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SF Data Weekly - ML for Hip-Hop Popularity, K-Means Clustering and #DuBoisChallenge

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February 4 · Issue #225 · View online
SF Data Weekly
This week’s pick is a project to predict whether a given hip-hop song will be successful on Spotify.
We also have a piece explaining the basics of K-means clustering, and a visualization contest to re-create W.E.B. Du Bois’ visualizations at the 1900 Paris Exposition using modern tools. Stay Healthy!

Our Pick
Using Deep Learning to Predict Hip-Hop Popularity on Spotify | by Nicholas Indorf | Jan, 2022 | Towards Data Science
Data Pipelines
Validate streaming data over Amazon MSK using schemas in cross-account AWS Glue Schema Registry | Amazon Web Services
Develop a Tailored View of Your Salesforce Customer Data, Acquisition, and Billing
Data Storage
How I Discovered Thousands of Open Databases on AWS | by Avi Lumelsky | Jan, 2022 | InfoSec Write-ups
Working with JSON data in BigQuery | by Lak Lakshmanan | Google Cloud - Community | Jan, 2022 | Medium
Executing Multiple SQL Statements in a Stored Procedure | Snowflake
Data Analysis
K-Means Clustering: Explain It To Me Like I’m 10 | by Shreya Rao | Jan, 2022 | Towards Data Science
Neural Network From Scratch In Excel | by Angela Shi | Jan, 2022 | Towards Data Science
Data Visualization
The #DuBois Challenge | Nightingale
Submit Your Work for the Outlier Viz Exhibit! | Nightingale
Data-driven Products
Cost Efficiency @ Scale in Big Data File Format
Data Engineering Jobs
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