Communicating Data Science Results

开始时间: 08/08/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 其他类别

大学或机构: CourseraNew



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Important note: The second assignment in this course covers the topic of Graph Analysis in the Cloud, in which you will use Elastic MapReduce and the Pig language to perform graph analysis over a moderately large dataset, about 600GB. In order to complete this assignment, you will need to make use of Amazon Web Services (AWS). Amazon has generously offered to provide up to $50 in free AWS credit to each learner in this course to allow you to complete the assignment. Further details regarding the process of receiving this credit are available in the welcome message for the course, as well as in the assignment itself. Please note that Amazon, University of Washington, and Coursera cannot reimburse you for any charges if you exhaust your credit. While we believe that this assignment contributes an excellent learning experience in this course, we understand that some learners may be unable or unwilling to use AWS. We are unable to issue Course Certificates for learners who do not complete the assignment that requires use of AWS. As such, you should not pay for a Course Certificate in Communicating Data Results if you are unable or unwilling to use AWS, as you will not be able to successfully complete the course without doing so. Making predictions is not enough! Effective data scientists know how to explain and interpret their results, and communicate findings accurately to stakeholders to inform business decisions. Visualization is the field of research in computer science that studies effective communication of quantitative results by linking perception, cognition, and algorithms to exploit the enormous bandwidth of the human visual cortex. In this course you will learn to recognize, design, and use effective visualizations. Just because you can make a prediction and convince others to act on it doesn’t mean you should. In this course you will explore the ethical considerations around big data and how these considerations are beginning to influence policy and practice. You will learn the foundational limitations of using technology to protect privacy and the codes of conduct emerging to guide the behavior of data scientists. You will also learn the importance of reproducibility in data science and how the commercial cloud can help support reproducible research even for experiments involving massive datasets, complex computational infrastructures, or both. Learning Goals: After completing this course, you will be able to: 1. Design and critique visualizations 2. Explain the state-of-the-art in privacy, ethics, governance around big data and data science 3. Use cloud computing to analyze large datasets in a reproducible way.

交流数据科学结果:重要说明:本课程的第二项任务涉及云中的图形分析主题,您将在其中使用Elastic MapReduce和Pig语言对大约600GB的中等大小的数据集执行图形分析。为了完成此分配,您将需要使用Amazon Web Services(AWS)。亚马逊慷慨地提供了本课程中的每个学习者最多50美元的免费AWS积分,以允许您完成作业。有关获得此学分的过程的更多详细信息,可以在课程的欢迎信息以及作业中找到。请注意,如果您用尽了信用额度,亚马逊,华盛顿大学和Coursera将无法偿还任何费用。 尽管我们相信这项作业可以为本课程带来出色的学习体验,但我们了解到某些学习者可能无法或不愿意使用AWS。我们无法为未完成需要使用AWS的作业的学习者颁发课程证书。因此,如果您无法或不愿使用AWS,则不应该为“交流数据结果”中的课程证书付费,因为如果不这样做,您将无法成功完成课程。 做出预测还不够!有效的数据科学家知道如何解释和解释其结果,以及如何将结果准确地传达给利益相关者,以指导业务决策。可视化是计算机科学的研究领域,它通过链接感知,认知和算法来研究人类视觉皮层的巨大带宽,从而研究定量结果的有效传递。在本课程中,您将学习识别,设计和使用有效的可视化。 仅仅因为您可以做出预测并说服其他人采取行动并不意味着您应该这样做。在本课程中,您将探索围绕大数据的道德考量,以及这些考量如何开始影响政策和实践。您将学习使用技术保护隐私的基本限制,以及为指导数据科学家的行为而出现的行为准则。您还将了解数据科学中可重现性的重要性,以及商业云如何帮助甚至在涉及海量数据集和/或复杂计算基础结构或两者的实验中支持可重现性研究。 学习目标:完成本课程后,您将能够: 1.设计和评论可视化 2.解释有关大数据和数据科学的隐私,道德规范,治理方面的最新技术 3.使用云计算以可重现的方式分析大型数据集。


Statistical inferences from large, heterogeneous, and noisy datasets are useless if you can't communicate them to your colleagues, your customers, your management and other stakeholders. Learn the fundamental concepts behind information visualization, an increasingly critical field of research and increasingly important skillset for data scientists. This module is taught by Cecilia Aragon, faculty in the Human Centered Design and Engineering Department.



Important note: The second assignment in this course covers the topic of Graph Analysis in the Cloud