This is the best high-level advice I have seen so far in this list: business infrastructure FIRST!
Don't forget, that includes a "business model" (basically, what do you plan to offer and how will you make money in the process of delivering it) plus "customers" (you know, the people that pay for what you plan to offer).
Good ideas are important, but a business really needs the model and customers. Otherwise, it is called a "hobby".
Good luck.
From my own (painful) experience: if you don't plan for it up front, you are always fighting fires (playing catch-up). Organizing your data can help a LOT! If it is media, arrange it by genre (e.g. video animation or video classical or whatever) to keep a particular grouping small enough to backup easily. If it is data, arrange by some category that works for you (e.g. current financial projects or past analytic projects).
The most useful guide I have found for resources allocated to backup: how much is it worth to me to re-create this resource? ("Worth" can be money, time, sentiment, or any other measure(s) or combination you chose.)
My current feelings: disk is the most versatile and cost effective.
As a university instructor who encourages the use of laptops in class, I feel it is mostly a matter of focus. (I am teaching intro stat and operations research.) I tell the students to bring a laptop, but it is a tool and we will be looking at how to use that tool to solve problems. (I also tell them that if they want to watch youtube or hulu they should sit at the back of the class and use headphones to keep the distraction down for the other students.)
The (stated) speed-up could be nice, but:
(1) how locked-in is it (just some tuning, serious modification, what?)
(2) have they actually released it?
Pretty quick read with some nice old pictures.
One thing I did not find: reference to the MAT statements in the old BASIC and how M$ dropped them. They were a very interesting feature at many levels.
What do you mean "slowly warming up to the idea"? As opposed to "a cold day in
Reality checks bounce ALL to often down here.
I would disagree w/ "Most academics are pretty clueless about statistics", but agree that there are opportunities there. Big Data is opening more possibilities for computer intensive statistics and the GPUs are a current tool to make these possible in a "reasonable" amount of time. For example, check out the book "Bootstrap Methods and their Application" by Davison and Hinkley - it is old at this point, but very worth reading. (Inter-library loan can come in VERY handy here.
Working in Statistics generally can be interesting. I have gotten paid fairly well to have experts teach ME about their field and specialty so that I can then advise them on how to analyze their data. (If you don't know where it came from, you can't really interpret the data.) For me, it was like having a hobby I got paid for
Finally, formal training and credentials are helpful (but not ALWAYS necessary) in Academia. Results are critical in or out of Academia. You can freelance or look for someplace that has an in-house consulting group. Getting a "client base" takes time but could be a path for you to start a freelance practice.
Good luck.
My reading of the article brought out the point that, for a few extra bucks, you can actually go to the head of the line - giving the window of opportunity to perform the other actions described. Interesting definition of "free market"
Always draw your curves, then plot your reading.
