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Learner Reviews & Feedback for Communicating Data Science Results by University of Washington

135 ratings

About the Course

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 - 25 of 35 Reviews for Communicating Data Science Results

By Vijay P

Jun 8, 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.

By Chen Y

Oct 2, 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.

By Mary A

Nov 3, 2018

The assignments for this course are outdated and not well supported.

By Reese

Jun 22, 2017

yikes update the github resources please

By Piyush K

Jan 7, 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.

By Red R

Jan 11, 2022

Outdated labs and technology, no updates and no free offer by Amazon. Please avoid taking it!

By Weng L

Jun 6, 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.

By Bingcheng L

Aug 7, 2019

Too little people participated and long peer review time.

But the course content is good.

By Shivanand R K

Jun 18, 2016

Excellent thoughts and concepts presented.

By Menghe L

Jun 27, 2017

very good course for learner

By Daniel A

Dec 18, 2015

Great class !

By Julia L

Feb 9, 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.

By 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.

By 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.

By 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.

By 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

By Tebogo M

Feb 2, 2017

Nice course into data science

By 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!

By 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.

By 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.

By Joris D

Jul 8, 2017

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

By Solvita B

Apr 20, 2016

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

By Alexandre C

Apr 1, 2016

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

By Jana E

Dec 7, 2017

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

By Anton S

Dec 19, 2015

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