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.
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!!!
par Jeremi S•
Dec 07, 2018
Challenging. The course could possibly offer a 'here's how it could be done' ideal example after final submission and pass.
par Terry L J•
Nov 28, 2018
I appreciate all the work they put into creating the course,. However, it can be frustrating to follow. It would be nice if they would structure it in a more organized fashion.
par Robert C•
Aug 03, 2018
I wish that either there were a choice of capstone projects, or that there were a more numerical component to the analysis than such a pure text based assignment.
par Filipe R•
Oct 07, 2018
par 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.
par Emi H•
Jun 22, 2017
Good project. Got me to think outside the box and really challenge myself.
par Telvis C•
Jul 16, 2016
I enjoyed the course. This course took me waaaay more time than I thought because I struggled with a few issues. First, I wish I'd started by taking the NLP online course before starting the Capstone (https://www.youtube.com/watch?v=-aMYz1tMfPg). There was an issue installing RWeka, RJava and it took me several days to work through the issues. I eventually moved to using quanteda (https://cran.r-project.org/web/packages/quanteda/vignettes/quickstart.html). I also waited far too long to develop a method to test my model using a subset of the training data, so I could test whether changes to my model improved and reduced performance. It turns out that my model trained on a 25% sample performed just as well as a model trained on 100%. I'm thankful for the Discussion Forum and final peer review process. Both helped me learn how I can improve my model and demo application. I really appreciate the instructors for creating this specialization. I've learned a lot.
par Carlos D C G•
Mar 27, 2017
Very interesting, but Capstone is much more difficult than the rest of the course.
Be sure to study carefully the first courses, and don't rush.
par Greig R•
Mar 16, 2018
A tricky end to the specialisation - but quite a lot of fun.
par 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!!
par Rudolf E•
Jun 20, 2017
Great course, great content, didn't like the final capstone project though.
par Romain F•
Jul 03, 2017
A very tough and challenging project, but a great way to learn a lot about Natural Language Processing and algorithm coding in R, and in the end to have a cool Shiny app to add to your portfolio. The project weekly structure could be enhanced (maybe adding one more week could help) and the weekly instructions, while informative, could also be improved. Thankfully the forum has been very helpful. Informative and motivating videos but where were the SwiftKey people mentioned ? Finally, the quizzes 2 and 3 should be replaced by other exercises with more educational value. Overall an interesting learning opportunity !
par Zaman F•
Aug 24, 2017
Most of the courses were very well tought and contained useful material.
Thanks to all three instructors
par Jay B•
Oct 04, 2016
This is not for beginners with no experience. The estimated weekly hours are absurdly low.
No one has seen any sign whatsoever of the industry partner, SwiftKey, despite claims they will be around to help. The field has advanced dramatically since the course was developed. Be prepared to do a lot of research and trial and error.
The specialization has been an excellent way to learn a fair amount on the topic, but it is just the beginning. The capstone will challenge you. It is rewarding when you complete it.
par Victoria A•
Mar 29, 2017
With this course I learned to go through a data problem from the scratch and get a real data product, and document it. My only constructive comment is that, when reviewing the projects of classmates, there is a huge dispersion on the effort and quality of the products presented, from very basic and simple Apps to a very professional products, and the scoring of them all is quite the same, perhaps one or two points of difference, in eleven points maximum score.
par Murray S•
Oct 09, 2016
Good test of what we learned in the courses.
par HIN-WENG W•
Aug 27, 2017
Challenging real life project that apply the academic knowledge
par Kalyan S M•
Nov 06, 2016
Really great course to apply all the techniques learned earlier in the specialization.
par Josh M•
Oct 12, 2016
Good scenario and a good learning opportunity. I don't think the quizzes related well to the problem we were trying to solve and introduced a red herring, however. Predicting the next best word is not the same as predicting the relative probability of 4 words where one is the "right answer" but not necessarily the best prediction of a text prediction algorithm.
par Marcus S•
Sep 20, 2016
A good & fun idea to implement. Would have prefered implementing my own idea though.
par Gary B•
Sep 15, 2017
tough capstone and took a lot of time
par Sandeep A•
Sep 13, 2017
Very good Course as a beginner course for Data science , you will learn a lot of stuff and the capstone is a very good starter for Natural Language processing
par 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.
par 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.
par 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.