Analytics in a Big Data World: The Essential Guide to Data Science and its Applications (Wiley and SAS Business Series)

"Analytics in a Big Data World" provides a perfect introduction into the foundations and potential of analytics. It starts by introducing the basic nomenclature, the analytics process model, and its relation to other relevant disciplines, such as e.g. statistics, machine learning, and artificial intelligence. Chapter 2 highlights the most important steps of the process model, such as sampling, treatment of missing values, and variable selection, among others. The subsequent two chapters focus on state-of-the-art techniques on predictive and descriptive analytics, respectively. A broad range of real-world applications underlines the relevance of "Analytics in a Big Data World" putting special emphasis on credit risk, survival analysis, and social network analysis. Particularly interesting is Chapter 7 where the author provides tools and hints on how to put analytics to work. Topics covered here range from backtesting and benchmarking approaches to data quality issues, software tools, and model documentation practices. Up-to-date references provide sources to further study each one of the topics treated. This book fills the gap between rigorous scientific literature on the one hand and high-level descriptions of particular applications on the other by providing the right level of detail regarding quantitative methods in order to understand analytics and its potential for a broad range of business applications. It is full of sound mathematical tools that have proved their usefulness in many real-world applications, but always explained from a business-oriented perspective. The way how even complex phenomena are presented in simple and easily understandable terms is particularly interesting and adds a lot of value. Bart Baesens, a world-renowned expert in analytics, shares with the readers of this book his knowledge and experience from many projects and training courses performed worldwide and in several different industries as well as from his role as leading researcher in analytics and data science. I highly recommend this book for newcomers to the area as a source for a solid introduction, for students and teachers of undergraduate courses or executive education, and last but not least for professionals working in industry or consulting related to business analytics.

Dr. Bart Baesens does an excellent job of setting up the business case for using analytics in the Big Data world in this book. He goes over all of the needed general functions for analytics and has great use cases as well. Only things I might see which could have been added are how to visualize and/or envision the results of the Big Data analytics and also the case of when data gets so large, that often statistical regressions don't give accurate tests of significance just due to the sheer volume of data records going into a regression model and how these should or could be adjusted accordingly. Otherwise, this is a great resource for any organization who has data larger than they can currently deal with and gives great insights.

Analytics and Big Data are popular concepts which rely on the simultaneous application of statistics, computer science, and operational research for business decisions. For this reason the whole picture behind these concepts is needed but most available books do not provide it: while Big Data literature is filled with anecdotes on how important analytics modelling is (but without providing details of the techniques), most statistics/computer science books focus instead on various models and on the math behind them, but give no guidelines for their successful usage in the context of business. However, Bart Baesens’ “Analytics in a Big Data World” provides a perfect blend between the essentials in data mining techniques and expert knowledge regarding the successful implementation of those methods in the business world. The book provides detailed expertise in three applications that seldom appear together: churn prediction (usually seen as a CRM/quantitative marketing task), credit risk (mostly covered in financial books), and social network analysis, another hot topic which has been studied mainly in the field of computer science. These applications are studied under a unifying framework, together with the most suitable techniques for each task. Traditional statistic/econometric methods such as logistic regression and survival analysis, and machine learning approaches, such as Support Vector Machines and Self Organized Maps, are studied jointly. The descriptions of the methods are easy to follow, although a basic knowledge of statistics is necessary. The sections I enjoyed the most were the last two chapters, in which the author shares his vast expertise in the applications mentioned. In summary, “Analytics in a Big Data World” is an intermediate book which will allow you to understand the most important methods used in analytics while providing tips for the successful implementation of your BI projects. It is also a great guide for teaching/learning business analytics courses.

New uses of all that data companies have gathered.

Many people have heard the word or even have collected some "Big Data" in the last few years but possibly not many of them know how to use these "Big Data". This book provides a perfect answer by presenting some analytical applications that could be used by practitioners in various industries. I am teaching analytics to university students from various levels of quantitative background. I have been using this book as a key learning text for several of my courses. For advance analytic students, this book not only provides a collection of practical examples to show how analytics could be used by organisations in a wide range of sectors but also includes some popular analytic topics (e.g. social network analytics, survival analysis, etc.). Many analytic students (or even practitioners!) have overlooked the importance of the challenges in putting analytics in practice. A chapter of this book contributes to this important issue. This is an excellent book with good balance of theoretical and practical topics. For those who are less keen on numerical subjects, this book also provides a great reference because the author uses some simple approaches to present the mathematics and statistics behind these analytical applications. If you are struggling with equations or mathematical symbols but you are interested to understand more about analytics, this book is a must buy for you! It is an easy-to-read and full of practical case studies book, which provides a great foundation for anyone, who would like to make good use of "Big Data"!

Awesome

Good one

I can recommend this book to anyone who wants to know more about using big data for business applications. What I loved about it is that it provides a general overview allowing you to frame all you know already or that you heard before on the topic, and at the same time it provides insight in an intuitive manner in the technical workings of a number of fundamental techniques in the field – about which you also heard a lot already, but you never really bothered to look into how these techniques really function basically. The book is not too academic in nature, something you often have, and is clearly written by someone who knows how things go in a company and what’s important to the business. The book is written fluently, provides insightful examples – in summary: very pleasant to read!