Description
Book SynopsisThis book is a timely and critical introduction for those interested in what data science is (and isn't), and how it should be applied. The language is conversational and the content is accessible for readers without a quantitative or computational background; but, at the same time, it is also a practical overview of the field for the more technical readers. The overarching goal is to demystify the field and teach the reader how to develop an analytical mindset instead of following recipes. The book takes the scientist's approach of focusing on asking the right question at every step as this is the single most important factor contributing to the success of a data science project. Upon finishing this book, the reader should be asking more questions than I have answered. This book is, therefore, a practising scientist's approach to explaining data science through questions and examples.
Trade Review"Data science is no longer the exclusive domain of computer scientists and engineers. The contributions of other stakeholders are required for taking a holistic approach to the problems that can be addressed by analysing a given dataset. Not only is this likely to lead to better solutions, but also a smoother journey to their implementation, validation and widespread adoption. However, in the same way that a computer scientist should at least gain an operational understanding of the tackled problem, the domain expert should also understand the foundations and correct use of the tools unveiling its solutions. In this context, How to Think about Data Science is an unusual book in that it provides an accessible introduction to this broad and booming discipline without sacrificing the understanding of key questions in data science. I can only recommend this book to those aspiring to acquire this knowledge and mindset."
--Pedro J. Ballester, PhD, Senior Lecturer, Imperial College London; Wolfson Fellow, The Royal Society
"What is the difference between a regular cook from a renowned chef? A regular cook may follow recipes and create edible dishes, but knowing which ingredients to use and how to combine them, how to cook each one and for how long, and how to finally present them is what makes all the difference. The tools and processes are important for sure, but what really provides value is being able to choose and integrate the right tools, ingredients and processes to create a terrific dish. In data science it is the same: anyone can execute a clustering or build a neural network with default parameters but what matters is to know, given a dataset, what questions can be answered, what algorithms we should use to answer each question and what ethical issues and privacy concerns should be considered; answering these questions would allow a data scientist not just to follow recipes, but to apply the right algorithms to answer the right questions while minimizing potentially discriminating outputs. This book focuses on these relevant questions. If you want to cook a terrific dish, this book will help you."
--Jordi Conesa i Caralt, PhD, Associate Professor of Computer Science, Universitat Oberta de Catalunya
"Today, big data influences nearly everything we do, and harnessing its enormous power remains a key driver of business analytics, research innovation, cultural revolution, and global politics. This book offers a great gateway to this broad and evolving subject by asking the right questions, introducing concepts clearly and succinctly, and making rational connections between computation and their wide ranging applications. The book also discusses important issues related to data bias, discrimination, data privacy, and security. The final chapter debates the limits of artificial intelligence and the computational, ethical, and philosophical conundrums it presents. Thought-provoking and refreshing – it is a must-read book!"
-- Subhajyoti De, PhD, Associate Professor at the Center for Systems and Computational Biology, Rutgers, the State University of New Jersey
Table of ContentsA bird’s-eye view and the art of asking questions. Descriptive Analytics. Predictive Analytics. How are predictive models trained and evaluated? Are our algorithms racist, sexist and discriminating? Personal data, privacy and cybersecurity. What are the limits of Artificial Intelligence?
