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学生对 约翰霍普金斯大学 提供的 数据课程毕业项目 的评价和反馈

4.5
1,190 个评分
316 条评论

课程概述

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....

热门审阅

NT
Mar 4, 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 28, 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!!!

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201 - 数据课程毕业项目 的 225 个评论(共 306 个)

创建者 Fábio A C

Jun 19, 2017

Excelent course!

创建者 Jeremy O

Mar 13, 2017

best course ever

创建者 siddhesh p

Nov 26, 2020

one of the best

创建者 Anang H M A

Sep 13, 2018

A great course!

创建者 Raja J

Mar 26, 2018

Awesome course

创建者 Ahmed M Z

Oct 3, 2019

Great Course

创建者 Pedro M

Jan 30, 2020

Pretty cool

创建者 Luv K

Nov 27, 2020

it was fun

创建者 Omkriti M

May 28, 2021

good one!

创建者 Suprotik S

Sep 28, 2020

Excellent

创建者 Shailesh P

Apr 28, 2020

Very Good

创建者 Anand V

Jun 19, 2017

Excellent

创建者 Diego T B

Oct 19, 2018

engaging

创建者 Laro N P

Sep 13, 2018

Awesome.

创建者 Hrithik M

Jul 9, 2021

Awesome

创建者 Sergio R

May 10, 2018

Thanks!

创建者 Amit K

Jul 5, 2017

Thanks.

创建者 Abdelbarre C

Jan 9, 2018

Thanks

创建者 Efejiro A

Feb 23, 2019

Cool

创建者 Ganapathi N K

May 24, 2018

Nice

创建者 Sherif H M A A

Feb 13, 2018

Good

创建者 Thuyen H

May 31, 2016

good

创建者 Prabhakar B

Jan 14, 2019

E

创建者 Anil G

Jul 27, 2018

E

创建者 Dwayne D

Sep 1, 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.