Written by the well-known data science experts Foster Provost and Tom Fawcett, Anyone who intends to understand data science and data mining, and wants to develop a skill at data-analytic thinking, This proves to be just the book for them. This is one deep but not too technical book that is not based on algorithms but Instead, it presents itself to us with a set of fundamental principles that help us in the extraction of useful knowledge from data. These very fundamental principles not only act as the foundation for many algorithms and techniques for data mining but also most importantly, these principles also underpin the processes and strategies that are necessary to solve problems in business through data-mining techniques with real-life examples. This is one solution presented to you in order to help you to understand the data-mining techniques used today and in the run, also prove to be a guide that will help you walk through smoothly, allowing you to take this book to your full advantage.

About The Book:

The book Data Science for Business helps in building up the reader's understanding of data science by discussing the fundamental principles in the context of business with real-life examples and then shows how the principles can provide an understanding of the most common methods and techniques that are used in data science today. After reading the book, the reader will not only be able to discuss data science intelligently with data scientists and other stakeholders but will also have a better understanding of proposals for data science projects and data science investments and thus, in return will be able to participate integrally in projects related to Data-science.

The book succeeds in covering several key algorithms like decision trees, support vector machines, logistic regression, k-Nearest Neighbors and Term frequency-inverse document frequency (TF-IDF) but not in great depths of implementation as its main goal is to focus on understanding Data-Science and not give a high-level overview.

The explanations of the algorithms in visual terms and its focus on the expected value framework for evaluating data mining models act as the strength of this book. This book consists of a Diversity of explanation's that is indeed a good thing as you can read different explanations and it feels like one is speaking directly to you. This book spends more of its time on discussing practical applications as compared to other books on data mining and this one feature makes it even better. You don't only get the theoretical knowledge, but also understand on how to implement it!

This book, Data Science for Business turns out to be a must read if you are among the audience that aims in doing data mining in a commercial environment.

About The Authors:

Foster Provost is a Professor and NEC Faculty at the NYU Stern School of Business. Previously, he also worked as a data scientist for Verizon for five years, winning a President's Award for his work there. His research and teaching focus on data science, machine learning, business analytics and crowd-sourcing for data analytics. His prior work extended data science methods to business applications including fraud detection, counterterrorism and more. His work has won many awards like IBM Faculty Awards, the President's Award etc.

Tom Fawcett is a Data Scientist at Silicon Valley Data Science and holds a Ph.D. in machine learning from UMass-Amherst and has also worked in industrial research. In his career, he has published numerous conference and journal papers in machine learning. He has been glorified with the SCOPUS Award from Pattern Recognition Letters, and a President's Award from Verizon.

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