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

4.5
886 Bewertungen
234 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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201 - 225 von 226 Bewertungen für Data Science Capstone

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 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.

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 Rajib K

Sep 04, 2017

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

von Max D

Aug 19, 2019

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

von John D M

Sep 20, 2019

A capstone is typically defined as integrating key material from a course. This capstone did not require material from key courses, specifically the machine learning, regression models, and statistical inference courses. That was a great shame. Instead, it threw us into a completely new area, Natural Language Processing.

There were many complaints about that, and I agree. However, it was a challenging task to explore an area in data science we didn't touch on, and challenging in terms of the programming and enormous data file sizes. In that sense it was probably good prep for unexpected challenges in the workplace and therefore good training to make us real data scientists. Still, I would like to see the capstone rejigged to include material from the missing courses. As for NLP, some students claim it is not a useful area to study, but in my case it is exactly the right thing for me to study as I work with analyzing user queries in the form of tickets in a CRM. I found it especially trying to try to integrate some material such as Kneser-Ney theory and opted for a more basic approach. My learning experience would have been better with some proper instruction in that area.

von unijoy

Mar 23, 2016

need more details

von Lee M S

Apr 23, 2016

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

von Sevdalena L

Dec 10, 2016

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.

von Clara B

Sep 21, 2016

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.

von Michael S

Jul 02, 2016

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'

von CW

Jul 17, 2017

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.

von Dmitri P

Mar 30, 2016

The course is outdated and abandoned by the teachers.

SwiftKey engineers are nowhere to be seen.

There is no guidance.

von Tavin C

Aug 17, 2017

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.

von WONG L C

Jun 08, 2016

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.

von Marco S C

May 26, 2016

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.

von Jeffrey G

Jan 17, 2018

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.

von Sandro R

Jun 28, 2019

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.

von Stephen E

Jun 27, 2016

A poor end to a poor Coursera specializations.

von Jesse S

Apr 29, 2016

Coursera lost my thoughtful 2-star review so I am replacing it with this. I learned a lot through my own efforts and through the efforts of students who bothered to post in the forums. The one mentor disappeared half-way through the course.

von Aleksey K

Mar 16, 2016

None of the previous classes will prepare you for this one. This is not really a class, but rather a project on a topic NEVER covered in any of the previous classes in this specialization.

von Chun-Fu W

Mar 20, 2017

In my opinion, this course is a waste of time, it simply throws a bunch of links and terminology for you to google and research. The project is interesting but once again, you have to do tons of research and take up other courses to fill the gaps (might as well do the other courses instead of this one).

I do not recommend this course or the specialization.

von Matthias R

Sep 17, 2017

Unfortunately, the Data Science Capstone was the worst of all the courses in the specialization. Most of the techniques and models/theories needed to complete the capstone are not covered in the other courses, e.g. natural language processing, markov models, etc.

von Joerg L

Jun 04, 2016

I currently taking this capstone and I must unfortunately say that this is the most worst course in the whole specialization. Of course the topic NLP and word prediction is interesting, but the problem is, that this is a dead course. A couple of students in the forum strugeling with details, but there is NO Mentor, no Professor or other course staff and no SwiftKey engineer as announced in the Project Overview.

So everything you have to figure out completely by yourself and this takes a lot of more time than the 4-9 hours. And also why should you pay for a course where you learn anyway only ba your own.

Pick any intersting topic you would like to work on and invest the time in this instead of paying for this Capstone without any support form Coursera, JHU or SwiftKey.

von E. C

Feb 18, 2017

NLP is a total different thing and should be a course by itself. I would prefer a a large scale machine learning capstone where we could make models and it would fit better to real life situation! Through all the courses I worked hard only to reach NLP capstone? this doesn't feel right! Please fix it!