Data Science at Microsoft

Onboarding to a data science team

A checklist for your first days on the job

No customer is average

The simple average is rarely simple, but probability distributions and mixture…

How we created and are nurturing a Women in Data Science community at Microsoft

A tale of two frameworks: PyTorch vs. TensorFlow

Comparing auto-diff and dynamic model sub-classing…

Demystifying mentorship for mentors and mentees alike

Segmenting the academic audience

Moving beyond the .edu approximation

Your analytics platform gone rogue, Part 1: Unforeseen costs

“The costs of my data analytics platform…

Celebrating our first anniversary of Data Science at Microsoft

What we’ve learned this year — and…

Moving from interesting to actionable analysis

New Year’s resolutions for a data scientist

How we measured competing features in our Azure app, to the benefit of both

Partnering for metadata management

In my previous article, “Common data engineering challenges and their solutions,” I talked about…

Break your bar chart habit

Deconstructing visualization design using visualization grammar

Causal inference (Part 3 of 3): Model validation and applications

Machine Learning model governance at scale

Author Peter Saddow is joined for this article by co-author Daniel Yehdego

Causal inference (Part 2 of 3): Selecting algorithms

Building resilient data products quickly (Part 2 of 2)

At Microsoft, we define resiliency as the…

Causal inference (Part 1 of 3): Understanding the fundamentals

Building resilient data products quickly (Part 1 of 2)

Anomaly detection through the lens of online…

It’s all yours: The why and how of visualization branding

Breaking through: How we’re making recommendations actionable in a world filled with them

Calculating customer lifetime value: A Python solution

How to refine retention insights to create clarity for your team

How we built a machine learning and data science community at Microsoft

The data scientist toolbelt

What you need to succeed in the field