Ever noticed how most behavioral research is based on studies of Western, upper-middle-class, undergraduate university students? If you, like me, are American, it might never occur to you to wonder whether those results can really be generalized to describe the behavior of "people." After reading The weirdest people in the world? (Western, Educated, Industrialized, Rich and Democratic (WEIRD)), you may want to go back through your favorite studies on decision-making, collaboration, cognition, and symbol interpretation and question your first read.
Tuesday, March 27, 2012
Thursday, March 22, 2012
I think I can say, without fear of hyperbole, that this is the best math book in the history of the entire universe. The fact that there are only six reviews of the book so far, instead of six hundred, hints at the fundamental problem I personally see in math education: it looks harder than it is because we communicate so poorly about it. Urdan communicates clearly and naturally, so the chilly math textbook mystique drops away, and you are left with a functional vocabulary of basic stats techniques.
Urdan starts with the assumption that all humans can understand and benefit from statistical techniques. By assuming that, he makes it true. He not only defines every term and every symbol he uses -- which is already amazing -- but the new terms and definitions are summarized at the end of each chapter. He lays out lots of context and many straightforward and interesting examples. The chapters are short, which gives you a nice feeling of accomplishment and plenty of breaks to think. He even humanizes the experience by speaking in the first person, expressing personal preferences, and even cracking the occasional joke. It's like talking about math over tea with a good friend.
In the modern data space, there's a great shortage of people who have a comfortable intuition for stats. If this book were in every undergraduate class, I'd wager that shortage would just go away.
Monday, March 19, 2012
Kentaro Toyama, assistant director of Microsoft Research India, has spent a lot of time thinking about how to use technology to change social systems. He's focused on using technology to further development in rural India (ICT4D, or Information & Communications Technology for Developing Countries). He published a very cool set of essays in The Atlantic in 2011 about the topic. I've been thinking about them ever since. Check 'em out.
Kentaro talks about technology as basically an amplifier for people's will. Don't let the "virtue" language deter you; fully unpacked, it's a pretty loaded concept.
Sunday, March 18, 2012
Remember, the driver is as important as the car. If you want to make the best use of your BI application, your organization needs the right people to exploit it. BI is not just about reporting and visualization anymore. It involves intensive and creative analysis, along with data management, to create value for an organization. - Got BI? Now You Need to Hire a Data Geek. Here’s What to Look For.
Data geeks are a hot commodity. Why?
Data is piling up around the industry's ears. We humans are suddenly generating a mountainous drift of accumulating data, growing exponentially, that nobody anticipated having. That mountain is filled with profitable, scientific gems that we are just beginning to learn how to mine out.
The market is unprepared for the demand. Even with the rush to train data miners, the market isn't coming close to keeping up with the pace of the data mountain's growth. Folks like me are hounded by recruiters; folks with +5 years data mining experience/ education are actively stalked.
So if professional data miners are so hard to get, why do you need one of us?
[Your Company Here] needs an excellent data scientist. If humans use your digital product, your company is already generating an enormous quantity of ultra-rich data. Based on that fact alone, I can make the following safe bets:
Skill #2: Data Munging (Suffering). The second critical skill mentioned above is “data munging.” Among data geek circles, this refers to the painful process of cleaning, parsing, and proofing one’s data before it’s suitable for analysis. Real world data is messy. - The Three Sexy Skills of Data Geeks
Your data has, or should have, journeyman-level richness. If you send an apprentice-level data-miner into that trove, you're going to come out with a handful of iron ore. You can hire an apprentice analyst to run the queries you specify and graph them. You can't hire a apprentice to ask big, hot, actionable, counter-intuitive
With big data comes big headaches; also big, big opportunity for a reputation for genius product development. If your analyst can't handle the big hairy real-world data mess, or doesn't know statistical relevance from a hole in the wall, you get bunk analysis. If your analyst just plain isn't that into it, you will get shallow, token inquiry. If your analyst is a passionate data/social science geek, you get game-changing analysis, and stand to score media-worthy customer relationship coups.