Confident Data Science: Discover the Essential Skills of Data Science

by Adam Ross Nelson

Overview

The global data market is estimated to be worth $64 billion dollars, making it a more valuable resource than oil. But data is useless without the analysis, interpretation and innovations of data scientists.

With Confident Data Science, learn the essential skills and build your confidence in this sector through key insights and practical tools for success. In this book, you will discover all of the skills you need to understand this discipline, from primers on the key analytic and visualization tools to tips for pitching to and working with clients.

Adam Ross Nelson draws upon his expertise as a data science consultant and, as someone who made moved into the industry late in his career, to provide an overview of data science, including its key concepts, its history and the knowledge required to become a successful data scientist. Whether you are considering a career in this industry or simply looking to expand your knowledge, Confident Data Science is the essential guide to the world of data science.

About the Confident series...
From coding and data science to cloud and cyber security, the Confident books are perfect for building your technical knowledge and enhancing your professional career.

Product Details

ISBN-13:	9781398612327
Publisher:	Kogan Page, Ltd.
Publication date:	09/26/2023
Series:	Confident Series , #15
Pages:	408
Product dimensions:	5.51(w) x 8.50(h) x 0.87(d)

About the Author

Adam Ross Nelson is a data science consultant and career coach based in Washington D.C. As a consultant, he provides insights on data science, machine learning and data governance. He previously worked as a data scientist at The Common Application.

Having transitioned into the data science field from his career as an attorney, he offers workshops, talks and online courses for those looking to develop their data science skills, pivot their career or improve their career trajectory.

Table of Contents

* Section - SECTION ONE: Getting oriented; ** Chapter - 01: The untold history of data science; ** Chapter - 02: Genres and flavours of analysis; ** Chapter - 03: Data culture and the data science process; * Section - SECTION TWO: Getting going; ** Chapter - 04: Data science examples in production; ** Chapter - 05: A weekend crash course; * Section - SECTION THREE: Data science for clients; ** Chapter - 06: The client, the project and the data; ** Chapter - 07: Topic analysis; ** Chapter - 08: Regression; ** Chapter - 09: Classification; ** Chapter - 10: Sentiment analysis; * Section - SECTION FOUR: Tools of the trade; ** Chapter - 11: Data sources; ** Chapter - 12: Data visualization; ** Chapter - 13: Python + R; ** Chapter - 14: Retrospective / prospective

Editorial Reviews

"This book has it all! From the history of data science to practical examples for how to grow your data career. I wish I had this book when I was starting out. It's a must-read for anyone trying to break into data science."

Asa Howard

"This is not just a book-it's an invitation to understand and immerse yourself in the world of data science. It artfully discusses ethics, data culture, and the processes that help data science provide immense business value."

Diogo Resende

"A work of immense practicality and intellect, which serves as both an extensive guide for novices and a valuable reference for seasoned practitioners."--Stuart Flint, Chief Data Scientist, Yomp and Cloudify Legal

"Data science guru Adam Ross Nelson thoughtfully discusses key fundamentals of data science - including AI and machine learning - so that you can learn and apply the concepts no matter how the field evolves."--Kristina 'KP' Powers

"If you are thinking about making a career move into data science, then pick up this book before you get on that path."--Mark Paige, Professor & Chair, Department of Public Policy, UMass-Darthmouth

"Provides a clear, vibrant guide to all things data. This book engages the reader in a comprehensive exploration of data science. Nelson approaches the topic with humility, clearly sketching both the possibilities and limits of data science."--Julie F. Mead, Professor Emerita, University of Wisconsin-Madison

"This book has it all! From the history of data science to practical examples for how to grow your data career. I wish I had this book when I was starting out. It's a must-read for anyone trying to break into data science."--Asa Howard, data analyst and educator

"This is not just a book-it's an invitation to understand and immerse yourself in the world of data science. It artfully discusses ethics, data culture, and the processes that help data science provide immense business value."--Diogo Resende

"Value-packed on every page. Everything you need to break into data science is covered. Nelson also offers various technical examples through code to ensure every concept is thoroughly understood. I Highly recommend this book to both data science beginners and seasoned practitioners."--Derrick Mwiti, Machine Learning Developer

From the Publisher

"Value-packed on every page. Everything you need to break into data science is covered. Nelson also offers various technical examples through code to ensure every concept is thoroughly understood. I Highly recommend this book to both data science beginners and seasoned practitioners."

Derrick Mwiti

"If you are thinking about making a career move into data science, then pick up this book before you get on that path."

Mark Paige

"Data science guru Adam Ross Nelson thoughtfully discusses key fundamentals of data science - including AI and machine learning - so that you can learn and apply the concepts no matter how the field evolves."

Kristina 'KP' Powers

"A work of immense practicality and intellect, which serves as both an extensive guide for novices and a valuable reference for seasoned practitioners."

Stuart Flint

"Provides a clear, vibrant guide to all things data. This book engages the reader in a comprehensive exploration of data science. Nelson approaches the topic with humility, clearly sketching both the possibilities and limits of data science."

Julie F. Mead