But when compared to similar searches historically, you can draw conclusions from the trend.
If one assumes a similar historical probability between looking for a house and buying a house, it follows that if more people are looking for houses outside of SV, more people are also buying houses outside of SV.
At the very least, it shows that people in SV are more interested in housing markets outside of SV today than they were a few years ago, even if they don't move there.
Maxima seems very similar to Mathematica in principle. What does Mathematica have in favor? Far more mathematician/engineer-hours invested due to early (and then compounded) financial success?
Notebooks where all the rage in the 90's. I spent quite a few years (~15) with Mathematica and its "notebook". Of course, Maple, Mathcad and Matlab all had the same thing. At some point, I wanted to write more readable and modular code that others could use (and that I could reuse) so I switched to using and IDE for python, Java and C. My workflow enables exploratory data analysis as well as algorithm development. The huge advantage of IDE's vs. REPL's is that in the end, it's easier to write the quality of code that is ready to be shipped off to a production server. I have watched the "Return of the Notebook" with some trepidation: yes, it allows quick iteration and lower barrier to entry, but ultimately if your goal is to create a software product, learning good coding skills in an IDE is a much better path.
> ultimately if your goal is to create a software product, learning good coding skills in an IDE is a much better path
IPython developer here. A lot of the use cases for notebooks are where you're not creating a software product. It's useful where the product you're really after is a scientific result, some plots, or the like, along with a description of how you got that. And where the product is a presentation, a demo, or documentation.
Of course, as you're doing that kind of thing, code that starts out in a notebook often becomes something you want to reuse, and you therefore move it out to an importable module. We are interested in ways to make that process more fluid.
This is one thing I struggle with. I usually start in a notebook and then eventually want/need to port to a module environment. It would be nice if there were some guidelines or support for this sort of transition. Any pointers?
I use it quite a bit for data analysis, in particular for user-defined functions with python. Being able to explore your data (via SQL and it's powerful syntax) in addition to functions and aggregators that I define, is REALLY useful. You could do the whole thing in python (and data imports), but adding the SQL part in is so much easier than building dictionaries and filtering, sorting etc...
wouldn't it be better to use existing structures, like all the abandoned malls in the usa? why not distribute the workforce so people can live in an affordable, commutable area? and it bugs me further they couldn't find a way to locate walking distance to bart or caltrain.
I used to work for a company that leased spaced out of an old mall. They also built their own huge new corporate office campus and moved everyone into it while I was there.
I actually preferred the mall space. There was a movie theater and a drivers' license annex in it, and some lunch-friendly restaurants. The commute was faster. Parking was never a problem. City buses stopped there.
The new corporate headquarters was in the middle of nowhere. You had to drive for 15 minutes to get anywhere else that you might want to be, including spiraling up all those ramps in the underground parking garage. It was well done architecturally, and very pretty, but I don't care about that while I'm working. And when I'm done working, I want to go somewhere else, and do something that is not work.
The supposed benefit to collaboration was not realized by reducing the walking time between offices. The people that worked together most often were still seated the same distance from each other, and from their regular conference rooms. Anyone not physically present was still brought in via conference call. The increased distance to anything else made it more likely that you would miss someone who was out to lunch, at an appointment, or on a personal errand. The start and end of ordinary business hours created localized traffic congestion.
So yes, it would be better to have a more distributed workforce.
Google management must feel there is a benefit to having their employees clustered into a small number of campuses. Maybe mountain view isn't the optimal place for that, but uprooting the campus and distributing it among failed shopping malls throughout the world or building a giant googleplex in Montana is probably not a good strategy to keep the bulk of their employees.
MDBM was designed to be fast with special care taken on the lookup path. The goal was to do lookups with as few cache misses as possible. You can get to any key with at most two page faults.
Or ... you could stay in the city and get an MS in Analytics from University of San Francisco at the downtown 101 Howard campus. http://www.usfca.edu/analytics/
The program is excellent, has a REAL practicum experience and boasts nearly 100% employment 3 months post graduation. It comes in at about half the cost of cal's program. [Full disclosure ... I'm an adjunct there] Here's what you can learn in one year
http://www.usfca.edu/artsci/msan/courses/
The combination of location, instruction and work experience is unprecedented.
