A dirty dozen

LONG READ
A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments
“In our experience of running thousands of experiments with many teams across Microsoft, we observed again and again how incorrect interpretations of metric movements may lead to wrong conclusions about the experiment’s outcome, which if deployed could hurt the business by millions of dollars.” - Microsoft

VIZ THIS
The Python Graph Gallery
What bl.ocks.org does for D3.js, this gallery does for Python. Explore visualizations you can build using matplotlib, Seaborn, and pandas, all with reproducible code. (Not a Python user? Try The R Graph Gallery). - The Python Graph Gallery

NO PIPE DREAM
Segment vs Fivetran vs Stitch: Which Data Ingest Should You Use?
Choosing a pipeline tool comes down to which of these criteria is your top priority: harnessing an open source framework, handling high volumes of data with minimal downtime, or getting your data into third-party tools. - Stephen Levin

PEDAL ON
When Are Citi Bikes Faster Than Taxis in New York City?
The next time you need to commute from Williamsburg to Manhattan, grab your helmet instead of hailing a cab. - Todd W. Schneider

MIND THE GAP
When to Avoid the Subway
If taxis and bikes aren’t your preferred mode of transportation, this analysis of a decade of NYC’s MTA alerts has you covered. - Iterating

New from Mode

Custom Chart Colors and Brand New Palettes
Create chart colors that represent your data and your brand or choose from new default palettes (great for those charts with tons of categories!).

How to Use Your Brand’s Color Palette in Data Visualizations
One of Mode’s product designers shares tips for defining thoughtful, useful color palettes.


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