Stoltzmaniac Fans – It’s time for a #100DaysOfCode update.
I have completed 11 days of the challenge. Let me tell you, it has been a blast and I have already learned a lot. In this post I’ll walk you through what I’ve done thus far. Here is a link to the code on my GitHub repository.
As you may recall from my previous post I set out to create a flask application to host data science projects for the Meetup group that I organize (Fort Collins Data Science Meetup). My goal is to provide people with an outlet to run code online where they will get the benefits of having a server and a dynamic UI. This will improve the group’s collaboration and Git skills along with allowing people to showcase their work without having to build infrastructure. In case you’re wondering, I built this using Docker Compose, Flask, NGINX, PostgreSQL, and MongoDB.
In order to keep from boring myself to sleep while writing this, I’m going to keep it short and to the point. You might be asking, “what does this application look like?” That’s a great question. It’s a normal website where people contribute Python scripts to do some sort of data processing or analysis. For example, here’s a word cloud generator where the user inserts a Twitter handle with a link to a logo of some sort and then a word cloud is created from all of the most recent tweets! Here is @realdonaldtrump as the Republican elephant and @barackobama as the Democrat donkey.
Starting the 100 Days of Code ( #100DaysOfCode ) challenge
I am always looking to boost my coding skills and as I watch everyone make resolutions for the year, I couldn’t help but think I should try this challenge. In case you don’t know what I’m referring to, one resource is https://www.100daysofcode.com/ – which really gives you a good overview of what the challenge involves.
What will I be building?
I am a project-oriented person, so I will be building a web application that runs sentiment analysis on text data from APIs.
The basic topics I hope to cover:
Store data from external APIs
Utilize PostgreSQL and MongoDB
Back end API development
Luigi ETL pipeline
I will try and send out a blog update every week or two with highlights! I will also be updating GitHub as I go along. Part of the challenge is also posting on Twitter, so each day I’ll be using the hashtag #100DaysOfCode and you can follow me @stoltzmaniac
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()