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

Project is good for practice what you've learnt

Good test of what we learned in the courses.

I really had fun working on this project.

Nice balance of focused and open-ended

tough capstone and took a lot of time

Good and interesting project.

Very fun final project!

Interesting assignment!

Excellent course.

Great project.

Very hard!

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.

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!

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.

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

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.

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

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

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

Most stressful but interesting. Not enough material was provided

I liked every course in this specialization except

it could've given more instructions!

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.

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.