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学生对 华盛顿大学 提供的 Communicating Data Science Results 的评价和反馈

3.6
128 个评分
36 个审阅

课程概述

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 个评论(共 33 个)

创建者 Reece K

Jun 23, 2017

yikes update the github resources please

创建者 Piyush K

Jan 07, 2018

Really disappointed by his way of teaching. He assumes we know every thing before hand, database, server etc. He just has basic concepts in his lecture classes while intermediate level implementations of it in different languages. He just instructs check out this tutorial online and do this assignment.

If you are already familiar with all the languages and software platforms that he is using than you can go ahead with the course or you will end up like me where you will have to take up different courses to just complete assignments of this one.

创建者 SIEW W L

Jun 06, 2016

Very good exercise to pick up PIG and AWS environment. It is best to pick up jupyter notebook prior to taking this class for the first exercise. I like how David has been able to present so much content in a 3 weeks lesson.

创建者 Menghe L

Jun 27, 2017

very good course for learner

创建者 Daniel A

Dec 18, 2015

Great class !

创建者 Shivanand R K

Jun 18, 2016

Excellent thoughts and concepts presented.

创建者 Bingcheng L

Aug 07, 2019

Too little people participated and long peer review time.

But the course content is good.

创建者 Vijay P

Jun 08, 2019

I wish there is a coherent explanation of procedure to do graph analysis on AWS. The required details are provided in bits and pieces in the discussion forum and in github. I had to spend a lot of my time figuring this out. If you are new to this be ready to spend a lot of time or better take some other course where all explanations will be provided. But if you have some experience then this course is great.

创建者 Albert P

Jun 18, 2017

The information from the last assignment is split into Forums and Tasks description. This is very easy to fix and not doing it shows passivity from the organizers

创建者 Julia L

Feb 09, 2016

First professor was incredibly good at giving an overview over design choices in data visualizations.

Second professor sadly somehow spoke too fast and had less of a red thread through his presentations.

The first and second week of courses were good, the third week however was too hypothetical and one-sided.

创建者 Tebogo M

Feb 02, 2017

Nice course into data science

创建者 Gregory R

Nov 10, 2016

Good class, very effective hands-on homework tasks. One thing I found is that the time for homework is very underestimated by course creators. It takes much longer to complete the tasks than indicated and within time given. Otherwise, very happy with taking the class.

创建者 Seth

Jan 14, 2016

Excellent content. Detractors were some of the lectures had a continual popping in the audio and the instructions for the final assignment seemed a little dated and required a bit more work to figure out the correct steps.

创建者 Chen

Oct 02, 2016

The instructions are very good, and it's nice to work on real big data. Also it is very helpful for hearing information about how a data scientist should consider problems carefully. Without taking the class, it wouldn't be easy for me to rationalize for example cost and sensitivity issues.

However I took out one star because of the instruction for the final assignment being out of date. Although the task it self is not too hard to figure out. The initial instruction on how to start using AWS was outdated.

创建者 Fermin Q

Nov 12, 2016

Great and useful first week about visualization, although I wish it would cover more material . The ethics and cloud computing felt somewhat incomplete, but useful as well.

创建者 Mary A

Nov 03, 2018

The assignments for this course are outdated and not well supported.

创建者 Ivajlo D

Nov 13, 2018

The material was very general and I think a little bit superficial especially the first week concerning visualisation. There was very little connection between the videos and the actual required skills for the assignments and although I like learning by doing a little bit of guidance would have been nice so that you know that you are doing things in the best or most appropriate way.

创建者 Roberto S

Jun 13, 2017

I took it when the specialization was just a single, 12 week course. The assignments are barely updated and you have to rely on instructions found in the forum. It has audio quality issues as well. Otherwise, the content it top notch.

创建者 Joris D

Jul 08, 2017

Not really the same quality as the first two courses in this specialisation. The lectures videos are somewhat disconnected from the assignments.

创建者 Anton S

Dec 19, 2015

OK, but obvious for someone who has worked with data science.

创建者 Angel S

Jan 07, 2016

Nice course

创建者 Solvita B

Apr 20, 2016

Nice lectures with lot of good information. AWC setup instruction need to update according new AWC interface.

创建者 Jana E

Dec 07, 2017

Guest lecture is interesting, other lectures are of quite low quality

创建者 Alexandre C

Apr 01, 2016

Very interesting subject. Nevertheless the training course material is too theorical.

创建者 Fernando S

Nov 18, 2016

The peer-review assignment is not properly designed. From my own experience, colleagues tend to underestimate other people's projects. In addition, the peer-review had an extra/optional advanced component (analysing criminal patterns for a second city; comparing patterns across two cities), which I carried out but got no extra credit for. The extra work was not even part of the assignment classification -- there should be a bonus question for students who carry out the advanced part of the assignment!