Data Science: An Update on my Goodreads Reviews as of 2020

A few years ago, in 2016, I wrote a brief post on statistics when looking at my Goodreads reviews. A friend's comment recently reminded me of this work and I decided to update it with information all the way up to the present day. Unfortunately, a big limitation has been that, as of earlier this month, Goodreads discontinued their API. I had a lot of code written to access it and generate the plots and had to spend some time rewriting it to use a CSV export of my library. It's not the same, but I was able to reconstruct most of the information. So without further ado, let's look at what we can discover.

Data Science: Republican & Democratic Conventions

In the past few weeks, the two major political parties in the United States of America held their national conventions. While I couldn't listen to all the speeches, I followed the news and paid attention to the overall scene. After they were done, I decided to grab the speeches of the major speakers and see if I could find any obvious trends in their word choices, similar to what I did with my Twitter project. In this blog post, I'll discuss what I can see in the data. You can find my data and all my scripts at this GitHub repo.

Data Science: The Divided States of America

In the prior two posts, I have described how I gathered twitter data from @HillaryClinton and @realDonaldTrump, how I ran a sentiment analysis on the individual tweets, and how I performed a principal component analysis on the most commonly used words. Today, I’ll tie everything together and describe how I created a model to predict whether a given tweet belongs to either of the two candidates.

Data Science: Principal Component Analysis of Twitter Data

As described on my last blog post on this topic, I've been tracking tweets from the US presidential candidates, Hillary Clinton and Donald Trump. I've looked at the top words they used and the sentiments expressed in their tweets given their word choice. However, some words are used with others almost all the time, a notable example being a slogan like Make America Great Again. As such, it may be beneficial to look at groups of words rather than individual words. For that, I took an approach applying a Principal Component Analysis. Below I describe what this is, how I used it, and what it reveals. Do note, however, that I'm applying things I learned in astronomy to this problem rather than taking courses specific to text mining. It may be that there are better tools out there than what I've used.

Data Science: Presidential Candidates on Twitter

Over the past few months, I've been working on a little hobby data science project to explore twitter data with regards to the upcoming presidential election in the United States. The project has evolved quite a bit and detailing it in full is beyond the scope of a single blog post. As such, I've decided to split it into (at least) 3 posts. This post is the first of the series and will go over the basics of gathering data from Twitter and doing some simple text mining. The second and third posts will discuss more details of the project and show some neat visualizations I've created. I'll release all my code after the third post for any curious coders. For now, let's get started seeing what Hillary Clinton and Donald Trump's Twitter accounts are talking about.

Data Science: Essentials of Data Science Bootcamp

About a month or so ago, I undertook a bootcamp called "Essentials of Data Science" and run by a handful of people with support from NYCAscent. I've been meaning to write up a blog post about this, but only just now had the time. While I already had some knowledge of programming in R, I still learned a lot and feel far more confident about entering the data science job market now. Below, you'll find my general thoughts on the experience and a brief overview of what my project was about.