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學生對 华盛顿大学 提供的 Communicating Data Science Results 的評價和反饋

3.6
135 個評分

課程概述

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....

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1 - Communicating Data Science Results 的 25 個評論(共 35 個)

創建者 Vijay P

2019年6月8日

創建者 Chen Y

2016年10月2日

創建者 Mary A

2018年11月3日

創建者 Reese

2017年6月22日

創建者 Piyush K

2018年1月7日

創建者 Red R

2022年1月11日

創建者 Weng L

2016年6月6日

創建者 Bingcheng L

2019年8月7日

創建者 Shivanand R K

2016年6月18日

創建者 Menghe L

2017年6月27日

創建者 Daniel A

2015年12月18日

創建者 Julia L

2016年2月9日

創建者 Gregory R

2016年11月10日

創建者 Seth

2016年1月14日

創建者 Fermin Q

2016年11月12日

創建者 Albert P

2017年6月18日

創建者 Tebogo M

2017年2月2日

創建者 Fernando S

2016年11月18日

創建者 Ivajlo D

2018年11月13日

創建者 Roberto S

2017年6月13日

創建者 Joris D

2017年7月8日

創建者 Solvita B

2016年4月20日

創建者 Alexandre C

2016年4月1日

創建者 Jana E

2017年12月7日

創建者 Anton S

2015年12月19日