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學生對 约翰霍普金斯大学 提供的 数据课程毕业项目 的評價和反饋

4.5
979 個評分
260 條評論

課程概述

The capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners....

熱門審閱

NT

Mar 05, 2018

Capstone did provide a true test of Data Analytics skills. Its like a being left alone in a jungle to survive for a month. Either you succumb to nature or come out alive with a smile and confidence.

SS

Mar 29, 2017

Wow i finally managed to finish the specialization!! definitely learned a lot and also found out difficulties in building predictors by trying to balancing speed, accuracy and memory constraints!!!

篩選依據:

151 - 数据课程毕业项目 的 175 個評論(共 251 個)

創建者 Michał D

Jan 12, 2018

Great final test!

創建者 Muhammad Z H

Oct 21, 2019

Thanks Professor

創建者 Harland H

Apr 08, 2019

Great NLP intro!

創建者 Javier E S

Dec 02, 2018

Excellent course

創建者 Fábio A C

Jun 19, 2017

Excelent course!

創建者 Jeremy O

Mar 13, 2017

best course ever

創建者 Anang H M A

Sep 13, 2018

A great course!

創建者 Raja J

Mar 27, 2018

Awesome course

創建者 Ahmed Z

Oct 03, 2019

Great Course

創建者 Pedro M

Jan 30, 2020

Pretty cool

創建者 Shailesh S P

Apr 28, 2020

Very Good

創建者 Anand V

Jun 19, 2017

Excellent

創建者 Diego T B

Oct 19, 2018

engaging

創建者 Laro N P

Sep 13, 2018

Awesome.

創建者 Sergio R

May 10, 2018

Thanks!

創建者 Amit K

Jul 06, 2017

Thanks.

創建者 Abdelbarre C

Jan 09, 2018

Thanks

創建者 Efejiro A

Feb 23, 2019

Cool

創建者 Ganapathi N K

May 24, 2018

Nice

創建者 Sherif H M A A

Feb 13, 2018

Good

創建者 Thuyen H

May 31, 2016

good

創建者 Prabhakar B

Jan 15, 2019

E

創建者 Anil G

Jul 27, 2018

E

創建者 Dwayne D

Sep 02, 2017

Completion of this project requires most (all?) of the skills you will have learned in completing the prerequisite courses. If you've worked to ensure you truly understand the concepts, tools and techniques presented in the prerequisite courses, you will be able to complete this project. The problem domain is a little different from most of the examples in the prerequisite courses. I find that a good thing. Whenever I learn something I believe to be useful, I always wonder how it applies in other contexts. This course was an exercise in doing just that — applying what you've learned to a "new" (i.e., new to me) a domain.

Heads up / Be aware: If you're "like me" — inexperienced with NLP, and one of those people who doesn't feel quite right about using a recommended toolset or algorithm until I understand why it's the right tool for the job — you should start reading up on the basics of text mining, NLP and next-word prediction models 1-2 weeks before you start the course. For some, that might be overkill; but I'm a slow reader at the end of a workday (we all have day jobs, right!?). Given this foundational understanding, I felt comfortable making tradeoffs among the state-of-the-art and the practical, given the project objectives, my own time constraints, etc. Reading the course forums and reviews, I think some who had trouble completing the project weren't able to take sufficient time to get oriented with this domain before attempting to build their first word prediction model.

Note: By "foundational", I mean enough to intuitively grasp why what's accepted as best practice is that. When I've read about someone's approach to solving a problem, and I'm able to say "makes sense, but I probably don't need to do X or Y to meet the need for this effort", then that's often enough… But :-) because I at times overthink things (don't we all!), I get a little more comfortable when I at least skim over descriptions of how a couple others have solved a similar problem; and I can see patterns of convergence… I do NOT mean enough to write your own thesis, unless that's what you really want to do. Whatever floats your boat! LOL

I have a software development background (and completed the previous courses in the specialization), so translating approaches I found described in various sources into code wasn't "easy"; but it wasn't a barrier, either. I was helped along GREATLY by the existence of R packages such as tm and tokenizers, and I was always able to find guidance on addressing thorny issues via "good ole Google Search". Most often, my searches would lead me to StackOverflow or write-ups from capstone project alumni. While I did my own write-ups and wrote my own code, I benefited in a big way from lessons learned by others who've already tackled similar problems.

I would recommend the Data Science Specialization by JHSU, which (as it should be) is a package deal with the capstone project. Applying what I learned to a new domain really solidified my understanding and has whet my appetite for the next challenge.

創建者 Angela W

Apr 17, 2018

Overall, I was semi-satisfied with the capstone project:

On the negative side, my foremost issue is that the project has very little to do with what we learned in the nine courses before. I get that you will always see new data formats as a data scientist, but having the whole course cover numeric data and then having the final project be on text data where you can't apply what you learned seems sub-optimal. Also, to me it seemed that the accuracy increased mostly with how much data you train your algorithms on, and not so much how you design your algorithm. My second issue is that the class only starts every two months, and the assignments are blocked before the session starts so you can't see them if you're trying to get a head start. What happened to everyone learning at their own pace? I have a lot to do and had to switch sessions at least once for most classes, and this class was really stressful for me because I didn't want to move my completion back by two months. Lastly, I really hate RPresenter and that the instructors force us to use it, but maybe that's just me.

On the positive side, I did learn a lot: The basics of text prediction, how to do parallel programming in R and how to set up an RStudio instance on AWS (the latter two are not very hard, I recommend them to anyone struggling with gigantic runtimes, as long as you're willing to invest like $40 or so for the computing power). I liked that the guidelines were very broad, so there was a lot of room for creativity. I also finally found out how to make an pretty(-ish) presentation in R, though I would always choose Powerpoint in real life.

I really enjoyed the series as a whole and learned a great deal.