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Data Science Capstone(으)로 돌아가기

존스홉킨스대학교의 Data Science Capstone 학습자 리뷰 및 피드백

1,188개의 평가
315개의 리뷰

강좌 소개

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....

최상위 리뷰

2018년 3월 4일

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.

2017년 3월 28일

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!!!

필터링 기준:

Data Science Capstone의 305개 리뷰 중 201~225

교육 기관: Jeremy O

2017년 3월 13일

best course ever

교육 기관: siddhesh p

2020년 11월 26일

one of the best

교육 기관: Anang H M A

2018년 9월 13일

A great course!

교육 기관: Raja J

2018년 3월 26일

Awesome course

교육 기관: Ahmed M Z

2019년 10월 3일

Great Course

교육 기관: Pedro M

2020년 1월 30일

Pretty cool

교육 기관: Luv K

2020년 11월 27일

it was fun

교육 기관: Omkriti M

2021년 5월 28일

good one!

교육 기관: Suprotik S

2020년 9월 28일


교육 기관: Shailesh P

2020년 4월 28일

Very Good

교육 기관: Anand V

2017년 6월 19일


교육 기관: Diego T B

2018년 10월 19일


교육 기관: Laro N P

2018년 9월 13일


교육 기관: Hrithik M

2021년 7월 9일


교육 기관: Sergio R

2018년 5월 10일


교육 기관: Amit K

2017년 7월 5일


교육 기관: Abdelbarre C

2018년 1월 9일


교육 기관: Efejiro A

2019년 2월 23일


교육 기관: Ganapathi N K

2018년 5월 24일


교육 기관: Sherif H M A A

2018년 2월 13일


교육 기관: Thuyen H

2016년 5월 31일


교육 기관: Prabhakar B

2019년 1월 14일


교육 기관: Anil G

2018년 7월 27일


교육 기관: Dwayne D

2017년 9월 1일

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.

교육 기관: Angela W

2018년 4월 17일

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.