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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
125 個評分
35 個審閱

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 32 個)

創建者 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.

創建者 Reece K

Jun 23, 2017

yikes update the github resources please

創建者 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.

創建者 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.

創建者 Mary A

Nov 03, 2018

The assignments for this course are outdated and not well supported.

創建者 Jana E

Dec 07, 2017

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

創建者 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.

創建者 Menghe L

Jun 27, 2017

very good course for learner

創建者 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

創建者 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.

創建者 Lei Z

Mar 22, 2017

The course does not has lecture slides that is better for students to understand.

創建者 David A

Mar 13, 2017

The big assignment at the end contains instructions that are outdated and incomplete. Given the length of the course, it feels like you already need to know the material before even taking it.

創建者 Tebogo M

Feb 02, 2017

Nice course into data science

創建者 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!

創建者 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.

創建者 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.

創建者 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.

創建者 Christine

Sep 17, 2016

I was very upset by the end of this course. The documentation was terrible. I ended up spending way to much on AWS even though I managed it the best I could Terminating instances. In general, I think this course would have been more fulfilling if the documentation was appropriate.

創建者 Yu I

Jul 21, 2016

This course is really bad. The instruction is not enough to solve the programming assignment. The almost contents aren't related to communicating data science result.

創建者 Andre J

Jun 21, 2016

I'll say the same about this class as the rest of the specialization, if you have the skills to complete this course then you don't need to take this course. If you don't have the skills to complete this course, you will not complete this course. The course instruction is at 10000 feet level and the assignments are very challenging and the course will NOT teach you the skills required to complete the assignments. The AWS final assignment is a very much throw you into the deep end with no real instruction (well at least completely outdated instructions) and will expect you to swim (or more likely for most people, to drown).

I recommend the Machine Learning Course (from Bill's colleagues) at University of Washington. That is a course where you get some real instruction and understanding of how to complete assignments (though still very challenging).

創建者 Shivanand R K

Jun 18, 2016

Excellent thoughts and concepts presented.

創建者 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.

創建者 Ben K

May 27, 2016

This course is not maintained. It's flat out exploitative to throw students at an AWS assignment without updated instructions and with outdated versions of pig scripts, etc. They're setting students up to hemorrhage money on AWS and possibly not get anything out of it. Under no circumstances should you take this course or even this specialization so long as this assignment is gating it.

創建者 Yuan-Fen K

Apr 26, 2016

Much of the assignment was out of date. The content was not related to big portion of the assignment. There was no way of getting clarification over the outdated assignment content.

創建者 Solvita B

Apr 20, 2016

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