In addition to my experience as a creative, I have a BA in Data Analytics from Denison University. As a digital native, I've learned to use analytics as a tool to complement my own creative process, a particularly important skill when it comes to creating content for social media. While I analyze my own channel's performance on a day-to-day basis using the tools provided by Google Analytics and YouTube Studio, I also have experience wrangling raw data, coding in Python and R, creating visuals in Tableau, and working with relational databases including SQL.
Below, I've included an assortment of graphs that came from my recent project analyzing the YouTube Trending Tab. As these graphs were primarily targeted at an analytical audience, they are rather bare-bones.
This research was started in 2017 by software developer Mitchell Jolly and an independent YouTube channel called Coffee Break. It's a quantitative deep dive into how the trending tab works and what channels or kinds of content it favors, if it favors any at all. I expanded upon it in late 2019, scraping a new data set which has increased resolution and comparing it to the historical results. I further expanded upon their work, and created predictive models that give a rough idea of where a video will rank on the trending tab based on it's initial performance. I also hypothesized a working theory (based on the results) of how the trending tab works, practically speaking.
Scrolling over any image will give a short description of what the visual is showing. For more specific analysis, if you go to this link you can watch a live recording of the presentation I gave of of results. Copies of both the PowerPoint I used as well as my final research paper are available on my GitHub.