Growing up, most of my friends had cable television, but whenever I brought it up, my parents would always say that I watched enough TV already (which was true). So it was always a treat when we went somewhere like a hotel, where there were more than five channels. I didn't know what any of the shows were, but it sure was fun pressing buttons on the remote. Today, I still don't know what most of the shows are, but the novelty is gone.

Nowadays, I have different choices (and priorities). I can entertain myself online, and services like Netflix and Hulu make that easier. When I do turn on the TV, it's often just for background noise as I cook dinner or do something on the computer.

I almost never watch shows when they actually come on, and I only know the schedules of a few of them. And nowadays, the gift of choice feels more like a waste, as I flip through sixty something channels and see nothing that I want to watch.

The other day I thought to myself, "I'm paying forty bucks per month to watch Groundhog Day. Again." But then I looked at the cable bill that I had not looked at in a year, since it's on auto-pay. I'm paying $64.99 for digital cable from Comcast, plus $15.95 for HD and DVR, and then there's about $5 in taxes and fees. The introductory price ran out long ago.

I could buy an obscene number of tacos from Jack in the Box with that cash.

So I looked into cutting the cord completely. I want to save money, but more importantly, I want to get more of what I want for my money. Toss the channels and shows I don't watch.

At $85.91 per month for the most basic HD plan from Comcast, that comes in at just over a grand per year. With Netflix and Hulu, it's $15.98 per month, or just under $200 per year. That's a big gap between Comcast and Hulu+Netflix. $839.16, to be exact, which is quite a buffer.

Of course you need a device if you don't already have one to play Netflix and Hulu on your television. A Roku costs between $50 and $100, and an Apple TV is about $100. Current difference: $739.16.

Also, you don't get all you want with just Hulu and Netflix. Personally, I watch basketball when good games are on. The NBA League Pass lets you watch (more) games over broadband though, on a Roku or Apple TV. That's $109 for the season. But, and it's a big one, in-market and nationally broadcast games aren't available via the League Pass. More on this to follow. Current difference: $630.16.

Then to get local channels, you can still use an antenna. The bestselling antenna on Amazon is $35.99. Current difference: $594.17.

Finally, that leaves a healthy amount to buy and rent shows and movies not available on Hulu or Netflix, which you can get on iTunes and Amazon Instant. For example, the pass for this season's How I Met Your Mother is $26.47. You could buy (and own) 22 full seasons of your favorite shows with the available buffer. I'm pretty patient though and don't mind waiting for stuff to become available on Netflix. I just need to be able to watch sports live. My wife has been really into Downton Abbey, and the season pass is $17.04. It's free on PBS, but she usually can't watch it when it airs. Current difference: $577.13.

After all the additional stuff, that's $577.13. Over 1,000 tacos.

But back to the basketball problem. Since League Pass doesn't get me nationally broadcast games, that means I wouldn't get most of the playoffs on ESPN and TNT. (I suspect the same for hockey, baseball, and football.) That's the most important part of the season, save the finals, which are broadcast on ABC. And my wife really likes HGTV and a handful of reality shows that aren't available on Roku, Apple TV, iTunes, or Amazon. Crud.

By the numbers and tacos, it makes sense to cut the cord. From a perspective of want though, it's harder to let go. It comes down to this: Is a year of a tiny subset of programming on cable and playoff games not available on ABC worth $577.13?

This document describes a plan to enlarge the major areas of technical work of the field of statistics. Because the plan is ambitious and implies substantial change, the altered field will be called "data science."

For those unfamiliar, Cleveland's work on graphical perception might ring a bell.

The first time I heard "data science" was in 2007 while reading a proposal that my adviser had passed along, outlining an academic program similar to what we think of as data science. Now that I think of it, the proposal probably had a lot of similarities to the program outlined by Cleveland (which I would have signed up for in a heartbeat).

Cleveland outlines six areas and the percentage of focus for each.

• Multidisciplinary Investigation (25%) — collaboration with subject areas
• Models and Methods for Data (20%) — more traditional applied statistics
• Computing with Data (15%) — hardware, software, and algorithms
• Pedagogy (15%) — how to teach the subject
• Tool Evaluation (5%) — keeping track of new tech
• Theory (20%) — the math behind the data

That sounds like what we associate data science with, but current practitioners focus more on tools and less on pedagogy and theory. Although that's not to say that data scientists today couldn't benefit for more traditional statistical knowledge under their belt. And the same goes for statisticians learning more about how to use the tools available (and how to make the tools themselves).

Cleveland's overall theme is a melting of various fields that obviously fit well together.

Computer scientists, waking up to the value of the information stored, processed, and transmitted by today's computing environments, have attempted to fill the void. One current of work is data mining. But the benefit to the data analyst has been limited, because the knowledge among computer scientists about how to think of and approach the analysis of data is limited, just as the knowledge of computing environments by statisticians is limited. A merger of the knowledge bases would produce a powerful force for innovation.

Sounds familiar. Of course, John Tukey seemed to have the right idea in the 1970s. In any case, it was refreshing to find this from a statistician over the canned stat scoff that data science is statistics.

