Realizations in Biostatistics

One of the things I'm realizing from Massively Open Online Courses (MOOCs) -- those online free classes from universities that have seem to sprung up from almost nowhere in the last year and a half -- is that they offer a perfect opportunity to explore outside my field. At first (and this was even before the term MOOC was coined), I took classes there were just outside my field. For instance, I've been in clinical and postmarketing pharmaceutical statistics for over 10 years, and my first two classes were in databases and machine learning. I did this because I was aching to learn something new, but I figured that with a class in databases I could make our database guys in IT sweat a bit just by dropping some terms and showing some understanding of the basics. It worked. In addition, I wanted to understand what this machine learning field was all about, and how it was different from statistics. I accomplished that goal, too.

Since then, I have taken courses in the area of artificial intelligence/machine learning, sociology and networks, scientific computing (separately from statistical computing), and even entrepreneurship. I have also encouraged others to take part in MOOCs, though I don't know the result of that. Finally, I have come back to some classes I've already taken as a community TA, or former student who actively takes part in discussions to help new students take the class.

This is all valuable experience, and I could write several blog entries on the benefits. The main one I'm feeling right now is the feeling that I'm coming up for air, and taking a sampling of other points of view in a low-risk way. For example, though I don't actively use Fourier analysis in my own work, one recent class and one current class both use it to do different things (solve differential equations and process signals). Because these classes involve programming assignments, I've now deepened my understanding of the spectral theorem, which I only studied from a theoretical point of view in graduate school. I'm also thinking about this work from the point of view of time series analysis, which is helping me think about some problems involving longitudinal data at work.

From a completely different standpoint, another class helped me think about salary negotiations in terms of expected payoff (i.e. combination of probability of an offer being accepted vs. salary). This sort of analysis invited further analysis of the value of that job vs. what I would be paid and the insecurity of moving to a different job. In the end, I turned down what would have been a pretty good offer, because I decided it did not compensate for the risks I was incurring. The cool thing is that these were all applying concepts I already understood (expected value, expected payoff), but applied in a different way from what I was already doing.

The best thing about MOOCs is that the risk is low. All that is required is an internet connection and a decent computer. Some math courses may require a better computer to do high-powered math, but I've seen few that require expensive textbooks or expensive software. Even Mathworks is now offering Matlab at student pricing to people who are taking some classes, and Octave remains a free option for people unable to take advantage of it. And, if you are unable to keep up the work, there is now downside. You can simply unenroll.

Caltech's Machine Learning MOOC is coming to an end this spring, with the final session starting on April 2. There will be no future sessions. The course has attracted more than 200,000 participants since its launch last year, and has gained wide acclaim. This is the last chance for anyone who wishes to take the course (http://work.caltech.edu/telecourse ).
Best.
The Caltech Team

I strongly recommend this course if you can take it, even if you have taken other machine learning classes. It lays a great theoretical foundation for machine learning, sets it off nicely from classical statistics, and gives you some experience working with data as well.

If you were for some reason waiting for the right time, it looks to be now or never.

Caltech has an online course Learning from Data, taught by Professor Yaser Abu-Mostafa, that seeks to make the course material accessible to everybody. Unlike most of the online courses I've taken, this one is independently offered through a platform created just for the class. I took the course for its second offering in Jan-March 2013.

The platform on which the course is offered isn't as slick as Coursera. The lectures are offered through a Youtube playlist, and the homeworks are graded through multiple choice. That's perhaps a weakness of the class, but somehow the course faculty made it work.

The class's content was its strong point. Abu-Mostafa weaved theory and pragmatic concerns throughout the class, and invited students to write code in just about any platform (I, of course, chose R) to explore the theoretical ideas in a practical setting. Between this class and Andrew Ng's Machine Learning class on the Coursera platform, a student will have a very strong foundation to apply these techniques to a real-world setting.

I have only one objection to the content, which came in the last lecture. In his description of Bayesian techniques, he claimed that in most circumstances you could only model a parameter with a delta function. This, of course, falls in line with the frequentist notion that you have a constant, but unknowable "state of nature." I felt this way for a long time, but don't really believe it any more in a variety of contexts. I think he played up the Bayesian v. frequentist squabble a bit much, which may have been appropriate 20 years ago but is not so much an issue now.

Otherwise, I found the perspective from the course extremely valuable, especially in the context of supervised learning.

If you plan on taking the course, I recommend leaving a lot of time for it or having a very strong statistical background.

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!