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1911 results in Knowledge Management, Databases and Data Mining

Page 48 of 96

  • 23 - Measuring Long-Term Treatment Effects

  • from Part V - Advanced Topics for Analyzing Experiments
  • Ron Kohavi, Diane Tang, Ya Xu
  • Book:
    Trustworthy Online Controlled Experiments
    Published online:
    13 March 2020
    Print publication:
    02 April 2020, pp 235-245

  • Summary

    Why you care:  Sometimes the effect that you care to measure can take months or even years to accumulate – a long-term effect. In an online world where products and services are developed quickly and iteratively in an agile fashion, trying to measure a long-term effect is challenging. While an active area of research, understanding the key challenges and current methodology is useful if you are tackling a problem of this nature.

  • 15 - Ramping Experiment Exposure: Trading Off Speed, Quality, and Risk

  • 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 171-176

  • Summary

    Why you care: While experimentation is widely adopted to accelerate product innovation, how fast we innovate can be limited by how we experiment. To control the unknown risks associated with new feature launches, we recommend that experiments go through a ramp process, where we gradually increase traffic to new Treatments. If we don’t do this in a principled way, this process can introduce inefficiency and risk, decreasing product stability as experimentation scales. Ramping effectively requires balancing three key considerations: speed, quality, and risk.

  • 10 - Complementary Techniques

  • from Part III - Complementary and Alternative Techniques to Controlled Experiments
  • Ron Kohavi, Diane Tang, Ya Xu
  • Book:
    Trustworthy Online Controlled Experiments
    Published online:
    13 March 2020
    Print publication:
    02 April 2020, pp 127-136

  • Summary

    Why you care: When running experiments, you also need to generate ideas to test, create, and validate metrics, and establish evidence to support broader conclusions. For these needs, there are techniques such as user experience research, focus groups, surveys, human evaluation, and observational studies that are useful to complement and augment a healthy A/B testing culture.

  • 6 - Organizational Metrics

  • from Part II - Selected Topics for Everyone
  • Ron Kohavi, Diane Tang, Ya Xu
  • Book:
    Trustworthy Online Controlled Experiments
    Published online:
    13 March 2020
    Print publication:
    02 April 2020, pp 90-101

  • Summary

    Why you care? Organizations that want to measure their progress and accountability need good metrics. For example, one popular way of running an organization is to use Objectives and Key Results (OKRs), where an Objective is a long-term goal, and the Key Results are shorter-term, measurable results that move towards the goal (Doerr 2018). When using the OKR system, good metrics are key to tracking progress towards those goals. Understanding the different types of organizational metrics, the important criteria that these metrics need to meet, how to create and evaluate these metrics, and the importance of iteration over time can help generate the insights needed to make data-informed decisions, regardless of whether you also run experiments.

Trustworthy Online Controlled Experiments
  • A Practical Guide to A/B Testing
  • Ron Kohavi, Diane Tang, Ya Xu
  • Published online:
    13 March 2020
    Print publication:
    02 April 2020
  • Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions.Learn how toUse the scientific method to evaluate hypotheses using controlled experiments Define key metrics and ideally an Overall Evaluation CriterionTest for trustworthiness of the results and alert experimenters to violated assumptionsBuild a scalable platform that lowers the marginal cost of experiments close to zeroAvoid pitfalls like carryover effects and Twyman's lawUnderstand how statistical issues play out in practice.

Data Mining and Machine Learning
  • Fundamental Concepts and Algorithms
  • 2nd edition
  • Mohammed J. Zaki, Wagner Meira, Jr
  • Published online:
    07 February 2020
    Print publication:
    30 January 2020
  • The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

A First Course in Network Science
  • Filippo Menczer, Santo Fortunato, Clayton A. Davis
  • Published online:
    05 February 2020
    Print publication:
    23 January 2020
  • Networks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.

A Hands-On Introduction to Data Science
  • Chirag Shah
  • Published online:
    01 February 2020
    Print publication:
    02 April 2020
  • This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

Page 48 of 96