Executive Data Science

by Brian Caffo, Roger D. Peng, Jeffrey Leek

https://leanpub.com/eds
Executive Data Science

A Guide to Training and Managing the Best Data Scientists

About the Book

In this concise book you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.

Genres: Technology, Business

151 pages, ebook

About the author

Brian Caffo is a professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He graduated from the Department of Statistics at the University of Florida in 2001, and from the Department of Mathematics at UF in 1995. His doctoral advisor was James G. Booth. He works in the fields of computational statistics and neuroinformatics and co-created the SMART working group. He has been the recipient of the Presidential Early Career Award for Scientists and Engineers, Johns Hopkins Bloomberg School of Public Health Golden Apple and AMTRA teaching awards.

Reviews

[Bett Correa-Bollhoefer · Rating 5 out of 5]

Very nice overview of data science. I really learned a lot. Its written in a conversational tone that makes the concepts very accessible.

[Alex · Rating 3 out of 5]

on one hand, i really appreciate all the material that the JHU team puts out, and especially their thinking about process. on the other hand, most of their leanpub books are a haphazard collection of blog posts or course notes that aren't really ready for public consumption. executive data science, like the other JHU leanpub work, is procedural. it tells you the steps to take to analyze data/manage a data team, and this is valuable because nobody else is really do this. but it's also frequently frustrating to me because the procedures are rarely motivated -- it feels like this book is so close to actually arriving at a theory of data science but then it just never does, which sort of kills me. i suspect that an editing process before publishing would probably have brought more of this out and resulted in more coherent organization.

so the tl;dr is that i'm appreciative of this book as a collection of course notes, but find it frustrating as a book or a self-contained didactic unit. i look forward to reading more from the JHU crew, and especially to a proper textbook on data analysis from them

[Mark · Rating 4 out of 5]

The first few chapters are more interesting than the cover would imply- considerations on how to put a team together to solve problems using data science approaches. The latter chapters are a handy reference, quick hits on statistics from a data scientist's viewpoints. My edition would have benefited from a thorough copy edit but otherwise clear and engaging as a primer for the topic.

[Garrett Alain Colas · Rating 2 out of 5]

Dry and boring. The first half was fine, but the last half was a drag.

[Bruno Faria · Rating 3 out of 5]

..

[Splinter · Rating 5 out of 5]

I really like the part about setting up expectations, check if expectations match data, and revise expectations.

[Joe · Rating 1 out of 5]

Terribly presented. The professor should have hired an editor or at least used a spell-checker. The content is unprofessionally handled, and the book is better used as a list of topics to look into.

[Rodrigo Tinoco · Rating 5 out of 5]

good