Final Project (Part 3)

The Idea/Outline

Project Summary (Part I)

For the final project, I will analyze my own personal Spotify data, particularly self-curated playlists, from 2019 thus far to formulate a tailored guide that will represent new musical selections for consumption in the upcoming year 2020. Besides perusing my own data, I am interested in comparing myself versus the “average” U.S. individual, in terms of each group’s musical prospects for 2020. I was spurred to tackle this project due to skepticism on Spotify’s “Discover” playlists, which seems inconsistent and/or lacks satisfaction. In the Digital Age of the 21st Century, we have permitted computers and algorithms to dictate choice, therefore, I am looking to present a chance to customize musical journeys that align with individuals’ specific musical tastes. Basically, implementing step-by-step instructions to carry self-analysis. Many Americans, no the world, listen to music on the daily, so why not have the best music that fits you?

Project Structure (Part I)

The project will begin looking at two datasets: Spotify’s Current Top 200 Songs from the U.S. (Pulled 11/13/19) and my own 2019 Spotify data (primarily from created playlists). Looking at both data structures, it ultimately has to be organized into buckets: Genre/Mood, Artists, and Songs. Since the objective is to discover new songs for consumption, there will be limitations on selected songs, no songs that are found prior 2018 will be considered. For example, knowing that one of my top played genres is “Pop”, I then can eliminate genres that do not match that stipulation. The same principle can be applied on the remaining buckets. Once the elimination process has been completed, there will be a truncated list of the trend data. Due to the massive amounts of music, there will have to be barriers on selection. Therefore, selection will be based on these guidelines:

I believe that this will give enough content to conduct a deeper analysis towards the selection of music for 2020. After these have been determined, I will have to do some personal digging on both what the average U.S. individual and I should listen to. Ultimately, creating two new playlists. The new playlists will involve 20-30 songs, enough content to have a solid musical selection for the start of the new year.

The Data (Part I)

As I mentioned previously, I will use both the United States’ and my Spotify data to structure the top favorite genres, artists, and songs for each participant. Overall, a way to curate two playlists, (one for the U.S. and another for myself) which will allow both parties to tap into new musical content for the start of 2020 that more efficiently gauges interest. Additionally, provide simple instructions for individuals to conduct analysis on their own; a way to give every individual the opportunity to shape their own musical journey. To create these playlists, it will require me to tap into Spotify’s music library and test out new content to determine if it resonates with both parties’ interests.

U.S. Data

To my advantage, Spotify publicly shares their data. The U.S. Top Charts for 2019 (Songs) can be found on Spotify’s public website which shows the top charts both weekly and daily in various regions in the world.

My Data

I use a program called Soundiiz which would take my 2019 created Spotify playlists and form a CSV file that would display: Song Title, Album, Artists, Genre, Year, Date Added and Playlists Name

Method and Medium (Part I)

Story Platform

For the project I will be utilizing the digital platform Shorthand to map out my data story. For my project, it will be told using a narrative approach by documenting the steps starting with acquiring the data all the way to the constructed final deliverables (playlists). It also hosts a plethora of positive user components such as allowing code to be placed within the website.

Recording Data & Graphing

For measuring and presenting the data I will take advantage of Tableau to host all of the excel data and Tableau Public to allow my data visualizations to be presented publicly with my readers.

Brainstorms/Sketches

To collect my thought process throughout this project, I will use the digital platform Balsmiq. My reasoning is based on the final project deliverable which is an electronic presentation. Therefore, by using Balsmiq, which has the capability to replicate a website, it will provide an easy transition for me to transfer data easier onto Shorthand.

Design and user research

Sketches and storyboards (Part II)

Initial Sketches

User research protocol and findings (Part II)

Actual Survey

Survey Feedback

Screen Shot 2019-12-04 at 3 49 27 PM Survey Responses via Google Sheets

Wireframes (Part II)

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Final Data Story

Intended Audience (Part III)

As this project was constructed upon two reasons: My selfish want for new music and to provide individuals access to their own music data, I did not have a specific audience in mind. However, as I progressed in this project, I believe an intended audience developed:

To make sure my project was catering to my audience I did the following:

Final Project Summary (Part III)

From the moment I selected my topic, I knew that I was going to love this project. However, that did not mean it was easy. My project created many challenges. Even with the challenges however, I was able to find meaningful takeaways about each dataset and about the project overall. In addition, I was able to tap into my artistic side not only with the data visualizations, but with music. Having my bachelor’s in music, it was interesting to observe some of the components of music industry that affect music streaming services. As well as curating the playlists allowed me to be critical not only because it would be released into the public but would be a reflective measure of the data. I am very that these datasets were available to the public and having access to Balsamiq and Tableau, which were extremely beneficial for the project.

Challenges

United States Data

André Data

Overall Takeaways

Playlists

United States
André

Final Project

References (Part III)

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