MOOCs have exploded!

About a year and two months ago, Stanford University taught three classes online: Intro to Databases, Machine Learning, and Artificial Intelligence. I took two of those classes (I did not feel I had time to take Artificial Intelligence), and found them very valuable. The success of those programs led to the development of at least two companies in a new area of online education: Coursera and Udacity. In the meantime, other efforts have been started (I’m thinking mainly edX, but there are others as well), and now many universities are scrambling to take advantage of either the framework of these companies or other platforms.

Put simply, if you have not already, then you need to make the time to do some of these classes. Education is the most important investment you can make in yourself, and at this point there are hundreds of free online university-level classes in everything from the arts to statistics. If ever you wanted to expand your horizons, now’s the time.

I’ve personally taken 7 online classes now, and earned certificates in all of them. I use the material in many of these classes in my work, and I even have used two (Machine Learning and Probabilistic Graphical Models) to expand my company’s capabilities. I am far more secure in my job because of what I’ve learned. In addition, I had the honor of trying out the Probabilistic Graphical Model Community TA program, and my only regret is that I couldn’t put more time into it. To the extent that I took advantage of it, I got a lot out of the experience.

Now, the hard part. These classes require self-discipline. Like universities, there are some duds as well. At least you can add and drop at will, not worrying about prerequisites. You have to take responsibility for your own education and your own motivation.

In all, I’m very grateful that there are these pioneers Andrew Ng, Daphne Koller, Sebastian Thrun, and others who saw this need and had the knowledge and motivation to fill it. They are now moving in the direction of accreditation, and both free and premium models (probably for some kind of licensing or degree, which I don’t care about right now). For now, you can sign up and take classes at will.

Happy MOOCing!

Rare things happen all the time

John Cook reports on the probability of long runs. This is a very useful reality check.

I think there is a larger principle here, though, that rare things happen all the time.

Analysis of the statistics blogosphere

My analysis of the statistics blogosphere for the Coursera Social Networking Analysis class is up. The Python code and the data are up at my github repository. Enjoy!

Included are most of the Python code I used to obtain blog content, some of my attempts to automate the building of the network (I ended up using a manual process in the end), and my analysis. I also included the data. (You can probably see some of your own content.)

Here's what I learned/got reminded of the most:

• Doing projects like this is hard when you have other responsibilities, and you usually end up paring down your ambitions toward the end
• Data collection and curation was, as usual, the most difficult process
• Network analysis is fun, but I have a ways to go to know where to start first, what questions to ask, and so forth (these are the things you learn with experience)
• The measures that seem to be the most revealing are not always obvious -- in this network, it was the number of shortest paths compared to a random graph
• Andrew Gelman's blog is central (but you probably don't need a formal analysis to tell you that)
• There's a lot of great content about statistics, data analysis, data science, and statistical computing out there. I've relied on blog posts for a lot of my work, and I've found even more great stuff. It's a firehose of information.

Snapshot of the statistics blogosphere

This was generated during my social network analysis project. I haven’t finished yet, but I did want to show the cute picture. The statistics blogosphere is like a school of jellyfish.

Sometimes, saving CPU time is worth it for small data jobs

There appears to be a conventional wisdom, one that I myself have espoused on several occasions, that for “most” statistical computing jobs that developer time is more precious than CPU time. (The reason I write “most” in quotes is that there are some people who work in environments where Big Data or large jobs is the norm, or they are developing high performance computing libraries, and they have to squeeze every last bit of performance out of the CPU.)

However, sometimes it can be worth it to save a few extra minutes small jobs, especially if they are run over and over. At one point today, I had an algorithm that I wrote inefficiently using Python’s built-in lists. I decided to stop the job and rewrite using the NumPy libraries, which took me an extra half hour. At first, I thought the time was wasted, but I have ended up running the code several times for various reasons. Those save minutes have now, a couple of hours later, saved me more time than I spent rewriting.

Politics vs. science and the Nate Silver controversy

I’ll take a small departure from the narrow world of biostatistics and comment on a wider matter.

Nate Silver of FiveThirtyEight has really kicked the hornet’s nest. This is a nest that really needed stirring, but I do not envy him for being the focus of attention.

This all started, I think, when he released his book and basically called political pundits out for a business model of generating drama rather than making good predictions. This wouldn’t be a huge deal, except that he has developed a statistical model that combines data from national and state polls with demographic data to project outcomes of presidential and senatorial elections. This model, as of this writing, has President Obama at close to an 81% probability of re-election, given the current state of things. As it turns out, there are a lot of people that don’t like this, and they generally fall into two camps:

1. People who would rather see President Obama defeated in the election, and

2. Pundits who have a vested interest in a dramatic “horse-race” election

I’ll add a third:

3. Pundits who want to remain relevant (whether to keep their jobs or reputations).

Frankly, I don’t think that pundits will have to worry about #3. There’s an allergy to fact in this country, a large group of people who would rather ignore established fact and cling to a fantasy. (You can find a sampling of these people over at the intelligent design blogosphere, for instance.) I think the demand for compelling stories over dry facts will remain.

