Chevron Left
Вернуться к Communicating Data Science Results

Отзывы учащихся о курсе Communicating Data Science Results от партнера Вашингтонский университет

Оценки: 135

О курсе

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....

Лучшие рецензии

Фильтр по:

1–25 из 35 отзывов о курсе Communicating Data Science Results

автор: Vijay P

8 июня 2019 г.

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.

автор: Chen Y

2 окт. 2016 г.

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.

автор: Mary A

3 нояб. 2018 г.

The assignments for this course are outdated and not well supported.

автор: Reese

22 июня 2017 г.

yikes update the github resources please

автор: Piyush K

7 янв. 2018 г.

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.

автор: Red R

11 янв. 2022 г.

Outdated labs and technology, no updates and no free offer by Amazon.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

автор: Weng L

6 июня 2016 г.

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.

автор: Bingcheng L

7 авг. 2019 г.

Too little people participated and long peer review time.

But the course content is good.

автор: Shivanand R K

18 июня 2016 г.

Excellent thoughts and concepts presented.

автор: Menghe L

27 июня 2017 г.

very good course for learner

автор: Daniel A

18 дек. 2015 г.

Great class !

автор: Julia L

9 февр. 2016 г.

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.

автор: Gregory R

10 нояб. 2016 г.

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.

автор: Seth

14 янв. 2016 г.

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.

автор: Fermin Q

12 нояб. 2016 г.

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.

автор: Albert P

18 июня 2017 г.

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

автор: Tebogo M

2 февр. 2017 г.

Nice course into data science

автор: Fernando S

18 нояб. 2016 г.

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!

автор: Ivajlo D

13 нояб. 2018 г.

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.

автор: Roberto S

13 июня 2017 г.

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.

автор: Joris D

8 июля 2017 г.

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

автор: Solvita B

20 апр. 2016 г.

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

автор: Alexandre C

1 апр. 2016 г.

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

автор: Jana E

7 дек. 2017 г.

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

автор: Anton S

19 дек. 2015 г.

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