Principles of Strategic Data Science

Take the strategic and systematic approach to analyze data to solve business problems

Key Features

• Gain detailed information about the theory of data science
• Augment your coding knowledge with practical data science techniques for efficient data analysis
• Learn practical ways to strategically and systematically use data

Book Description

Principles of Strategic Data Science is created to help you join the dots between mathematics, programming, and business analysis.

With a unique approach that bridges the gap between mathematics and computer science, this book takes you through the entire data science pipeline. The book begins by explaining what data science is and how organizations can use it to revolutionize the way they use their data. It then discusses the criteria for the soundness of data products and how to best visualize information. As you progress, you'll discover the strategic aspects of data science by learning the five-phase framework that enables you to enhance the value you extract from data. The final chapter of the book discusses the role of a data science manager in helping an organization take the data-driven approach.

By the end of this book, you'll have a good understanding of data science and how it can enable you to extract value from your data.

What you will learn

• Get familiar with the five most important steps of data science
• Use the Conway diagram to visualize the technical skills of the data science team
• Understand the limitations of data science from a mathematical and ethical perspective
• Get a quick overview of machine learning
• Gain insight into the purpose of using data science in your work
• Understand the role of data science managers and their expectations

Who this book is for

This book is ideal for data scientists and data analysts who are looking for a practical guide to strategically and systematically use data. This book is also useful for those who want to understand in detail what is data science and how can an organization take the data-driven approach. Prior programming knowledge of Python and R is assumed.

Table of Contents

1. What is Data Science?
2. Good Data Science
3. Strategic Data Science
4. The Data-Driven Organization

Dr Peter Prevos is a civil engineer and social scientist who also dabbles in theatrical magic. Peter has almost three decades of experience as a water engineer and manager, working in Europe, Africa, Asia, and Australia. He has worked on marine engineering, drinking water, and sewage treatment projects. Throughout his career, analysing data has been a central theme.

He also has a PhD in marketing and is the author of Customer Experience Management for Water Utilities. In his work, he aims to combine the social sciences with engineering to create value for customers. Peter occasionally lectures marketing for MBA students.

He is currently responsible for developing and implementing the data science strategy for a water utility in regional Australia. The objective of this strategy is to create value from data through useful, sound, and aesthetic data science. His mission is to breed unicorn data scientists by motivating other water professionals to ditch their spreadsheets and learn how to write code.