Important topics including information theory, decision tree, Nave Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing.
The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail.
Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding. To make clarity on the topic, diagrams are given extensively throughout the text. The book discusses design issues for phases of mining in substantial depth. The stress is more on problem solving.
In addition, the reader is introduced to considerations for developing the BI roadmap, the platforms for analysis such as data warehouses, and the concepts of business metadata. Other chapters focus on data preparation and data discovery, the business rules approach, and data mining techniques and predictive analytics.
Finally, emerging technologies such as text analytics and sentiment analysis are considered. This book will be valuable to data management and BI professionals, including senior and middle-level managers, Chief Information Officers and Chief Data Officers, senior business executives and business staff members, database or software engineers, and business analysts. How to retrieve information? How to capture data? How to format it? The answer lies in Data Warehousing.
This HOTT Guide will give you access to all the essential information about the newest data storehouse: through articles by expert trendwachters on strategic considerations, how-to reports defining the various ways to extract the data needed for critical business decisions, technical papers clarifying technologies and tools, business cases and key concepts that will provide the reader with a comprehensive overview of a business solution that is already indispensable.
The easy-to-understand recipe names make this a handy test reference book. Python developers and programmers with a basic understanding of Python and Python testing will find this cookbook beneficial.
It will build on that basic knowledge equipping you with the intermediate and advanced skills required to fully utilize the Python testing tools.
Broken up into lots of small code recipes, you can read this book at your own pace, whateve Practical PostgreSQL. Arguably the most capable of all the open source databases, PostgreSQL is an object-relational database management system first developed in by the University of California at Berkeley. In spite of its long history, this robust database suffers from a lack of easy-to-use documentation.
Practical PostgreSQL fills that void with a fast-paced guide to installation, configuration, and usage.
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