SF Data Weekly - Beautiful Cities and Snowflake for Fun Not Profit

These city maps, which use OpenStreetMap data and are built in Python, are just gorgeous. A great starting point for any visualization of data that is geolocated in a city.

Sometimes you need data that isn’t available to download, but must be scraped from a website. This piece has a short overview of six scraping tools.

Using Kafka as a database is a popular topic. David discusses the pros and cons, and some alternatives, in this interesting post.

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A basic intro to creating a data warehouse in Snowflake, for those of you who haven’t used it before, using stock data (including $GME).

Performance issues with SQL databases are often hard to diagnose and fix. Google’s Cloud SQL Insights tool has some sophisticated tools for understanding database performance, including a really nice graphical query analyzer.

The Python library statsmodel, based on the statistical programming language R, is an excellent tool for dealing with time-series data. This piece gives an overview and examples.

A basic introduction to fundamental statistical models used for data science, and data analysis.

Using the Javascript React framework to build a website usage dashboard. The techniques shown here are applicable to a wide variety of visualization software, not just website statistics.

In case you missed it, this is a fascinating exhibition that examines the 19th-century roots of information graphics. It’s a great site to visit when you need a break, and maybe a little inspiration.

A solid product analytics stack is the foundation of data-driven culture. But, in the early days, how and when should founders invest in their startup’s product analytics stack? This article explains how to set up a product analytics stack and enforce good data hygiene at a startup.