flowing Data) — Finally, and this is the most important thing I’ve learned, always ask why. When you see a blip in a graph, you should wonder why it’s there. If you find some correlation, you should think about whether or not it makes any sense. If it does make sense, then cool, but if not, dig deeper. Numbers are great, but you have to remember that when humans are involved, errors are always a possibility. This is basically how to be a scientist: know the big picture, study the details to find deviations, and always ask “why”.
- WoW Armory Data Mining — a blog devoted to data mining on the info from the Wow Amory, which has a lot of data taken from the servers. It’s baseball statistics for World of Warcraft. Fascinating! (via Chris Lewis)