Preparing for a data science interview is a daunting prospect, if only because the potential range of topics is so broad: Machine Learning (ML) algorithms, linear algebra, deep learning architectures, SQL syntax, data engineering techniques, MLOps, and more.

On the spectrum of questions that might get asked, statistical problems land…


CSAT is the king of customer satisfaction surveys. It’s the go-to metric that companies use to measure customer satisfaction for a variety of experiences: online searches, product purchases, customer support, and more.

Because of CSAT’s popularity, numerous how-to guides describe how the survey works and how the results are calculated…


In business, analytics and data science are often used to develop a better understanding of the customer, and results are often expressed in terms of an average customer: average revenue, average return rate, average page views….

Averages like these are simple to calculate and easy to understand, making them a…


One of John Tukey’s landmark papers, “The Future of Data Analysis”, contains a set of analytical techniques that have gone largely unnoticed, as if they’re hiding in plain sight.

Multiple sources identify Tukey’s paper as a seminal moment in the history of data science. Both Forbes (“A Very Short History…


At Microsoft, we have over 2,300 people with “data scientist” in their titles. We all work for the same company in — ostensibly — similar roles, so you might expect that we’d have a shared understanding of the discipline. …


One challenge for data science teams is the ad hoc nature of our work. Presented with one-off problems, we tend to produce one-off solutions. Every analysis is separate and self-contained, and every model is self-sufficient. …


Data science allows us to answer questions from the broadest possible perspective without sacrificing the fidelity of the individual customer experience.

For example, we might want to know how customers get started in the cloud. How do they go from running everything on their own infrastructure to their first successful…


At Microsoft, I run a data science team within Cloud + AI, the division that’s responsible for Azure, Visual Studio, Power BI, and more. My team has a straightforward charter: to learn from our customers.

The concept is simple, but the lessons are powerful.

Because the cloud is a new…

Ron Sielinski

Principal director of data sciences @ Microsoft

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