thauck

I've authored a Packt book - it isn't of high quality.

It was a good experience, but they don't provide the resources needed (help and monetarily) for doing good work.

http://gist.io/ is kinda like that.

There's a lot of claims (and rightfully so) about the right losing the demographic game to the left. If there's one area where the right may be on the "right" side with young people is privacy.

Big opportunity for them if they play this correctly.

Analysts in finance/consulting certainly haven't had to upgrade their skills, but analysts in media/marketing definitely have had to.

Certainly there are people in this space who can't do much beyond spreadsheets, but there are many analyst now who use python/pandas or R to do work.

A few projects now have a "good as a first PR" tag for issues... This is great for helping people get started.

Fernando Perez (the force behind IPython) has done several in depth tutorials at various pydata confs... I'd start there.

Thanks for this working on this project. I've used it before with success.

I don't think python on hadoop is the answer for complex map/reduce jobs, but this project has fit an niche for me.

This is my biggest gripe, otherwise I like it. Visually the design is pretty, but in terms of representing information it leaves a lot to be desired.

As always, it's how it's presented that matters.

I always ask for general demographic information when I interview with a company. It isn't about being sexist (well maybe it is against men)... I just don't want to work with a bunch of mid-20s white males. Not enough diversity of thought.

Disclosure: I'm a mid-20s white male.

At this point, I might argue for IPython Notebook.

Correct me if I'm wrong, but the only reason the matrix being sparse is mentioned is because the author stuck a bunch of 0s in the matrices... this solution should work regardless of the sparsity.

As a person who's done GA implementations and subsequent reporting on around 100 websites this is correct.

On a side note, a tangential GA issue was recently discussed on HN and nearly everyone had a misunderstanding of how GA worked in that thread too... sometimes I'm worried that developers will get wise to analytics and I'll be out of a job, and then I come here and my fears allayed.

The thing is, GA is very simple and it all works together. I'll leave the below link here, because I think a lot of people could get value from it...

http://cutroni.com/blog/

With all due respect that's a long time to use GA without understanding how it works. Here's a good article by Google's GA evangelist I would highly recommend to everybody in this thread who are throwing out anecdotes.

http://cutroni.com/blog/2012/02/29/understanding-google-anal...

Like has been mentioned by many, this is incorrect interpretation... and quite common to see on blogs or single page sites. Although technically it's not dependent pageviews, but interactions (so pageviews or events).

So, one common way to handle this on blogs is to use setTimeout in conjunction with an event. Basically you fire an event after 15 or so sections which will then count as an interaction.

Some would argue why it can't be more like IPython. :)

But agreed, good work... tab completion is very helpful when learning a language.

I know how you feel... I didn't start until I was 21 and I'm 25 now. If you try to compete with somebody who has been coding since they were 10 on pure coding talent you'll lose.

But that's fine. Use your cross-functional skills to your advantage... whatever you did from ages 8-21 when combined with the coding you did from 21-present is likely just as (if not more) valuable than the skills a person who's been coding since childhood.

That's pretty nifty - thanks.

EDIT: Also, I have used bpython, but since I use python more on the data side as opposed to web (or something not data) IPython along with everything else make it too good to not use.

Bpython is good, but IPython with %edit and knowledge of a command line text editor has changed my workflow[1]. I wish more repls had this ability.

[1]: Maybe you can do it Bpython and I'm just not aware.

I know MBAs get shit on a lot around here (and I am not one, but close) this really is a simple expected value proposition. Something an MBA is introduced to early.

On the one hand he can sell his start up for a thus undisclosed amount of money, on the other he can continue on and have some expected payout (could be 0 could be a lot). If `expected sell amount` < `continue amount when sold`, continue, else sell. Certainly you have to consider time value of money, the future opportunities of having financial security.

Given the stats presented (2% of comp sell for > 2 million) we could deduce a lower threshold for what he sold for assuming the above behavior... but really he did what was best given the circumstances...

Congrats

Two things:

1. I wish the author would define "market share" - is it % of users, percent of pageviews via that browser, or something else.

2. The adoption visualization really shows the difference between release strategies.

I mostly agree with this, because I do like Ruby even though I don't use it.

The missing link here, and the reason Python gets more love from the data community, is that Python scales down to the smaller data sets as well as it handles big ones. (Not sure if you ment it couldn't, but the distinction you make implies that.)

This is similar to the Adobe article on HN yesterday. Link bait for real improvement through statistical analysis, then the article undelivers.