Principles of Strategic Data Science

▶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

▶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

▶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.

▶Approach
This book covers the basic approach of creating value from data. It is developed as a practical guide to strategically and systematically use data to create a better world. It doesn't dwell on promises of machine learning, artificial intelligence or quantum computing. The framework in this book is inspired by the author's current and desired practice as an engineer and social scientist, with a data science responsibility and bestpractice in management.

▶Audience
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.