I’ve run into people of the first type, when I’ve published some armchair statistician analyses based on Twitter sentiment, for instance. The responses weren’t critiques of the method, but rather, “who cares, Republicans rule!” Even more dangerous, I’ve run into similar responses to negative clinical study results in cases where sponsors have a vested interest in positive outcomes. (There was at least one case I remember a sponsor moved forward with an expensive study to follow on, and some where I was asked to reanalyze a zillion times.)

Nate write The Signal and the Noise where he, among a lot of explanation, points out that there is a whole cottage industry of people getting paid to BS about politics. So I think that some in the second category are starting to face an existential crisis, and that makes them dangerous.

Ultimately, we have to understand where Nate is coming from to understand his prediction. His money is (literally – He made a bet[1] on Twitter with “Morning Joe” Scarborough of NBC) on Obama’s victory in the election, not necessarily because he wants Obama to win, but because he has confidence in his prediction. When he made the bet, he made the controversy more than just trading words, but he called Joe’s bluff (Joe had said that anyone not calling the race a tossup is an ideologue). We can now call him The Statistician Who Kicked the Hornet’s Nest – the punditry, including the public editor of the New York Times that hosts his blog, is collectively attacking him.

Unfortunately, the punditry has the upper hand, because people are more interested in the narrative than the science.

[1] The bet originally consisted of the loser donating $1000 to charity. Nate subsequently donated $2538 to the Red Cross before the election.

Willful statistical illiteracy

The fine folks over at Simply Statistics have a very good educational article about the difference between the probability of winning an election and vote share. This article stems from a controversial column over at Politico criticizing Nate Silver and his election forecasts.

Twitter responses are even worse. Conservative filmmaker John Ziegler calls Nate Silver a “hyper-partisan fraud” who is “not an expert on polls.”

Glenn Thrush mentions a “conservative 538:”

And it’s not hard to find other examples.

I’ve run into this reaction a bit, especially when it comes to politics. There are a large group of people, who will dismiss any evidence going against their beliefs. I guess the punditry wasn’t so dismissive of Silver in 2010.

At any rate, I give a recommendation I rarely give: read this Politico article and the comments (ignore the “conservatives aren’t bright” nonsense, which is the same stuff coming from the left).

And let’s thank Nate Silver, RealClearPolitics, and all the honest pollsters who try to shine some data on this election.

The most valuable thing about my little stat blog network project

So, I decided to construct the linking graph through blogrolls, and finally settled on using a manual process. The best part of this project is really finding out for myself all the great content out there!

SNA class proposal

I’ve been taking several classes through Coursera (nothing against the other platforms; I took two of the original three classes via Stanford and just stuck with the platform). The latest one is Social Network Analysis, which has a programming project. Here is what I have posted as a proposal:

Ok, I've been thinking about the programming project idea some, and at first I was thinking of analyzing the statistics blogging community, mostly because I belong to it and I wanted to see what comes out. The analysis below can be done for any sort of community. I've developed this idea a little further and wanted to record it here for two reasons. First, I simply need to write it down to get it out of my head and in such a way that the public can understand it. Second, I'd like feedback.

As it turns out, I took the NLP class in the spring and think there's some overlap that can be exploited. (This comes up nicely in the Mining the Social Web and Programming Collective Intelligence books.) There are measures of content similarity, such as cosine similarity, which are simple to compute and reasonably work well to see how similar content is. Content can then be clustered based on similarity. So, then, I have the following questions:

• What are the communities, and do they relate to clusters of content similarity?
• If so, who are the "brokers" between different communities, and what do they blog about? There are a couple of aggregators, such as StatBlogs and R-Bloggers, that I imagine would glue together several communities (that's their purpose and value), but I imagine there are a few others that are aggregator-like + commentary as well. Original content generators, like mine, will probably be on the edges.
• Is it better to threshold edges based on a number of mentions, or use an edge weight based on the number of mentions?
• If I have time, I may try to do some sort of topic or named entity extraction, and get an automated way of seeing what these different communities are talking about.

Nate Silver on The Daily Show

Watch it!

There’s an interesting conversation about how the campaigns use analytics in get out the vote efforts. It doesn’t go a lot in depth, but I think this is an important aspect of campaigns that will come out into public view in the next couple of election cycles.

Of course, you can find his blog at http://fivethirtyeight.blogs.nytimes.com/.