Data Science: The Hard Parts: Techniques for Excelling at Data Science

by Daniel Vaughan

This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline—machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one.

Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.

With this book, you

Understand how data science creates valueDeliver compelling narratives to sell your data science projectBuild a business case using unit economics principlesCreate new features for a ML model using storytellingLearn how to decompose KPIsPerform growth decompositions to find root causes for changes in a metricDaniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).

393 pages, Kindle Edition

Published November 1, 2023

Reviews

[Matthew Mechtly · Rating 5 out of 5]

A hodgepodge of practical advice across various tasks that a data scientist will encounter. Much of the information was elementary, but much was also deep and original (given how many data science books I have read, that is really saying something).

Downsides: many of the visualizations assumed color, but the publisher--even in the PDF--removed colors from those visualizations, thereby degrading their information.