Name your child for success!

Shakespeare famously posed the question:

"What's in a name?"

The answer may actually be: quite a bit!

Your given name (and your family name, for that matter) likely contains a lot of subtle information about you and your history. For example, we often assume names correlate with gender (as I have in previous articles)... Except the gender identity of some common names has changed over history (examples here)! Your name may also correlate with your political affiliation or what job you have.

I recently wondered: do certain names correlate with brilliance or high intellectual achievement? 

To find out, I gathered a large dataset of full names from people with PhDs in science (from the IAU and AAAS), as well as the names of lawyers using several recent years of bar exam "pass lists" provided by WA, NY, and TX. In total I was able to easily (read: quickly) gather over 36,000 full names of scientists and lawyers!

With this corpus of highly educated names in hand, let's look at which are most common!

The most common names of scientists:

Right away you can see a dramatic trend: it's mostly dudes. In fact, of the top 100 most common names for scientists, only 14 are female!

The most common names of lawyers:

While mens names still dominate, there are definitely more women in top list. For comparison, of the top 100 most common names for lawyers, 50 are female! That difference is shocking to me.

The Dream of Spaceflight

Me and fellow member of team "Gagarin", at Space Camp (Huntsville, AL) circa 2001

This past Friday during a test flight over the Mojave desert the privately funded SpaceShipTwo crashed, killing one test pilot and severely injuring another. Combined with the destruction of a privately built Antares rocket during launch (thankfully nobody injured), it's been a hard week for the business of space.

Some worry these two accidents have been a damning setback to the dream. Here the dream I'm referring to is making space travel common, available to everyone. And to be clear, it's a LONG ways off... Currently it costs between $1 and $10 per lb to fly on commercial airlines (of course, we don't buy tickets by the lb, but its a round number). By comparison, launching something in to space is over 1000X more expensive, around $10,000 per lb. When private companies are able to make spaceflight an everyday reality, we'll be a lot closer to this dream.

Around 540 people have flown in space (and perhaps a few more if you believe militaries have flown secret missions). That's it! Fewer than 600 humans have ever left our world. Given that the World population is still increasing, I wondered:  Has the number of people who have flown in to space actually kept up with population growth?

In other words, even though it's insanely expensive to launch people, and very few have been fortunate enough to go, are we making any progress on the dream?

How Long Between Apple Releases?

As an Apple computer fan I've been a long time reader of Mac Rumors, a website that reports great Mac news and rumors. One great feature is their Buyer's Guide, which tracks the refresh history data (and rumors) to suggest when products are due for an upgrade.

I've been wondering if a) Apple products are actually getting cheaper over time, and b) if Apple is refreshing products faster?

Here's the base cost versus the days since last refresh for a bunch of current Apple products on Mac Rumors

Random Forest for Time Series Forecasting

I recently spent a week at the 2014 Astro Hack Week, a week-long summer school + hack event full of astronomers (and some brave others). The week was full of high level chats about statistics, data analysis, coffee, and astrophysics. There was a great crowd of people, many of whom you can (and should) follow on Twitter. Below is a quick post I wrote up detailing one of my afternoon "hack projects", which was originally posted on the HackWeek's blog here.

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After Josh Bloom's wonderful lecture on Random Forest regression I was excited to try out his example code on my Kepler data. Josh explained regression with machine learning as taking many data points with a variety of features/atributes, and using relationships between these features to predict some other parameter. He explained that the Random Forest algorithm works by constructing many decision trees, which are used to construct the final prediction.

I wondered: could I use the Random Forest (RF) to do time series forecasting? Of course, as Jake noted, RF only predicts single properties. As a result, RF isn't a good choice for doing trend forecasting over long time periods. (well, maybe) Instead, this would use RF to just predict the next datapoint.