SF Data Weekly - Fight SF Crime, Dunderless Python and DataViz Conferences

A pretty thorough analysis of San Francisco crime data with the challenge for the reader to do more, using the Deepnote notebook shared by the author. There’s also some interesting data prep using the fast.ai library.

Athena federated query allows Lambda data connectors to run queries against multiple AWS data sources, including ElasiCache, DynamoDB, Aurora MySQL and Redshift. This piece shows an example with four different data sources.

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A straightforward explanation of the fast_executemany option in Pyodbc, which allows in-memory acceleration of SQL Server inserts.

How to make your Python code more readable, which means creating a class or a data container in an easy and straightforward way, without boilerplate or effort to write or read object protocols (__init__ = dunder init dunder).

Missing data is a problem often present in real-life datasets, Imputation helps to fill the missing information and achieve better performance. This piece names three common practices to avoid, and recommends three imputations for missing data.

If you’re looking for a conference, this is the document for you – updated regularly by the author. She’s also taking recommendations for conferences if you have any.

“Ultimate” is a big claim, but this piece at least comes close to living up to it by having an exhaustive list of visualizations, with code examples.

Many, many StoryMaps covering a wide variety of topics, which shows off the StoryMap platform as well as providing inspiration for your visualization project.

Rocio Ruelas of Netflix LA shares her perspective of the projects, technical skills, and day-to-day of working in the Content analytics and metrics research space.