Visualizing Spotify data with a tableau course
Data science has emerged as a very demanding trend in the industry. Domain skills bring valuable insights from structured and unstructured data using standard tools and techniques. One of the aspects of data science is data visualization, which is called the graphical representation of the main information obtained from the processed data.
Table of Contents
Tableau is one of the standard tools in business intelligence, analytics, and data visualization. Tableau (desktop and, more recently, public) versions have changed how we interact with visualization and developed data stories for visual communication aids for our large stakeholders, including non-technical audiences globally. With Tableau, you can visualize Spotify streamed data of your favorite artists, tracks, brands, and more.
You can create dynamic dashboards for visualizing data by using the streaming data and API requested from Spotify and integrating it into the Tableau tool.
Tableau to Visualize Spotify Music
Once you connect to the Spotify page, you can download music data from the top charts. Top 200 options get a top track or trending track. Use the pull-down menu to filter by country or select Global Options. Similarly, choose from daily or weekly data for your analysis. Use the "Download to CSV" option at the top right of the screen. Daily data is downloaded in the default Spotify_Daily_Streaming file.
Now in the Tableau application, load the data source. To open the latest Spotify CSV file on the canvas, select the "Text File" option in the Connect pane. Use the Tableau options to analyze the uploaded data.
The following sections are summarized to analyze the given clause.
Popular Tracks and Artists
Follow the steps to get the most famous songs.
- Create a new sheet.
- Add the SUM to rows.
- Track URL to columns.
- Sort the songs in the order of greatest to lowest.
- Enter a name for the track. It is provided next to the Track URL on columns.
- Using the pill in the columns hide the Track URL title.
- Mark the Show Header option unchecked.
Popularity by country
This option is used to analyze how listening habits vary from country to country.
- Double-click the Country column in the Data panel. Being a geographical area, it plots data on a map. The tableau indicates the geographical column that placed the data on the map.
- Drop the Streams column into the color for creating a map. For multiple streams, the color turns black.
- For less than two streams are indicated in a lighter shade.
- The right-most side of the screen displays unknown values.
- Double-click on the error notification to remove the data.
- Use the filter to get the Global column.
Streaming songs or artists
- Add the “Week” to rows.
- Also, add the SUM to columns.
- You need to filter the Global data.
You can also analyze currents over time by adding consecutive “week (date)” in columns and SUM (streams) in rows. Of course, in-depth analysis like "seasonality" can be done in the table and allows customized analysis.
Bring to Dashboard
Just drag the sheets from the above analysis with the default map. Select the "Use as filter" option on the map and bar chart. Finish the map's visual presentation settings with color, grid, and format options.
Data Analytics Certification
It is convenient for new graduates with nearly 5 years of industry experience to upgrade with Visualizing Spotify Data for new graduates and with a tableau course designed for career professionals in data analytics. Imarticus Learning also offers you a PG degree in Data Analytics and Machine Learning.
You become stronger with the foundation of data analytics and machine learning concepts and with the most in-demand data science tools and processes to play a better role in the data science domain. With the Data Analytics course, you can master skills in data visualization as well as Python, SQL, data analytics, and machine learning for data visualization. To get more information about courses in analytics, contact us through chat support, or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon.