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

Good experience, little guidance for the topic but big challenge

Challenging real life project that apply the academic knowledge

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.

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.

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

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.

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.

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