Book Image

Principles of Strategic Data Science

By : Peter Prevos
Book Image

Principles of Strategic Data Science

By: Peter Prevos

Overview of this book

Mathematics and computer science form an integral part of data science, and understanding them is crucial for efficiently managing data. This book is designed to take you through the entire data science pipeline and help you join the dots between mathematics, programming, and business analysis. You’ll start by learning what data science is and how organizations can use it to revolutionize the way they use their data. The book then covers the criteria for the soundness of data products and demonstrates how to effectively visualize information. As you progress, you’ll discover the strategic aspects of data science by exploring the five-phase framework that enables you to enhance the value you extract from data. Toward the concluding chapters, you’ll understand 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.
Table of Contents (6 chapters)

Toward a Data-Driven Organization

The data science continuum provides a strategic map for organizations that seek to become more data-driven. Each of the steps in the continuum is equally important to the next level because these higher levels of complexity cannot be achieved without embracing the lower levels. The most important aspect of the data science continuum is that it summarizes an evolutionary approach toward becoming a data-driven organization. As an organization evolves toward more complex forms of data science, the earlier stages don't become vestigial appendices but remain an integral part of the data science strategy. All parts of this model are of equal relative value.

Being data-driven is, however, more than a process of increasing complexity. Evidence-based management requires the people within the organization to be data literate and work together toward a common goal. The systematic aspect of data science needs a formalized process to ensure sound outcomes. The increased...