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Comment by coderdude | Hacker News Reader

A couple things off the top of my head: -Web apps geared towards the emerging thin-OS netbook market

-I think Android apps will kick off in a much bigger way this year with more people getting their hands on phones supporting the OS. It's still early enough to get into IMO.

-On this page Veera suggested the Semantic Web, and while I have big hopes for the Semantic Web (I should, I blog[ged] about it), it's just not going to mature into what I think you're looking for in the amount of time you're looking for.

-Tichy suggested Data Mining, which I'm getting very into as of late, so maybe I'm not in the best position to give an impartial opinion but I think that will be taking off more. :)

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There is no evidence at all that an alternative OS is going to hit off in any significant way. Netbooks are popular cos they run Windows XP. I expect it to stay that way.

Android is a gamble.

Data Mining will make a lot of money for the person who can explain his data mining app in one sentence without ever using the words data or mining.

So what would you recommend be the first step in learning about Data mining? Any resources/books you can suggest?

[I figured since you just got into it ... ]

Learn some linear algebra. Literally every how'd-they-do-that-technique from OCR to PageRank is based on putting data in to a matrix and extracting some eigenvectors.

I took LA in college, but my professor was no where near as good and on topic in relation to computer algorithms as this guy: http://ocw.mit.edu/OcwWeb/Mathematics/18-06Spring-2005/Cours...

Oh, check out R. There are a lot of free papers and example code showing say, how principal component analysis on a dataset of migratory geese can be used to explain so and so, along with generating some pretty pictures.

It's a really nice bit of OSS!

"Programming Collective Intelligence: Building Smart Web 2.0 Applications" is worth checking out in Borders to get a sense if it's gobbledegook amazon link: http://bit.ly/5JlRz7

I may have mislead you. I occasionally refer to information extraction (through nlp) as data mining. It's a bad habit, but data mining just sounds cooler.

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