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:
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:
The disastrous impact of recent hurricanes, Harvey and Irma, generated a large influx of data within the online community. I was curious about the history of hurricanes and tropical storms so I found a data set on data.world and started some basic Exploratory data analysis (EDA).
EDA is crucial to starting any project. Through EDA you can start to identify errors & inconsistencies in your data, find interesting patterns, see correlations and start to develop hypotheses to test. For most people, basic spreadsheets and charts are handy and provide a great place to start. They are an easy-to-use method to manipulate and visualize your data quickly. Data scientists may cringe at the idea of using a graphical user interface (GUI) to kick-off the EDA process but those tools are very effective and efficient when used properly. However, if you’re reading this, you’re probably trying to take EDA to the next level. The best way to learn is to get your hands dirty, let’s get started.