- 16 Interviewing Tips for User Studies — these apply to many situations beyond user interviews, too.
- The Backlash Against Big Data contd. (Mike Loukides) — Learn to be a data skeptic. That doesn’t mean becoming skeptical about the value of data; it means asking the hard questions that anyone claiming to be a data scientist should ask. Think carefully about the questions you’re asking, the data you have to work with, and the results that you’re getting. And learn that data is about enabling intelligent discussions, not about turning a crank and having the right answer pop out.
- The Science of Science Writing (American Scientist) — also applicable beyond the specific field for which it was written.
ENTRIES TAGGED "Big Data"
Rise of the Patent Troll, Farm Data, The Block Chain, and Better Writing
- Rise of the Patent Troll: Everything is a Remix (YouTube) — primer on patent trolls, in language anyone can follow. Part of the fixpatents.org campaign. (via BoingBoing)
- Petabytes of Field Data (GigaOm) — Farm Intelligence using sensors and computer vision to generate data for better farm decision making.
- Bullish on Blockchain (Fred Wilson) — our 2014 fund will be built during the blockchain cycle. “The blockchain” is bitcoin’s distributed consensus system, interesting because it’s the return of p2p from the Chasm of Ridicule or whatever the Gartner Trite Cycle calls the time between first investment bubble and second investment bubble under another name.
- Hemingway — online writing tool to help you make your writing clear and direct. (via Nina Simon)
Ignore the hype. Learn to be a data skeptic.
Fault-Tolerant Resilient Yadda Yadda, Tour Tips, Punch Cards, and Public Credit
- Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing (PDF) — Berkeley research paper behind Apache Spark. (via Nelson Minar)
- Angular Tour — trivially add tour tips (“This is the widget basket, drag and drop for widget goodness!” type of thing) to your Angular app.
- Punchcard — generate Github-style punch card charts “with ease”.
- Where Credit Belongs for Hack (Bryan O’Sullivan) — public credit for individual contributors in a piece of corporate open source is a sign of confidence in your team, that building their public reputation isn’t going to result in them leaving for one of the many job offers they’ll receive. And, of course, of caring for your individual contributors. Kudos Facebook.
Establishing protocols to socialize wearable devices.
Game Patterns, What Next, GPU vs CPU, and Privacy with Sensors
- Game Programming Patterns — a book in progress.
- Search for the Next Platform (Fred Wilson) — Mobile is now the last thing. And all of these big tech companies are looking for the next thing to make sure they don’t miss it.. And they will pay real money (to you and me) for a call option on the next thing.
- Debunking the 100X GPU vs. CPU Myth — in Pete Warden’s words, “in a lot of real applications any speed gains on the computation side are swamped by the time it takes to transfer data to and from the graphics card.”
- Privacy in Sensor-Driven Human Data Collection (PDF) — see especially the section “Attacks Against Privacy”. More generally, it is often the case the data released by researches is not the source of privacy issues, but the unexpected inferences that can be drawn from it. (via Pete Warden)
- Mining the World’s Data by Selling Street Lights and Farm Drones (Quartz) — Depending on what kinds of sensors the light’s owners choose to install, Sensity’s fixtures can track everything from how much power the lights themselves are consuming to movement under the post, ambient light, and temperature. More sophisticated sensors can measure pollution levels, radiation, and particulate matter (for air quality levels). The fixtures can also support sound or video recording. Bring these lights onto city streets and you could isolate the precise location of a gunshot within seconds.
- An Investor’s Guide to Hardware Startups — good to know if you’re thinking of joining one, too.
- WebScaleSQL — a MySQL downstream patchset built for “large scale” (aka Google, Facebook type loads).
Google Flu, Embeddable JS, Data Analysis, and Belief in the Browser
- The Parable of Google Flu (PDF) — We explore two
issues that contributed to [Google Flu Trends]’s mistakes—big data hubris and algorithm dynamics—and offer lessons for moving forward in the big data age. Overtrained and underfed?
- Principles of Good Data Analysis (Greg Reda) — Once you’ve settled on your approach and data sources, you need to make sure you understand how the data was generated or captured, especially if you are using your own company’s data. Treble so if you are using data you snaffled off the net, riddled with collection bias and untold omissions. (via Stijn Debrouwere)
Data tools are less important than the way you frame your questions.
On Managers, Human Data, Driverless Cars, and Bad Business
- On Managers (Mike Migurski) — Managers might be difficult, hostile, or useless, but because they are parts of an explicit power structure they can be evaluated explicitly.
- Big Data: Humans Required (Sherri Hammons) — the heart of the problem with data: interpretation. Data by itself is of little value. It is only when it is interpreted and understood that it begins to become information. GovTech recently wrote an article outlining why search engines will not likely replace actual people in the near future. If it were merely a question of pointing technology at the problem, we could all go home and wait for the Answer to Everything. But, data doesn’t happen that way. Data is very much like a computer: it will do just as it’s told. No more, no less. A human is required to really understand what data makes sense and what doesn’t. (via Anne Zelenka)
- Morgan Stanley on the Economic Benefits of Driverless Cars — The total savings of over $5.6 trillion annually are not envisioned until a couple of decades as Morgan Stanley see four phases of adoption of self-driving vehicles. Phase 1 is already underway, Phase 2 will be semi-autonomous, Phase 3 will be within 5 to 10 years, by which time we will see fully self-driving vehicles on the roads – but not widespread usage. The authors say Phase 4, which will have the biggest impact, is when 100% of all vehicles on the roads will be fully autonomous, they say this may take a couple of decades.
- Worse (Marco Arment) — I’ve been sitting on this but can’t fault it. In the last few years, Google, Apple, Amazon, Facebook, and Twitter have all made huge attempts to move into major parts of each others’ businesses, usually at the detriment of their customers or users.