Fertility is something people don’t typically discuss openly in the US, which isn’t a surprise because it is an incredibly personal topic. In fact, it’s really difficult to even write a blog post about, I wrote this over a year ago and I’m only getting around to posting it now. It took us roughly 7 months to conceive a baby, and I’m proud to say we now have a happy baby boy!
However, every negative pregnancy test you see takes an emotional toll on you (and can even put strain on some marriages). During that time, I found that research online wasn’t extremely helpful. My wife and I found it relatively difficult to find answers to two very important questions:
What are the odds of a couple conceiving each month?
How much of a factor does age play?
I need to start this off by saying, I am not a doctor (nor do I play one on TV). In fact, I’m just going to start my exploration of this topic by first reading some blogs on the topic. This isn’t typically a great option, but then again, I’m writing a blog as well… What could go wrong, a blog based off of other blogs which might be discussed in another blog? I digress.
Recently, I started looking into data sets to compete in Go Code Colorado (check it out if you live in CO). The problem with such diversity in data sets is finding a way to quickly visualize the data and do exploratory analysis. While tools like Tableau make data visualization extremely easy, the data isn’t always properly formatted to be easily consumed. Here’s are a few tips to help speed up your exploratory data analysis!
We’ll use data from two sources to aid with this example:
Is George Washington better looking on the dollar bill or represented by a word cloud built with the text of The Constitution of the USA?
A colleague recently asked me that exact question. If you want to be taken seriously in the data science world, you better be able to answer something like this!
I decided that it would be fun to show off a Python package by Andreas Mueller called word_cloud (here) to make a fun image with the text of the Constitution and an image of one of the Founding Fathers.
I must warn you, word clouds are like pie charts people like the way they look but clouds don’t provide much information. That said, this package is really neat because it allows you to easily turn text into images utilizing masks, colors, and numpy!
I’ll keep this post short, what you want to do is simple:
Select an image which you would like to mimic in both color and shape
Read your image into Python using numpy
Read your text into Python using open() and read()
Anyone old enough to remember the Monty Hall problem from the old TV Show Let’s Make a Deal? It’s a classic probability problem – but despite its simplicity, it can be hard to understand what choices to make to maximize your odds of winning.
This is the problem:
You are a contestant on a game show. The host displays three doors. One has the brand new car behind it while behind the other doors have goats behind them. Here’s a beautiful image of all possible options you would have: Continue reading →
A little while ago I did a brief tutorial of the Google Vision API using RoogleVision created by Mark Edmonson. I couldn’t find anything similar to that in R for the Microsoft Cognitive Services API so I thought I would give it a shot. I whipped this example together quickly to give it a proof-of-concept but I could certainly see myself building an R package to support this (unless someone can point to one – and please do if one exists)!
A quick example, sending this image retrieved the location of the human face and created a caption! Here’s my dog lined up next to his doppelganger: