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Data Science Capstone(으)로 돌아가기

존스홉킨스대학교의 Data Science Capstone 학습자 리뷰 및 피드백

4.5
별점
1,201개의 평가
320개의 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

NT

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.

SS

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 310개 리뷰 중 276~300

교육 기관: Filipe R

2018년 10월 7일

Great project.

교육 기관: Kevin M

2018년 1월 15일

Very hard!

교육 기관: David M

2016년 7월 21일

This was essentially a self-study project with some social peers. The topic, approach, and standards were different from all of the other units in the Data Science specialization. I found the other units more enjoyable.

Learning the essentials of NLP quickly is necessary to begin the project. I ordered a textbook, for example, and I was fortunate that it arrived quickly. If NLP is a prerequisite for this capstone project - whether in the form of a prior class or textbook knowledge - this should be indicated clearly on the course description page.

Nevertheless, the main learning that I achieved with this course was in the area of software engineering - specifically, how to take advantage of vectorization in R to achieve reasonable computing performance. While this is a valuable skill, it doesn't seem the proper focus of a capstone course in a sequence focused primarily on other topics.

As noted elsewhere in these comments, there was a complete absence of any traditional teaching support. Learning outcomes suffered as result. The missing resources included instructors, mentors, partners, and learning materials.

The course site notes an expected time requirement of a few hours per week. My commitment was 20 hours per week, under some pressure. Numerous students take this "course" multiple time, in order to arrange for reasonable software development time.

Producing working software was fun, as it always is. The course learner community was supportive, which is fortunately typical for Coursera.

All in all, this project was *not* an effective capstone for the Data Science specialization. The project was interesting in its way, but it felt 'parachuted in' to this learning sequence.

교육 기관: Visha P

2021년 8월 25일

Really enjoyed this specialization and have learned a ton (and now applying it too!). Sadly, won't be able to get to finish off the specialization because the capstone course is just too different from what we learned, especially with the prediction model piece. Not using the package caret at all and that was the main premise of that course and I just really can't figure out how to build out prediction model with this kind of structure. Kind of bummed because I was looking forward to the certificate - but I just couldn't figure it out in a way that would be my own. Shoutout to the people who are able to figure this out and extrapolate all their learning to finish it. My brain just doesn't work well like this. I need examples and then can replicate. Please let me know if you ever modify the capstone experience and I will try to come back and finish it! Thanks for all the great material - will be using for sure.

교육 기관: Diego C G

2016년 4월 13일

Could be better. The teacher sometimes explain the concepts in a hard way, and not always shows how to do in practice.

But you will get curious and in case of doubts, you can find more simple explanations on the web, and the forum is very good.

The assignments are hard, you will need do research to accomplish then, but is the best way to learn.

I think the specialization is good to someone without much knowledge on the field (like me). But it's only the start!

교육 기관: Antonio E C

2016년 12월 30일

It's been a challenge to learn all these new concepts and package them into a working product in such a small period of time. I am glad of the things I learned. Also, in my opinion the materials / resources given to this course are scarce compared with previous courses of the specialization.

교육 기관: Matias T

2016년 7월 18일

Hi, the prject was nice and at the end I learned some new things, but it didn't have people to provide any guide. In the videos it was said that personal from SwiftKey will be there as well as JHU teachers could provide some insights. It looked a bit like a phantom course

교육 기관: Rainer-Anton E

2021년 8월 3일

I liked all previous courses. However, it feels like the task is a little outdated.

Maybe it would already help if students were free to use the programming language and approach they prefer. Like python and RNN models.

교육 기관: H Y

2018년 2월 10일

It's an inspiring project in the field of NLP, however, the major concern is that this topic and the corresponding skills have never been introduced before the capstone project.

교육 기관: Max D

2019년 8월 19일

NLP module should definitely be included into JHU Data Science specialization.

교육 기관: Michael N

2018년 1월 12일

Had to learn a lot on our own but very valuable content once acquired.

교육 기관: Pradnya C

2016년 4월 13일

Most stressful but interesting. Not enough material was provided

교육 기관: Adam B

2016년 6월 6일

I liked every course in this specialization except

교육 기관: Tracy S

2016년 11월 27일

it could've given more instructions!

교육 기관: Jeffrey G

2018년 1월 16일

With the exception of R Shiny programming, there was nothing about this course that required any real knowledge of anything in any course of the JHU Data Science certificate track. Why do you ask? Well, most of the class was just about learning natural language processing (NLP), which wasn't covered. What about R programming, you ask? Most of the NLP packages in R that I tested out couldn't process a 200MB text file in a reasonable amount of time or with a reasonable memory footprint. I ran Python and R programs in parallel to do sentence and word tokenization, and Python's nltk was (not exaggerating) 100x faster than R's NLP package, and R's tm package took 4GB of memory to parse the same 200MB corpus. In 2018, that's just unacceptable. There's no way you could ever write production-quality NLP code using these R packages. After the course was finished, someone pointed out an R package that could adequately accomplish the task, but by then it was far too late. Even R's basic data structures themselves weren't up to the challenge. I ended up building my model in Python, exporting it as JSON, and then importing that into my Shiny app. Comparing basic data structures in Python and R to represent the same JSON file (i.e., just read in the file and measure the size of the resulting object), R's list was nearly 2x as large in RAM than Python's dict. All of this combined with really very little reference to most of the material in the other nine classes in this track left me very disappointed. The reason I gave the class two stars and not one was because what we did learn about NLP was useful. Having to solve a gnarly, real-world problem starting from raw data is useful. Having to write an app with actual users interacting with it is useful. But could just about everything about this class have been done a lot better? Yes. I think a machine learning project that tied together everything that we'd worked on up until this point would have been a lot more fun and rewarding.

교육 기관: Leo C

2020년 7월 23일

Sadly disappointed. I can see how this worked before, when most people were active on the forums, but now it's extremely frustrating. Not because it's hard, I do not mind that, but because you really have to DIG through the forums to find vital information.

For instance, there are two quizes, you need 80 % or more to pass. However, the app you are simulating only get 20-30 % on such quizes, and you're not REALLY supposed to get that high. That's in the forums, but not on the quiz itself.

Also, very few things you've learned in the ML part of the specialization is actually used, and they specifically points you to a MOOC by another university. That's not very comforting.

The good part though is that the actual final exam does not really need good predictions to work, just an app that functions as you say it does. My tip? Look at the NLP, google a bit and learn the basics, then make an app that's as simple as possible - Then learn NLP with some guidance.

교육 기관: Michael S

2016년 7월 2일

Of all the offerings in the specialization, this one felt like it was thrown together in less than hour. I expected to have to learn quite a bit of material on my own, but even the references to additional materials were very thin.

I could have saved many days if more guidance on the project workflow would have been given. The pre-processing of the data was quite extensive (9 steps before generating the ngram tables I used in my model) and was the key to getting decent results IMHO, but one had to step on a quite a few landmines to figure this out.

The problem was an interesting one and I ended up reworking it after passing with 95% (the only class in the specialization I didn't get 100% on) because I didn't have time to implement much of what I had to figure out by 'hard-knocks'

교육 기관: Marco S C

2016년 5월 26일

Unfortunately this project is not fully aligned with all the previous program, which is a shame. Ideally, the project was more related to quantitative data, or have compulsory module NPL. It was certainly a very important learning, but very stressful to have to grasp NPL and do the project in a short time.

Learning NPL in short time in a DIY way without any help it was very negative and stressful.

교육 기관: Sandro R

2019년 6월 28일

As other reviewers said, the Capstone is too unconnected to the rest of the specialization. In the end, there is no metric as to what makes your model successful, it's just the Slides and the appearance of the Shiny app that counts towards the total mark. Also, the topic (Natural Language Processing) is just too unconnected to anything seen in the other courses. It was fun, but felt a bit off.

교육 기관: Tavin C

2017년 8월 17일

The series leading up to the capstone was excellent but the capstone itself was a disappointment. Very little instruction was provided and the grading criteria were flawed. Also, most of what we learned in the first 9 courses about statistics and machine learning turned out to be irrelevant to the capstone project.

교육 기관: Claudia R

2016년 9월 21일

The course has nearly nothing to do with the previous themes. I already have had enough knowledge, but as there is no support by the team it seems to be rather time consuming for others.

교육 기관: WONG L C

2016년 6월 7일

I hope it will involve statistics analysis in the capstone project. It is kind of bias to apply NLP knowledge and develop data product in the capstone project.

교육 기관: Sevdalena L

2016년 12월 10일

Not enough information on how to approach the final project. The project itself is very time consuming with lots of self learning and unclear specifications.

교육 기관: Lee M S

2016년 4월 23일

The capstone project doesn't fully utilise d knowledge from earlier modules such as Machine Learning, statistical analysis, regression models n etc.

교육 기관: CW

2017년 7월 16일

No physical way to complete the class within one session. Little is learned, no instruction is given, just build a thing that sort of works.