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Kursteilnehmer-Bewertung und -Feedback für Data Science Capstone von Johns Hopkins University

4.5
879 Bewertungen
232 Bewertungen

Über den Kurs

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....

Top-Bewertungen

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!!!

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176 - 200 von 223 Bewertungen für Data Science Capstone

von Gary B

Sep 15, 2017

tough capstone and took a lot of time

von Robert W S

Mar 19, 2017

Although this project is very open-ended with little guidance, it definitely requires the "full-stack" of data science to complete.

von 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.

von Yew C C

Jul 20, 2016

Good and interesting project.

von Michal S

Mar 03, 2018

The course project was very interesting. It can be challenging if you want to do it properly or easy if you just want to pass. I tried to do it properly for which I had to repeat the course 3 times, but in the end it was good - I think I learned a lot.

von Emi H

Jun 22, 2017

Good project. Got me to think outside the box and really challenge myself.

von Juan M

Jun 08, 2016

This is a great capstone project. It requires the student to really have an understanding of the concepts learned throughout the specialization and apply them to build a prediction app. There is very little guidance asides from the discussion forum which could be discouraging at first. Otherwise I would've rated it 5 stars.

von Wesley E

Aug 11, 2016

Overall a good course that makes you learn a lot on your own (unlike the rest of the series). Maybe a bit too much self learning. However, if you can complete it does give you a lot of learning especially in some text analysis which hasn't been covered before.

von Yong-Meng G

Jun 20, 2017

The capstone will be much easier if participants have hands-on experience and understanding of how R works. For those who have managed to overcome the steep learning curve, the reward from the learning experience is well worth it.

von Yoga A Y

Oct 11, 2016

The Capstone Project makes you summarizes what you have learnt so far and take it to the next level, natural language processing . Besides, the ability to create a working app is a reward by itself.

It is challenging and interesting at the same time.

von Greig R

Mar 16, 2018

A tricky end to the specialisation - but quite a lot of fun.

von shashank s

Sep 16, 2017

It was a challenging project and really pushes you to learn and manage on your own. It also pushes you to build and end to end product within time and memory constraints. Learned a lot during this project!!

Thanks!!

von Chonlatit P

Jun 27, 2019

Project is good for practice what you've learnt

von Neeraj A

Sep 08, 2019

Feeling proud after completing all the courses under Data Science Specialization. This was not an easy task to complete especially if you are not familiar with the Statistics. Requires continuous dedication and motivation to follow and complete. Course is well designed and cover most of the topics. Its just stats part can be enhanced further to cover some basic aspects. Thanks for all the support

von Guilherme B D J

Mar 24, 2017

The main reason for my rating is because the course is so "loose" on what your are supposed to achieve incrementally every week that it can lead to some hard situations.

Just to give my example: the first week was piece of cake and I didn't feel like it really contribute for the following weeks. Then, I was struggling with the suggested library (tm) until I got support through the discussion forums and someone suggested me to use quanteda.

Then thinks started to run smoothly, or so I thought. When implementing the language model (which, at first, I thought was supposed to be KBO), I got stuck for a long period. Not because my model was wrong (I was able to implement it and to check it against some hand-written and proved examples - which I should probably thank again), but because I was not able to make it run efficiently enough for the given constraints.

Being stuck in this stage for longer than I wanted, I had to sacrifice another important steps of data analysis pipeline in order to not jeopardize my final delivery by not meeting the final due date. I know that this is exactly what will happen in the "real" life, but I think that some better guidance could guarantee the students spent a more even amount of time in across all steps.

All things considered, I think the Capstone was really interesting and likely took more than the 4-9 hours per week, but most of this is probably because of the problems I faced.

I believe that with a better guidance on the paths to follow or maybe some suggested libraries to use, a lot of "noise" (useless difficulty) could be removed and this course would definitely get more starts.

von Antonio E C

Dec 30, 2016

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.

von Pradnya C

Apr 14, 2016

Most stressful but interesting. Not enough material was provided

von Adam B

Jun 06, 2016

I liked every course in this specialization except

von Rajib K

Sep 04, 2017

I would say, if we could introduce a capstone project more related to the first

von Matias T

Jul 18, 2016

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

von Tracy S

Nov 28, 2016

it could've given more instructions!

von Richard I C

Jul 19, 2016

As a capstone to a series of courses that covered data science and R, I found this one to be a bit lacking. There was no involvement from the professors at JHU or the folks at SwiftKey. As was mentioned in another review, the course feels abandoned. All you get a few short (two minutes or so) videos that give you little in the way of instruction or direction. Basically, they just say, "Go do this. Good luck!"

There were also no Mentors or TAs to guide students or answer questions. It was the students helping each other through the forums. Sometimes it was helpful and everyone involved learned something. Other times, it was the blind trying to lead the visually-impaired.

On a positive note, you will use all of the skills from the previous courses: writing R functions, performing exploratory analysis and publishing it via RPubs. Your final product will be displayed for everyone via ShinyApps and a presentation using R Presentation (also published via RPubs).

On a(nother) negative note, the topic of Natural Language Processing is not an easy one to just walk into and feel confident in providing a working next-word prediction algorithm in about eight (8) weeks. You're reading academic journal articles, watching multiple videos from another Coursera course (which actually focuses on the topic of NLP, and takes place over several courses and several months!).

Supposedly, there is work going on to update the course, so hopefully future students will get a better experience. I did take a bit away from this course, especially since I made more than one attempt to complete it. However, it was definitely a shock to find myself missing those things that one typically finds in a learning environment -- descriptive background, assistance to problems, etc. -- and seeing that I was for all intents and purposes on my own. Even in the professional world of data analysis, I have never experienced the lack of support that I found in this course.

With that, I am giving it three (3) stars. As I said, I did learn a bit, but it was a bit of a struggle that required multiple attempts to complete. This would have been better off as a stand alone topic (which it already is by another Coursera affiliated school), or having a capstone course that builds on a topic more in the wheelhouse of the JHU professors: a capstone project focusing on bioinformatics or biostatistics would have been amazing in comparison to this.

von Andrew S

Jun 26, 2017

I felt this course was the weakest of the series. The capstone focuses on building an NLP application, which although I find interesting, does not make for a good final problem as NLP was not really covered in the specialization and NLP is particularly challenging in R. That said, the series as a whole is well worth the time and effort.

von Michael N

Jan 13, 2018

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

von Hang Y

Feb 10, 2018

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.