Civis R&D Bookshelf: Project Management Edition

by Liz Sander

Civis Analytics
Published in
2 min readNov 17, 2017


Data scientists spend a lot of time thinking about the technical aspects of their jobs, especially modeling and programming. That makes sense; these things are important, and they’re usually what’s in the job description. But for this edition of Data Science Bookshelf, I wanted to highlight some readings on management, especially project management. Whatever your job title, good project management is important for making sure your code and models are useful and actually make it into production.

Elided Branches

Elided Branches is a blog about managing a technical team. It’s an excellent read, even if you aren’t a manager yourself. The author, Camille Fournier, highlights common sticking points between managers and engineers, and it’s helped me get a sense of how managers think about their work, and in turn, how I can work with my managers to help projects succeed. As a starting point, I recommend this blog post, which describes places where individual contributors get stuck during a project’s lifetime.

Project Management for the Unofficial Project Manager

As a data scientist, I’ve had a lot of ownership over the projects I’ve worked on. This independence and responsibility is exciting, but it can also be overwhelming because I (like many data scientists) have never had any training or formal experience in project management. This book was an excellent crash course on the process of taking a project from start to finish, with an emphasis on practicality over jargon.

Comparing Data Science Project Management Methodologies via a Controlled Experiment

If you want to get deep into the weeds of learning about project management, there’s an entire body of academic literature on the topic. I thought this paper was an interesting demonstration of how you could experimentally compare the effectiveness of data science project management strategies. I was especially interested to see how poorly Agile Scrum performed, given how dominant this method is in the tech world. This paper isn’t the final word on which methodology to use, especially since the experimental subjects were college students rather than professional teams, but it’s worth a skim.

This post is part of our Bookshelf series organized by the Data Science R&D department at Civis Analytics. In this series, Civis data scientists share links to interesting software tools, blog posts, scientific articles, and other things that they have read about recently, along with a little commentary about why these things are worth checking out. Are you reading anything interesting? We’d love to hear from you on Twitter.