Have a plan

When a first attempt at a seemingly straightforward task fails, it's often a good idea to ask if maybe the task is less straightforward. "Start studying for grad school" sounds obvious until you start to do it. Once underway, you may realize that the intervening 25 years has scattered your neatly organized knowledge base.

That's not necessarily a bad thing. Like any efficient storage system, your brain caches the stuff it uses a lot and pushes everything else to slower storage (or just lets it atrophy off entirely). However, when you try to bring all that old information back up to the front, the neatly defined boundaries of subjects and courses have all been lost. It's just a huge collection of loosely related facts that you can bring back with varying degrees of speed and accuracy.

So, it's time to put some structure back on it. Here's the high-level organization. The details and actual plan will emerge over the next few weeks.

• Remedial mathematics: if I'm going to ask for credit for these courses, I really should know the material. Three major subgroups present themselves:
• Algebra
• Measure/Probability Theory
• Statistics
• Remedial computer science: I'm less worried about big gaps here because this is much more closely related to my everyday work. Still, a review is in order, not to mention formalizing many of the topics that I've learned since undergrad.
• Programming Paradigms and Languages
• Data Structures and Analysis of Algorithms
• Design patterns
• Systems/Networks/Hardware - probably won't spend much time on these prior to fall.
• Data storage and retrieval: this is split out from the above group because it constitutes my primary research interest and it contains my current area of expertise (Data Architecture). So, the study here will be more exploratory - basically, the start of my literature review.