Free Seven Methods For Transforming Corporate Data Into Business Intelligence Ebooks Online
Advanced techniques for transforming data into decisions. Today's knowledge-intensive decision support tools can make organizations dramatically more intelligent, by utilizing the information organizations already possess, taking advantage of today's increasingly widespread data mining and warehousing systems, and leveraging mature and powerful artificial intelligence technologies. This is the first book to cover these new approaches in depth, for both the business decision maker and the technologist. Authors Vasant Dhar and Roger Stein are leading practitioners of AI in the financial services industry. They present detailed, up-to-date coverage of all major AI techniques, including genetic algorithms, neural networks, rule-based systems, fuzzy logic, case-based systems and machine learning algorithms. They discuss advanced OLAP and data warehousing systems, and show how to select the most appropriate tool for each business challenge. The book includes detailed checklists of key organizational and technical issues to consider, several practical case studies, and a common methodology and analysis techniques.
Paperback: 269 pages
Publisher: Prentice Hall; 1st edition (December 6, 1996)
Language: English
ISBN-10: 0132820064
ISBN-13: 978-0132820066
Product Dimensions: 9.2 x 7 x 0.6 inches
Shipping Weight: 15.2 ounces
Average Customer Review: 4.5 out of 5 stars See all reviews (12 customer reviews)
Best Sellers Rank: #1,402,152 in Books (See Top 100 in Books) #151 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Neural Networks #746 in Books > Computers & Technology > Business Technology > Management Information Systems #1793 in Books > Business & Money > Management & Leadership > Information Management
These authors have also written a book titled "Intelligent Decision Support Methods". The two books ("Seven Methods" and "Intelligent Decision Support") are virtually identical. They have the same content, the same graphics, the same layout, the same case studies. The basic content (first 202 of 250 pages) is word for word, page for page IDENTICAL. The ONLY diffference between the 2 books is that "7 Methods" includes suggested solutions for the case studies (adding about 1 page for each of the 7 case studies). Do NOT buy "Intelligent Decision Support Methods". It is twice as expensive (hard cover vs soft cover) with no benefit.
In my college days, I really got into exotic/fun stuff like Fuzzy Logic, Expert Systems, and Case-Based Reasoning. I read lots of books that delved into some author's view of the subtle nuances of some technical refinement or another. I even fancied myself as knowledgeable about Genetic Algorithms and published a research paper on the topic. [Yes, I'm a really exciting guy.] Since then I've come to appreciate that none of these "fancy" methods is perfect and they all have their strengths, weaknesses and best applications. The strength/contribution of "Seven Methods..." is that it rises above the Gee-Wiz details (many of which have come and gone) and instead takes a results oriented approach that summarized where each approach would best succeed in a practical business setting. It has consistently placed in the "top ten" of data-mining reviewers' lists and I think it will continue there for years to come because of this high-level/practical perspective. Each chapter illustrates an alternative data-mining approach using simple/tangible examples. The book ends with seven nontrivial and real world case studies to further illustrate how corporations have come to apply these methods to make (or save) real money.
I read this book after I read Data Mining Techniques For Marketing, Sales and Customer Support. There is a significant overlap between the books, but i suppose each one has a slightly different focus.While Data Mining Techniques focused and covered techniques most relevant for marketing purposes, Seven Methods is more general with enterprise business intelligence in mind. While rule-based systems and fuzzy logic are absent from Data Mining Techniques, Seven Methods is missing market basket and link analysis.Although Seven Methods is also written for laypersons, you can still sense that authors are of technical background and have interesting stories to tell about details of each algorithm. On the other hand, authors of Data Mining Techiques are of consulting background or practioner of techniques rather than researcher.I would suggest that, if you are not a core marketer, Seven Methods will better suit your appetite for learning a range of data mining techniques. If you are a marketer, then read Data Mining Techniques.
I have read another book by the same authors entitled "Intelligent Decision Support Methods". It is a clear book with solid conceptual foundations and good examples. To my surprise, this book (published about one year before the other book) has the very same table of contents and some of the reviewers here seem also to be the same. I must confess I am lost. Anyway, this one book costs half the price of the other one!
I found the book extremely well-designed for three reasons: First, the description of the "seven methods" is accompanied by a conceptual framework where the pros and cons of each are discussed and compared. Second, despite being a business-oriented book, it provides the reader with all the quantitative details needed to access more technical readings. Third, the authors use a very readable language throughout the book.
The authors provide interesting and useful insights on Data Mining; I liked chapters 2 and 3 because of the original approach to measure the concept of intelligence density. However chapter 3 is not clear, especially the "Stretch Plot" on figure 3.5; they do not explain how the factors are positioned on the map . In fact the position of the factors along the axes suggests that there is a logical reason for placing them as such but there is no explanation on this placement. Furthermore, in figure 3.6 the authors provide an example of Intelligence Density Profile but the number of squares scattered across the graph implies that there are additional factors represented on the graph that are not mentioned before:why?
Seven Methods for Transforming Corporate Data Into Business Intelligence Analytics: Data Science, Data Analysis and Predictive Analytics for Business (Algorithms, Business Intelligence, Statistical Analysis, Decision Analysis, Business Analytics, Data Mining, Big Data) Corporate Finance: Corporate Finance Guide To Understanding Corporate Finance With Strategies For Business Owners For Utilizing Corporate Finance Including ... Finance Business, Theory And Practice) Data Analytics: What Every Business Must Know About Big Data And Data Science (Data Analytics for Business, Predictive Analysis, Big Data) Data Analytics: Practical Data Analysis and Statistical Guide to Transform and Evolve Any Business. Leveraging the Power of Data Analytics, Data ... (Hacking Freedom and Data Driven) (Volume 2) Social Intelligence: A Practical Guide to Social Intelligence: Communication Skills - Social Skills - Communication Theory - Emotional Intelligence - Data Analysis and Data Mining using Microsoft Business Intelligence Tools: Excel 2010, Access 2010, and Report Builder 3.0 with SQL Server Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program (The Morgan Kaufmann Series on Business Intelligence) Java: Artificial Intelligence; Made Easy, w/ Java Programming; Learn to Create your * Problem Solving * Algorithms! TODAY! w/ Machine Learning & Data Structures (Artificial Intelligence Series) Javascript Artificial Intelligence: Made Easy, w/ Essential Programming; Create your * Problem Solving * Algorithms! TODAY! w/ Machine Learning & Data Structures (Artificial Intelligence Series) Business Plan: Business Tips How to Start Your Own Business, Make Business Plan and Manage Money (business tools, business concepts, financial freedom, ... making money, business planning Book 1) Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking Analysing Quantitative Data for Business and Management Students (Mastering Business Research Methods) Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications) The Microsoft Data Warehouse Toolkit: With SQL Server 2008 R2 and the Microsoft Business Intelligence Toolset Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner Agile Data Warehousing Project Management: Business Intelligence Systems Using Scrum Learning Tableau - How Data Visualization Brings Business Intelligence to Life Business Intelligence Guidebook: From Data Integration to Analytics
