Best Coursera Yet

I finished my third Coursera course tonight: Data Analysis, eight weeks of learning R and its application to statistics problems. I've enjoyed all three, but this one topped them all. It was a difficult eight weeks. I spent a lot of hours at night after work and weekend time poring over assignments. It's been tiring but worth it. I've remembered the statistics I'd forgotten, learned a lot of new things like generalized linear models, and deepened my knowledge of R. It's exactly what I set out to accomplish when I started taking on-line classes a year ago at this time. I wanted to see if I could adapt to a new style of learning and self-education. I wanted to prove that I could still absorb challenging material. I'm at an age when it's easy to sit back and tell yourself that you already know it all, that you're too old a dog to learn new tricks. Did I still have it in me? I think I succeeded on all counts.

The professor was Jeff Leek, who's on the faculty of the Johns Hopkins biostatistics department. They claim to be pre-eminent in the world, and after sitting through this course I believe them. His graduate students are fortunate.

I've earned certificates with distinction for the two other classes I've taken. I calculated my point totals for quizzes and assignments for 'Data Analysis'. I think I squeezed out another distinguished certificate. I'll have to wait a week or two to see if my numbers match those from Coursera, but I'm confident that it'll turn out well.

I've got a lot of other tasks lined up. I want to take more of these classes next year, but I'm uncertain about what to take. I want to go further with R and statistics, but there's no clear choice listed in the catalog. I might start haunting Kaggle.com and applying these new skills to problems. I've got some development tasks to get back to.

I found out that I'm two speeches away from achieving the Toastmasters Advanced Leadership Bronze designation. I'll fulfill those easily in the next few weeks. That would be three awards in one fiscal year. I'll only have ten speeches to achieve Advanced Communicator Gold and the Advanced Leadership Silver to become a Distinguished Toastmaster. Who would have thought it'd culminate in this when I started in 2008? Maybe I can do it by mid 2015.

I plan to relax a bit over the holidays. It's been a nice way to end the year.

Another One Bites The Dust

I just finished my second Coursera course. It's hard to believe that "Data Analysis Using R" started just four weeks ago.

This wasn't a terribly difficult course, but lecture time added up. The assignments weren't hard conceptually, but I found myself struggling with the API and the docs. "How do I do that?" was a common question. It was easy to think how I'd do something in a language that I knew better, like Java or Python, but I wasn't always able to conjure up the R equivalent at will. I had to do small experiments on the fly to figure out how to make the language do my bidding.

The third programming assignment set was time-consuming. I was behind the eight ball because I was out of town at a family wedding the weekend before it was due. Thankfully we were given an allotment of late days that we could apply as needed. I used up three of them after returning from MN so I could get the assignment in late without penalty.

Debugging in the R environment is crude, reminiscent of the gbd command line debugger that comes with Java. It's a comedown for a person who's used to using the best IDEs in the world to work with Java and Python. I started using the R plugin for IntelliJ for the fourth assignment. I hope they keep expanding and improving it.

R does have some rudimentary object-oriented features, but it's firmly in the functional camp. A friend pointed out that R looks similar to JavaScript. As usual, he's right. I started eschewing the '<-' assignment notation in favor of '=' to make the visual resemblance stronger. The Renjin interpreter makes it possible to run R inside Java. I found out that you can also run R on CUDA. I'm looking forward to trying those packages and seeing what performance benchmarks would look like.

This one was a sprint. The next one, "Data Analysis", will emphasize problems that R is used for, diving deeper into regression and analysis. I'm looking forward to getting back in touch with my mathematics roots. It begins next Monday.

First MOOC

I've always prided myself on being a lifelong learner. I've been watching the rise of massive open-source on-line courses with great interest and curiosity. All of my education was done the traditional way: sit in a classroom with a lecturer and other students on a fixed schedule, do homework, take tests, let material diffuse into your brain over a week or a semester and hope it sticks.

I've never taken a class on line. How would it feel? Could it be as effective as the traditional approach?

I was excited when I first heard about AI class being offered by Peter Norvig and Sebastian Thrun. Peter Norvig is the Director of Research at Google and the author of Artificial Intelligence: A Modern Approach. He's written some terrific stuff, including Teach Yourself Programming In Ten Years and How To Write A Spelling Corrector. The latter is astounding. When I get on an airplane I get myself a drink and decide which movie I'm going to watch; Peter Norvig writes a statistics-based spelling corrector in 21 lines of Python code that's 70-80% accurate. It was a revelation to me when I first saw it.

I signed up and started with the best of intentions, but then Hurricane Irene knocked our power out for ten days and put me behind the eight ball. No Internet; no computer; no lectures.

My interest in statistics has been growing over the last few years. I've tried to better understand the Bayes approach - what it means and how it differs from the frequentist view that I've been exposed to. I've read Doing Bayesian Analysis Using R and BUGS by John K. Kruschke. Don't let the adorable puppies on the jacket fool you: this is a terrific, well-written book. I've got blog posts describing other books about Bayes that have caught my attention.

But I've still never taken a basic statistics course. I saw that Sebastian Thrun, one of the AI class instructors, was offering intro statistics at Udacity. I liked the lectures I saw him give for the AI class, so I thought I'd give it a go. I started just after Thanksgiving, with the goal of finishing before the end of the year.

The key for me is to make regular, concentrated effort, track my progress, and make sure that I avoid long gaps between sessions. I set up an Excel spreadsheet to record the date and units I covered. It was the same approach that got me through my first half marathon: plan the work, work the plan. It made it easy to see when I had a few days without getting another dose of learning.

