Motivation
Sometimes it’s hard to step back from your day-to-day routine and look at how you work instead of what you are working on. Over the years I have come to realize that my set of computer tools has grown unnecesarily large. I have had access to commercial packages like Matlab and Mathematica which are great tools for data analysis and reporting. In the lab I needed Labview code to run my instruments and collect my data. And then, I’d throw in some MS Excel and Powerpoint, and maybe even some MS Access for database management. The main issue that I began to notice was the delay between collecting data and analyzing data. The former would take place in the lab, and the latter at my desk using my installation of Mathematica. Presumably, Mathematica can be made to talk to instruments through serial ports, etc., but the license is too expensive to justify mutliple copies in all the labs. So my typical day would involve collecting a small set of data in the lab, analyzing it at my desk, returning to the lab to tweak the experiment, then back to my desk to verify, and repeat.
So after some research I started to teach myself Python. The combination of Numpy and Scipy created a pretty complete set of tools for data analysis, and all the standard Python libraries meant that instrument interfacing was pretty straightforward. So Python promised to be my Swiss army knife solution. Then I started using the IPython notebook and realized that I could collect my data and at least perform preliminary analysis and graphing on any computer I wanted (thanks to the open source licensing of these tools). The net result is that the time it takes me to go from measurement to understood data and conclusions has been significantly reduced.
So if you have noticed some inefficiencies in your scientific computing workflow, you should seriously consider the Python environment. Not only is it active and growing very rapidly right now, it already presents a very nice solution from data collection to analysis and reporting.
Demotivation
In high school I wanted to try out for the soccer team. I had played when I was younger and did pretty well, and thought that maybe I could make the team. On the day of try-outs the first thing the coach instructed us to do was to run as a team what I believe was about 4 miles around the local neighborhood. Now I had not prepared for try-outs at all, so I quickly found myself at the back of the pack, and as I drifted further and further behind I ultimately just gave up and abandoned the try-out.
There are several things I learned from this. The obvious one was that if I really wanted it I should have prepared! But for my purposes here I think I became completely demotivated to continue because the goal was far too big for me and my capabilities at the time. Even if I completed the run I would have had nothing left to show the coach what I could do with a soccer ball. When we make a goal or a plan it is important to make it achievable. Of course we need to push ourselves with “stretch” goals, but if the goal seems unachievable it can be very demotivating.
So in the process of learning new computer skills, I would encourage students to keep a manageable goal in mind and work towards it. Don’t expect to obtain expert skills in just one session. Each new achievement leads the way to another and over time you will find yourself making the team.