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Computational Business Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series), by Subrata Das
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Learn How to Properly Use the Latest Analytics Approaches in Your Organization
Computational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies.
The book first covers core descriptive and inferential statistics for analytics. The author then enhances numerical statistical techniques with symbolic artificial intelligence (AI) and machine learning (ML) techniques for richer predictive and prescriptive analytics. With a special emphasis on methods that handle time and textual data, the text:
- Enriches principal component and factor analyses with subspace methods, such as latent semantic analyses
- Combines regression analyses with probabilistic graphical modeling, such as Bayesian networks
- Extends autoregression and survival analysis techniques with the Kalman filter, hidden Markov models, and dynamic Bayesian networks
- Embeds decision trees within influence diagrams
- Augments nearest-neighbor and k-means clustering techniques with support vector machines and neural networks
These approaches are not replacements of traditional statistics-based analytics; rather, in most cases, a generalized technique can be reduced to the underlying traditional base technique under very restrictive conditions. The book shows how these enriched techniques offer efficient solutions in areas, including customer segmentation, churn prediction, credit risk assessment, fraud detection, and advertising campaigns.
- Sales Rank: #2429275 in Books
- Published on: 2013-12-14
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x .70" w x 6.20" l, 1.15 pounds
- Binding: Hardcover
- 516 pages
About the Author
Subrata Das is the founder and president of Machine Analytics and also serves as a consulting scientist to other companies. He has many years of experience in industrial, government, and academic research and development. He earned his Ph.D. in computer science and master's in mathematics.
Most helpful customer reviews
2 of 2 people found the following review helpful.
Great guide and reference for solving complex data-based problems
By Ivan and Zorro
Most of the books currently available on the topic of so called “analytics” or “data science” focus on a specific platform such as a book on R or a book on Python. This text take a different approach, one that is platform agnostic (although some examples are shown using SAS). Each chapter provides a concise discussion of techniques to model data with enough mathematical theory to explain where something like logistic regression is applicable, but not so much theory that you are left feeling like you are swimming in Greek-alphabet soup. Don’t get me wrong, there are plenty of mathematical proofs, however in my opinion only what is necessary has been presented. I found the discussions very readable and refreshingly clear. Where things get a little murky, there is always an excellent example to clarify things. There is also enough foundational probability and statistics so that you don’t need to pick up a second book for background information.
This book also differs from other books I have read/used, in that it also includes sections on AI and machine learning. Actually the text analytics chapters, which rely heavily on AI and machine learning are what drew me to this book in the first place. I don’t see many people sitting down and reading from cover to cover, but more as a reference and guide to specific techniques or approach. An excellent well researched and clearly written reference for anyone working in either a research environment or working with large data both structured and unstructured.
1 of 1 people found the following review helpful.
Excellent guide to the world of analytics
By Thanuka
This book provides a rather comprehensive survey of tools and techniques of computational analytics in a business analytics context. The author presents the subject matter in a progressive pattern, so that it will be beneficial from an experienced to a novice researcher, who is ready to dive into a field that can be very overwhelming at first.
I admire Subrata's insight and feel for the subject, which is very well reflected in the overall organization of the book. Starting with an overview and preliminaries, he slowly builds a story by introducing Statistical, AI and ML analytical techniques, and also the trending unstructured data analytics. Furthermore, the traditional mathematically heavy books and papers could be daunting for someone first entering the field of analytics. This book handles this challenge in such a smooth fashion, via organization of chapters: chapters are logically grouped into 6 parts; each chapter introduces the material progressively with examples, figures, and derivations and concludes with a section on further reading, which is beneficial for someone to dive a little deeper into a particular area of interest. The overall organization is both fun and engaging.
I am writing this as an experienced researcher who actively works on both theoretical and application research in the broader area of signal processing and data fusion. I have read Subrata's book "High-level Data Fusion" (and related publications) as a graduate student, used it as a reference manual (along with David Hall's book on mathematical techniques in data fusion). Author has a natural talent in providing subject matter along with a broader view illustrating how everything is related (i.e., the big picture). I think this book will be invaluable to both practitioners and students of this very vast, and yet captivating field of computational analytics.
1 of 1 people found the following review helpful.
Great intro text
By Jessica
I chose this book as I am doing a Masters' program final project that relies on analyzing sentiment in large bodies and huge databases of unstructured text. I find the discussions to be quite clear (I do not come from an analytics background AT ALL) and the progression of the textbook to be as if I am taking a class in the subject and building upon each step. I will be using this textbook as my prime reference for my data analysis.
The chapters on AI and machine learning are especially intriguing. Although I cannot follow all of the mathematical formulas, I don't need to, because the explanations provide more than adequate information. This is a great book for people like me who need to make use of computational analytics but who don't have any experience with the subject.
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