Data Science for Startups

by Ben G Weber

Many startups are now hiring data scientists as early employees, but it’s often unclear how someone in this role can have impact at a small organization. The goal of this book is to provide an overview of how to make a startup more data driven, and covers topics ranging from tracking data to data pipelines to predictive modeling at scale. It is intended for analysts and data scientists that want to broaden their responsibilities and grow a data science discipline.

149 pages, Paperback

Published June 2, 2018

Reviews

[Gene Ishchuk]

pure introduction to everything but R and Java? Oh come on...
as Ben said, it is indeed a compiled version of his Medium posts, nothing more.

[Quy Nguyen]

This book takes into account in process of a specify Data Team from collect data, saving data to productizing. This book briefly in all steps of data processing with cases in Sony, Twitch. Data pipeline, Experimentation, and Productizing models are attractive chapters (My personal idea). They combine coding, statistics to improve the output. Overall, I realize the Data Science team is very close to a Dev team. DS team not only build the BI report, ML but also release the productizing model. It leaves me with some ideas and keywords to study, which I have not yet learned.

[Pritesh Shrivastava]

Beginners please ignore this book.

It is just a collection of Medium articles, and includes a lot of complex Java code without any introduction and context.

I thought I'll learn about industry best practises, but the only learning I got was to use Google Cloud managed services wherever possible.

You can probably skim through this book in an hour as there really is not much content to read.