Win £50 Amazon Voucher!
A winning entry will be chosen based on best visualization and successfully following the entry rules – winning a £50 Amazon voucher code!
The top 5 entries will also receive 2 data books from our sponsors Packt!
How to submit your entry:
- Follow Onyx Data on LinkedIn
- Share a LinkedIn post that contains both a direct @ mention to @Onyx Data, and the hashtag #dataDNA (it’s OK if you already follow Onyx Data)
- In your post, share an image of your visualization or dashboard (remember, it must be a single image)
- Tag, mention, and invite 5 connections to view your post or play along (optional)
- This month we are analyzing the tracks of Spotify!
- Can we identify what makes a hit track?
- – id (Id of track generated by Spotify)
- – acousticness (Ranges from 0 to 1)
- – danceability (Ranges from 0 to 1)
- – energy (Ranges from 0 to 1)
- – duration_ms (Integer typically ranging from 200k to 300k)
- – instrumentalness (Ranges from 0 to 1)
- – valence (Ranges from 0 to 1)
- – popularity (Ranges from 0 to 100)
- – tempo (Float typically ranging from 50 to 150)
- – liveness (Ranges from 0 to 1)
- – loudness (Float typically ranging from -60 to 0)
- – speechiness (Ranges from 0 to 1)
- – mode (0 = Minor, 1 = Major)
- – explicit (0 = No explicit content, 1 = Explicit content)
- – key (All keys on octave encoded as values ranging from 0 to 11, starting on C as 0, C# as 1 and so on…)
- – timesignature (The predicted timesignature, most typically 4)
- – artists (List of artists mentioned)
- – artists (Ids of mentioned artists)
- – release_date (Date of release mostly in yyyy-mm-dd format, however precision of date may vary)
- – name (Name of the song)
Data DNA – Dataset Challenge July – 2021 Winner
This month’s winning visualization is by – Salim Amarti