devilsdounut

Not sure 50k-90k for PhDs from top programs is really the case anymore. Companies hiring for data-science type jobs have always been pulling from the pool of quantitative PhDs, and as a result the pharma/genomics companies have been forced to raise salaries. Pure bench scientists would potentially have that kind of range, but anything quantitative is going to be much higher. This being said, there is a huge gap between those purely experimental scientists and the quantitative ones who have many non-science options.

Ok, so here it's used to cluster. There are tons of benchmark clustering datasets. Never seen it used on any of those.

I'd love to see a success story of this type of analysis outside of their canned examples. I keep seeing them use the same datasets over and over again without any real benchmarks to state of the art. Its amazing how a data product is being sold without any empirical studies or benchmark datasets.

Ayasdi seems successful to me in that it has a lot of flash and their results make intuitive sense, but I don't understand how a practicing data scientist would use this.

This brings up a big issue with this type of gamification. If someone prescribes a solution which has a superficial short term effect (think painkillers, etc), they may be rewarded in 'points' (e.g. money) because the patient is able to see a direct effect. This would reward treating symptoms rather than the underlying problem and could result in sub-optimal treatment. Unless this website is planning on adjusting rewards based on long term effects on the order of years, I'm not sure how this would be avoided.

EDIT: etc, as in et cetera

How does a company cover itself legally? People in the US love to litigate, especially when it comes to medical issues.

Interesting. Any advantages of this over IPython? I see fancy rendering of DataFrames, but there are libraries that let you do this in IPython.

Pretty big confidence intervals, unclear what the true test space is. I'm just going to leave this [1] here.

[1] http://www.tylervigen.com/

My favorite is "Uber for genetic testing". That is exactly what I want, a company like Uber completely ignoring all moral and legal rules with complete access to all of my genetic information.

Wolfram has a habit of creating blog posts about the test cases for their algorithms.

It uses tmpnb (http://lambdaops.com/ipythonjupyter-tmpnb-debuts) to spin up docker instances of a relatively basic IPython demo. I guess they are a bit overloaded right now. Pretty nice gateway drug to show the benefits of IPython to non-technical folks.

Looks pretty academic. I see no mention of data cleaning or more practical considerations in the table of contents.

I like Mathematica... while their magic functions don't exactly work for much outside of their demo's, it gives good fodder for open source projects to eventually borrow from. IPython would not be where it is right now with its killer notebook interface and interactive plots if it were not for them borrowing some key ideas from Mathematica.

Biotechs on the level that YC would fund don't have much incentive to ask for this money. The government has this one covered with the SBIR grants, which come with very few strings attached. While these are clearly not perfect, they don't leave too much room for low level investment.