If you majored in something quantitative there is just no need to take CS courses in a traditional college format. Online courses are on the same level or better if you can study by yourself and know enough math.
No idea about parent's background but in my experience those jobs are almost impossible to get if you are not already "inside". HFT is probably the most competitive part of finance for software engineers and the market is not expanding anymore. Same goes for other kinds of quantitative trading, but they generally pay less and are less competitive nowadays.
I really don't get why you consider 30 old or even near that. In my opinion it is a good time to do some self education, maybe change careers because many people don't realize what they really want until 30 or so. "Industry expert" on the other hand sounds plain boring.
300k for 40 hours a week in quant finance after 5 years in Boston? You are very lucky indeed. Most people don't make this much after 5 years in New York and usually work much more.
Disagree. If you have an easy recipe feel free to share :). You have to be a front office VP to be guaranteed this kind of money. Aleynikov made 400k as a senior VP working on HFT infrastructure at Goldman.
This comment makes it hard to believe you. Google has not been known to pay half a million dollars to regular people. And no one in their right mind would call that salary low, except maybe a spoiled Wall St banker.
I feel this would be quite confusing for an average programmer. It is more like a cheat sheet for people who have some statistical training but always have to look the formulas up because they don't use them frequently enough. For an average programmer, really understanding how linear regression works and some basic linear algebra would be a good start. A lot of programmers have trouble even with these "simple" topics.
Most of these formulas are very rarely used even by quantitative analysts. The most used are for standard deviation and regression. The more complicated ones are generally used as a part of statistical routines, say, in R. It is very rare that someone has to code them.
> From a statistical point of view, 5 events is
> indistinguishable from 7 events.
What is this supposed to mean? There is a concept of statistical significance but if an effect is not statistically significant it does not follow that it does not exist. Btw where is the Bayes formula? :)
Well, I know several software engineers with kids in NYC. Some live in NJ, some live in Brooklyn, some live in Manhattan. In fact, among married immigrants almost everyone has at least one kid after 30. Often mother does not work or works part time and looks for kids. Almost everyone of the "extreme commuters" has kids.
Tuition and health insurance is about the same everywhere. Housework is not that hard, maids are not that expensive. Absolute cleanliness is not something that kids require, neither is owning a (2-bed, 3-bed, huge) house.
In my opinion, your restrictions are quite arbitrary. So you would not have kids while working as a software engineer, but plenty of people do.
"A mixture of street counts and estimates indicated 557 people slept rough on any one night in London
5,678 different people slept rough over a year in London (April 1 2011-March 31 2012)"
"According to SFGov, the total number of homeless individuals and families in San Francisco for 2011 was 6,455."
There are definitely more homeless as percentage of population in San Francisco though.
> exercise to write a function which returns a boolean in
> response to the question of whether sequence A is a sub-
> sequence of sequence B.
If you can't solve this really fast it means you haven't practiced enough for the interviews.
> of course calculating the permutations of a list is n-
> squared
If I understand you correctly, it is actually n!, because there are n! permutations of n objects.
Basically, your problem seems to be lack of preparation. You study a lot but you haven't studied things that are asked in the interviews well enough.
I would recommend "Cracking the Coding Interview" book. Of course you need to actually solve problems from it. There is lots of valuable advice there. Courses on algorithms on Coursera are also pretty good.