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1818 results in Knowledge Management, Databases and Data Mining

Page 39 of 91

  • 1 - Necessary Foundations for Decision Support

  • from Section 1 - Getting Oriented
  • Elliot Bendoly, Ohio State University
  • Book:
    Excel Basics to Blackbelt
    Published online:
    08 May 2020
    Print publication:
    28 May 2020, pp 3-11
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  • Summary

    Objective: Appreciate the wide range of intentions, focal methods, and general characteristics of effective decision support tools.

Excel Basics to Blackbelt
  • An Accelerated Guide to Decision Support Designs
  • 3rd edition
  • Elliot Bendoly
  • Published online:
    08 May 2020
    Print publication:
    28 May 2020
  • This third edition capitalizes on the success of the previous editions and leverages the important advancements in visualization, data analysis, and sharing capabilities that have emerged in recent years. It serves as an accelerated guide to decision support designs for consultants, service professionals and students. This 'fast track' enables a ramping up of skills in Excel for those who may have never used it to reach a level of mastery that will allow them to integrate Excel with widely available associated applications, make use of intelligent data visualization and analysis techniques, automate activity through basic VBA designs, and develop easy-to-use interfaces for customizing use. The content of this edition has been completely restructured and revised, with updates that correspond with the latest versions of software and references to contemporary add-in development across platforms. It also features best practices in design and analytical consideration, including methodical discussions of problem structuring and evaluation, as well as numerous case examples from practice.

Mining of Massive Datasets
  • 3rd edition
  • Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman
  • Published online:
    16 April 2020
    Print publication:
    09 January 2020
  • Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.

  • 3 - Techniques

  • from Part I - Conceptual Introductions
  • Chirag Shah, University of Washington
  • Book:
    A Hands-On Introduction to Data Science
    Published online:
    01 February 2020
    Print publication:
    02 April 2020, pp 66-96

  • Summary

    There are many tools and techniques that a data scientist is expected to know or acquire as problems arise. Often, it is hard to separate tools and techniques. One whole section of this book (four chapters) is dedicated to teaching how to use various tools, and, as we learn about them, we also pick up and practice some essential techniques. This happens for two reasons. The first one is already mentioned here – it is hard to separate tools from techniques. Regarding the second reason – since our main purpose is not necessarily to master any programming tools, we will learn about programming languages and platforms in the context of solving data problems.

  • 14 - Choosing a Randomization Unit

  • from Part IV - Advanced Topics for Building an Experimentation Platform
  • Ron Kohavi, Diane Tang, Ya Xu
  • Book:
    Trustworthy Online Controlled Experiments
    Published online:
    13 March 2020
    Print publication:
    02 April 2020, pp 166-170

  • Summary

    Why you care: The choice of randomization unit is critical in experiment design, as it affects both the user experience as well as what metrics can be used in measuring the impact of an experiment. When building an experimentation system, you need to think through what options you want to make available. Understanding the options and the considerations to use when choosing amongst them will lead to improved experiment design and analysis.

Page 39 of 91