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General

August 25, 2019 5 minute read

Gems We Love

Ushashi Chakraborty

Director of Engineering

At Mode, we have an interview question we often use during phone screens for engineering candidates which is “What is an example of a library you think is really well-written"? Here are our answers.

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General

August 19, 2019 1 minute read

1,000,000x Improvement to Your Data Application Stack with the Switch Framework

Paul Thurlow

Director of Engineering

Josh Ferguson, Cofounder and Chief Architect at Mode, recently gave a talk at Data Council introducing our framework for building data applications called Switch.

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General

August 5, 2019 4 minute read

What Really Matters: Our Favorite Mode Values

Ushashi Chakraborty

Director of Engineering

At Mode, we make decisions while staying true to our company values. We asked few of our engineers and engineering leaders about the values they like most, and wanted to share their examples.

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Data Teams

May 2, 2019 3 minute read

4 Lessons to Keep Your Data Model Under Control

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Joel Carron

Data Scientist at Mode

A data model defines how an organization's various data sources are organized, related, and accessed. As data sources are added and complexity grows, the data model makes a big impact on the productivity and efficiency of anyone in the company that works with data.

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Data Teams

March 27, 2019 2 minute read

Get More Done With the New API Cookbook and Developer Docs

Leqi Long

Senior Customer Success Engineer

Today, we're announcing the newly-designed Mode developer site. The new site contains extensive documentation of commonly used API endpoints, plus a few new ones. We've also added an API cookbook give you templates for common tasks. These new resources make it easier than ever before to build with Mode.

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Data Teams

March 5, 2019 2 minute read

Empathy and the Art of Analytical Persuasion

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Benn Stancil

Chief Analyst

As analysts and data scientists, we have to make our work compelling and persuasive to change the way our organizations think. It can be easy to assume that our well-constructed graphs and tables are plenty convincing on their own. But this ignores a fundamental fact of analytics: it's still a human making the decision behind those numbers, and that person is rarely as quantitatively inclined as we are. Persuading this audience takes a skill that isn't discussed enough in the data science community: empathy.

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