YMMV, but my experience is that datasets aren't really usable for a particular endeavor without some munging; stripping unneeded columns, common-format date/times, etc. Also, I tend not to work on the seminal copy; too easy to alter it and screw up separate md5 sums, or worse. So, if I have to perform analysis, I tend to round up copies of all the relevant datasets in one directory. Finding them is still the key...

...but then google came along and taught me that it's not about know where things are, but rather about being able to find them. My email, for instance, is "organized" by the year in which it arrives, and I use the search function of my email client to find things. No big folder structure, moving messages around, and I haven't had problems finding any email I need. Oh yes, I keep them all... good fodder for "on x/x/xxx you said..." retorts.

For files, then, the key is to have descriptive file names that provide readily searched text. Including the data somewhere in the name (I tend to use this format because it sorts well: 20100815) makes it easier to sort through multiple versions.

Then, you can spend quality time figuring out how to reliably back up all that stuff.... :)

Having come from your world and making the same transition, albeit a little more graduated, I'd recommend Java as 1) the best language to transition into object-oriented programming, and 2)widely used.

OO is really important to understand if you want to do GUI programming of any sort.

Java is also very portable among the major hardware platforms.

That's what I get for posting before morning coffee... :)

Yes, the post was probably a bit hard-nosed, but I'm glad you recognized my point: it's what interests you that takes you to interesting places. There are two kinds of achievers: 1) Those that work hard at something, and 2) those that work hard as something that interests them. The latter benefit from the leverage of intrinsic motivation.

For my situation, I modify #2 slightly: Those that work hard at what comes easy to them. I am definitely a poster child for that... well, delete "work hard" and replace with "piddle at"...

...that you're more interested in the recognition than the achievement. Most folks I know who make real breakthroughs in a discipline are genuinely interested in the discipline.

I occasionally teach and mentor in a doctorate program, and my essential observation is that those who are interested in the topic have a higher probability of finishing than those who are "chasing the paper". Even those of the latter category who finish the program eventually find such a perspective catches up with them in the workplace or in academia.

I don't mean to sound trollish here, but you need to search your motivations and go for the thing that really interests you. That'll render reward far past achieving 'just something, anything' And that motivation will overcome obstacles such as home-based, etc. You'll find a way, if it interests you...

The most complex piece of Java (or any other language for that matter) I've ever worked with didn't have a single line of JDBC, SQL-whatever. There are a butt-load of scientific and real-time applications out there that don't have a DBMS anywhere in the code tree. And, if I'm on the hiring committee, not one of my big concerns for the things my company does (defense-related).

That said, DBMS-oriented programming is useful, but the world doesn't revolve around it. And yes, I have at one time taught it and programmed it (DBMSs, that is), so I'm not a "diametric myopic" here...

...in both developing and teaching, here's my take:

Computing:
    - Some kind of hardware (microprocessor, etc) course

Computational Math:
    - Discrete Math: Logic, Sets, Relations
    - Algorithms and Complexity

Application Math:
    - The Calculus
    - Statistics
    - Geometry & Trigonometry
    - etc... (depending on the industry to which you're attracted)

I really believe that the computational math is strongly supported by an understanding of the practical implementation of stored program computers. This helps both your programming and debugging/troubleshooting, in that you understand the impact of your little creation on the hardware.

Application math is application-dependent, ha-ha. I went for a long time both as a developer and teacher before I encountered advanced statistics or differential calculus; there are more folks out there doing this in aerospace than you might think.

I think the distinction between computational and application math helps to compartmentalize your consideration...

...are ones with which I have the most experience. WxWidgets IMHO is the best 'close-to-the-metal' API, with the most available constructs to allow me to implement in C/C++ the Perl prototypes I develop. But most recently I've been noodling with Java to develop a high-availability platform, and I regularly run multiple jvms in Windows command shells to build stuff out, and then take the classes unchanged to a ttylinux-SunJRE-based cluster I run with VirtualBox.

I'm not a Java advocate by any means, but you can't ignore the portability...