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Avis et commentaires pour l'étudiant pour Pojet final de science des données par Université Johns-Hopkins

4.5
870 notes
228 avis

À propos du cours

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....

Meilleurs avis

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 - 219 sur 219 Examens pour Pojet final de science des données

par 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.

par 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'

par 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The course is outdated and abandoned by the teachers.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.

par 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.

par 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.

par 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.

par 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.

par unijoy

Mar 23, 2016

need more details

par 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.

par 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.

par 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.

par 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.

par 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.

par 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.

par 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!

par Runhao Z

Nov 28, 2017

bad ending

par 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.

par Stephen E

Jun 27, 2016

A poor end to a poor Coursera specializations.

par 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.