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!!!
By Diego C G•
Apr 13, 2016
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!
By 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.
By Max D•
Aug 19, 2019
NLP module should definitely be included into JHU Data Science specialization.
By Sevdalena L•
Dec 10, 2016
Not enough information on how to approach the final project. The project itself is very time consuming with lots of self learning and unclear specifications.
By 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.
By 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'
By 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.
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.
By 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.
By 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.
By 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.
Mar 23, 2016
need more details
By 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.
By 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.
By 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.
By 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.
By 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.
By 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.
By 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!
By Runhao Z•
Nov 28, 2017
By 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.
By Stephen E•
Jun 27, 2016
A poor end to a poor Coursera specializations.
By 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.