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!!!
von Jean-Philippe M•
Dec 15, 2019
I wish we had more training on text mining. Was not that easy! But overall the program is very interesting and challenging! Enjoyed it a lot!
von Alex s•
Apr 12, 2020
The project is really interesting by itself, but there is a lack of preparation and instructions to build it, basically you are on your own.
von 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.
von Humberto R•
Apr 09, 2018
Very instructive, since it presents you with a real world problem, that you need to solve by yourself, in all of its complexity.
von 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.
von xuanru s•
Jun 20, 2017
Very challenge work. new topic. The only issue is if there is any videos that could guide us would be better.
von Zaman F•
Aug 24, 2017
Most of the courses were very well tought and contained useful material.
Thanks to all three instructors
von Kalyan S M•
Nov 06, 2016
Really great course to apply all the techniques learned earlier in the specialization.
von Marcus S•
Sep 20, 2016
A good & fun idea to implement. Would have prefered implementing my own idea though.
von Rudolf E•
Jun 20, 2017
Great course, great content, didn't like the final capstone project though.
von Emi H•
Jun 22, 2017
Good project. Got me to think outside the box and really challenge myself.
von HIN-WENG W•
Aug 27, 2017
Challenging real life project that apply the academic knowledge
von Greig R•
Mar 16, 2018
A tricky end to the specialisation - but quite a lot of fun.
von Chonlatit P•
Jun 27, 2019
Project is good for practice what you've learnt
von Murray S•
Oct 09, 2016
Good test of what we learned in the courses.
von Ajay K P•
Mar 30, 2018
I really had fun working on this project.
von Artem V•
Sep 14, 2017
Nice balance of focused and open-ended
von Gary B•
Sep 15, 2017
tough capstone and took a lot of time
von Yew C C•
Jul 20, 2016
Good and interesting project.
von Tiberiu D O•
Sep 22, 2017
von Sabawoon S•
Nov 25, 2017
von Filipe R•
Oct 07, 2018
von Kevin M•
Jan 15, 2018
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 David M•
Jul 21, 2016
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.