SF Data Weekly - Agile, Scala, Bird Calls and Hiring Poets

Going back to the original spirit of Agile to explain how you can make it work for analytics projects.

Amazon Redshift ML allows users to create, train and deploy ML models using SQL commands. This piece presents a step-by-step showing how to get started with Redshift ML.

A world-changing product is on the way. Changing the way e-commerce companies power their business with data integration.

This year’s top posts in case you missed some big Google Cloud news.

Antoine built a model based on Google Analytics data that that increased the return on ad spend by 221% over a rule-based model and 73% over off-the-shelf ML.

Using Python with a Kaggle dataset of bird sounds to build a ML model to recognize bird calls.

Mike Bostock, the creator of D3 shares the reasons for his data visualization tool’s longevity, and why it won the 10-year Test-of-Time award from the IEEE. Mike goes deep on D3 and Observable, which he also founded.

Choosing the right visualization pattern, checking all the necessary boxes and creating a unique solution that’s still in line with the project you are working on is hard, and it takes tons of practice, iterations and time to do it accurately and effectively.

Looking beyond STEM degrees for members of a data visualization team.

Learn more about everything big and small that goes into making Scala at Databricks work, from its inception and usage to style, tooling and the problems they faced – a useful case study for anyone supporting the use of Scala in a growing organization.

The stories of two data scientists working for Lyft in Kyiv.