[William Cleveland via @drewconway]

Homicide is a "serious crime that many people are concerned with, it is well-measured, and it is to a large degree well-reported and -recorded," says Alfred Blumstein, a criminologist at Carnegie Mellon University. "That is not to say that there aren't a variety of ways for fudging the measurement."

Among the factors that cloud homicide numbers: gaps between police-reported numbers and counts by public-health organizations. The discrepancy is wide in many African countries and some Caribbean ones. The United Nations attributes the disparity to several factors, including definitional differences—whether honor killings should count—a lack of public-health infrastructure in some countries, and undercounting—possibly deliberate—by police.

I think this is something the common public often doesn't understand about data. The numbers are entered and analyzed on a computer, so it's easy to mistake data for mechanical output. It must be accurate, right? That's usually not the case though, especially when it comes to data collection outside a controlled lab setting.

The game always changes when humans are involved. Not everyone responds to surveys, definitions of events vary across organizations, estimation methods change every year, and the list goes on.

For those who do stuff with data, you have to deal with that uncertainty, and as data consumers, you have to remember that numbers don't automatically mean fact.

[Wall Street Journal]

I got the numbers straight from the Bits post, but you can download the full report from 11mark for all the demographics. You have to register though, and I didn't want to be the guy who creates an online account to just read a report on what people do while they make dooty. I have standards.

At North Carolina State, an advanced analytics program lasting 10 months has, since its founding in 2006, placed over 90 percent of its students annually. The average graduate’s starting salary for an entry-level job is $73,000. Its current class of 40 students had 185 applicants, and next year’s applications are already twice that. In 2009, Harvard awarded four undergraduate degrees in statistics. Two graduates went into finance, one to political polling and one became a substitute teacher. There were nine graduates in 2010, 13 last year. They headed into Google, biosciences and Wall Street, as well as Stanford's literature department.

And in 2011, just about everywhere.

[New York Times via @jsteeleeditor]

Priceonomics takes the association of fixie bikes to hipsters, and creates the Fixie Bike Index. After starting with New York, they branch out to national numbers.

In short, fixed gear bikes = hipsters, and New York boroughs that have more fixies per capita should have more hipsters per capita. We sampled our data to see the number of used bikes for sale per capita in each borough with the term "fixie" or "fixed gear" in the product title to create the Fixie Index.

I don't know about these numbers. I lived in Modesto for a year and don't remember people riding bikes — or hipsters, and riding your bike in Los Angeles kind of sucks.

[Priceonomics]

In working with tenants to help their city attorney convict a group of slumlords, an economic justice organization collected public data on housing violations that were going unfixed. They tried standard mind mapping and organization software, but the relationships were too complex to unearth anything useful. So they eventually used social network analysis, revealing money exchanging hands in such a way that allowed owners to strip the value from buildings without actually fixing them.

The analysis results, combined with the city's investigation, allowed key convictions and court-awarded finances for tenants to move elsewhere.

Sounds like a good reason for Data Without Borders.

[Valdis Krebs via kottke]

Legos are the best toys ever invented. That's indisputable fact. So it's no surprise that Mark Changizi et al. at Duke University used the toys in their study of growing complexity of systems and networks. They looked at 389 Lego sets and compared the number of pieces in the set to the number of piece types, as shown above.

This curve demonstrates that as the number of pieces in a set grows, so do the number of piece types. However, the number of piece types grows sublinearly: while a larger set uses more piece types, as sets becomes larger, they use progressively fewer additional piece types (so larger sets actually use fewer types per piece). This is similar to other sublinear curves, where larger animals use less energy per cell for metabolism or larger cities actually need fewer gas stations per capita. Essentially, larger sets become more efficient, using the same pieces that smaller sets do, but in a more complex and diverse way.

Now to more important matters... look at this cool thing I made with the Legos my wife got me for Christmas. It's a spaceship airplane race car. Despite leaps in technology from now to the Lego man's current time, he still has to talk into a walkie talkie that is bigger than his head.

This just in: While the number of Lego piece types increases with larger sets, the maturity of the Lego users exponentially decreases.

[Wired via @ajturner]

"We're finally in a position where people volunteer information about their specific activities, often their location, who they're with, what they're doing, how they're feeling about what they're doing, what they're talking about," said Johan Bollen, a professor at the School of Informatics and Computing at Indiana University Bloomington who developed a way to predict the ups and downs of the stock market based on Twitter activity. "We've never had data like that before, at least not at that level of granularity." Bollen added: "Right now it’s a gold rush."

Or you could just get yourself a flux capacitor and save yourself some time.

[Boston]

Likewise, the scientists would have to reject an ego-driven perspective and embrace the distributed intelligence that the project demanded. Some were still looking for that silver bullet that they might find all by themselves. But that represented the antithesis of how we would ultimately succeed. We learned to depend on a philosophy that embraced multiple tracks, each contributing relatively small increments to the success of the project.

As I sit here reading about egos within IBM, with the NFL playoffs in front of me, I can't help but smirk.

[New York Times via Simply Statistics]