Build Your Career In Data Science [coauthored with Emily Robinson]

Available early 2020 from Manning Publishing (early chapters already available!)

You are going to need more than technical knowledge to succeed as a data scientist. Build Your Career in Data Science teaches you what school leaves out, from how to land your first job, to the lifecycle of a data science project, and even how to become a manager.

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Blog posts & articles

Red flags in data science interviews [coauthored with Emily Robinson]

Signs to watch out for when interviewing for positions.

When interviewing for any position, you should be evaluating the company just as much as they are evaluating you. Here are twelve signs the company may not be right for you as a data scientist.

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You're relying on data too much

Making decisions worse, not better

When you're running a business, you constantly make important decisions. While data has been marketed as the key to making the right ones, it can be just as harmful as helpful.

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Prioritizing data science work

Choosing between the many things you could work on

As a data scientist trying to support an organization, you often must decide what task you should be working on. You may be managing all sorts of different tasks, and you need a way to handle that.

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The academic trap and data science

How to get a data science position after academia with no previous industry experience

If you are a full time academic and are considering switching to industry, you may find it difficult to leave. A lack of industry experience is the academic trap, but it's possible to get out of it.

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Getting data science to work

Predictive models are more than just the predictions

When building a predictive model, most junior (and many senior) data scientists fall into a trap of thinking that the success of their work depends precisely on how accurate the predictions are.

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