I didn't meet my time goal of finishing before the end of 2012, but I didn't miss it by much. More importantly, I got through the entire course - every lecture, every assignment. The programming assignments were in Python, which I loved. I have the latest version of PyCharm - the Python IDE from JetBrains, makers of the best programming tools on the planet. I have NumPy and SciPy, two terrific libraries for scientific computing and numerical methods. It made programming a pleasure.

Most importantly, I proved to myself that I can take good advantage of all the courses on-line: MIT, Stanford, Coursera, Udacity, Apple U and others.

I would still like to revisit AI class. There's a course from Stanford called Probabilistic Graphical Models that presents Markov models in depth. Linear algebra from Gil Strang at MIT would be a treat and a privilege.

But my next choice is Computing for Data Analysis by Roger Peng. Coursera isn't offering it now, but it's available on YouTube from Simply Statistics.

All kinds of knowledge is available to anyone with a computer, an Internet connection, and the drive to take it in. What a time to be alive.

New Haven Bound

I got a message from my wife this afternoon: our youngest daughter was officially notified that she's been accepted into the Ph.D. program at Yale. She'll matriculate in the fall to begin work on her doctorate in biophysics and biochemistry. She'll graduate in May from the University of Connecticut with both her Bachelors and Masters degrees in molecular cell biology. It only took her four years to get both.

She went to visit New Haven Thu through Sun last week. She loved everything about it: the students, the professors, the programs, the campus. It sounded like everyone was enthusiastic about what was happening, and there's a lot going on. We thought it might take a few weeks to hear the final decision, but one of the professors - the one she most wanted to work with - called her personally to tell her that Yale hoped she would come.

I have never been one to claim the accomplishments of my children as my own, and I'm not going to start now. I will say that I'm so happy for her. It's a well-deserved opportunity that she's worked hard for.

She's always had an unusual focus. Even as a young child, she could play happily by herself for hours. (Until her beloved older sister came along with other alternatives.) She loved puzzles; she sought out jigsaws that would challenge an adult, let alone a child. She was ambidextrous. She would pass the crayon from hand to hand as she colored pictures. She finally settled on her left hand for writing and preferred status, but it was touch and go for a long time.

She was quiet and reserved. She chose her words carefully and thought before she spoke. She still does, but now the words and ideas pour out of her at a faster clip. She's a confident woman these days.

She's always been a fine student. She "gets it" with math and science. I used to ask her if she needed any help with math when she was in high school. I learned right away that she didn't need my help. It was always a conversation about a topic that we both enjoyed. She would talk to me about problems she was given more as a courtesy to me than a plea for aid. She knew that it made me happy.

I've had the pleasure of repeating that experience throughout her undergraduate career. I swim on campus on Friday nights. I'll call her every once in a while after I'm done and ask if we can get together. We'll hit a dining hall or Starbucks, grab some tea and dessert, and I'll hear about everything she's doing in the lab and the classroom. Sometimes it's chemistry; other times it's quantum mechanics, math, computing, physics, or something else like a one-credit class in weight training. It astonishes me to hear how well she understands what's presented to her. She has a way of digesting the message, internalizing it, and making connections well beyond the context in which it was presented.

She is a biology major at the University of Connecticut. At the end of her freshman year she researched professors on campus and solicited them for a job in their lab. She found Robert Birge and sent him an e-mail. Did he accept undergraduates to work in his lab? He said it was rare, but told her to send in a resume anyway. He took her in and gave her a thorough grounding in research and how to conduct herself in a lab. Dr. Birge, his wife, Connie, and his team of graduate students have been so generous to our daughter. She worked without pay in the lab that first summer, but they found funds to support her after that. They've been the best mentors and examples imaginable.

I feel so fortunate to have the children I do. I wish I could point to some thing or another that my wife and I did that made this happen. Maybe I could bottle it or write a book to out-do a tiger mom.

I believe it's a combination of factors: genetics and the good fortune of peace and enough prosperity at home to meet their needs. After that it gets fuzzier. We read to them a lot when they were small and beyond. It is a common sight to see one family member or another draped on a chair or couch in silence, reading a book. The house is still full of them. We had television around, but it wasn't the 24x7 bombardment that is cable today. We didn't forbid television, but it didn't crowd everything out, either. We've never had video games; still don't. We had a computer in the house when they were young, but the Internet wasn't the distraction it has become. There were no cell phones.

They played a lot by themselves. We have an infamous book of games that they wrote together when they were small. ("The Queen Game" was a special favorite.) The rules included stuff like "If somebody gets hurt, don't cry or we'll have to go inside." Some of them make the hairs stand up on the back of your neck, but they're funny to read today. They had a lot of opportunities to use their imaginations to entertain themselves. They played with other kids, but they spent a lot of time with each other, too.

My wife is an educator, so she's always been on the same schedule as the girls: home in the afternoon when they returned from school, able to spend lazy summers at home instead of being shipped around to camps. Quality time with children cannot be scheduled. You're either there when they're ready to spill or you're not. Being at the kitchen table after school with a snack put my wife in perfect position to hear what was on their minds. I'm glad that one of us was able to be there. They're lucky to have had her: she's so attuned to them, always knowing just what to do.

They weren't home schooled. We sent them to public schools, where they had more than a few outstanding teachers, such as Mark Logan, Jason Courtmanche, and Madame Bender, and that made a great difference.

Both our daughters are a great mix of mind, body, and spirit. They're just terrific to be around. I'm grateful for the vantage point I've enjoyed. It's a great show to see.

Yale, you're in for a treat.