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Summary: "Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data, and leverage this insight to improve a wide range of business decisions. In Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students. Delen's holistic approach covers all this, and more: Data mining processes, methods, and techniques The role and management of data Predictive analytics tools and metrics Techniques for text and web mining, and for sentiment analysis Integration with cutting-edge Big Data approaches Throughout, Delen promotes understanding by presenting numerous conceptual illustrations, motivational success stories, failed projects that teach important lessons, and simple, hands-on tutorials that set this guide apart from competitors"-- Provided by publisher.

Summary: Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out.

Summary: Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the "what" and the "why" of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you'll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. This book outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value. This book will enable you to: Learn exactly what data science is and why it's important ; Adopt a data-driven mindset as the foundation to success ; Understand the processes and common roadblocks behind data science ; Keep your data science program focused on generating business value ; Nurture a top-quality data science team.

Summary: Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.

Summary: This book features a selection of papers presented at the Second IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2014, held in Wroclaw, Poland, in September 2014, in the framework of the Federated Conferences on Computer Science and Information Systems, FedCSIS 2014. The 9 revised and extended papers and one invited paper were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management and are organized in the following topical sections: tools and methods for knowledge acquisition; models and functioning of knowledge management; techniques of artificial intelligence supporting knowlege management; and components of knowledge flow.

Summary: Knowledge-based management systems include data warehouses as their core components. This book features research and practical achievements in data warehousing and on-line analytical processing (OLAP) technologies.

Summary: "This book covers research topics of data mining on bioinformatics presenting the basics and problems of bioinformatics and applications of data mining technologies pertaining to the field"--Provided by publisher.