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!!!
교육 기관: Marcio G•
Aug 21, 2017
The whole specialization is a bit of a mixed bag... Many of the courses rely too heavily on teaching R programming and not sufficiently on data science concepts (such statistics or machine learning). The instructors (specially Peng) spent way too much time detailing R syntax that could have been picked up by the students on their own from other resources available on the web...
The regression models and statistical inference courses are exceptions though: Together with the machine learning course, these are probably the most useful from the whole specialization.
The materials in this capstone project are way sloppier than materials in other courses by the way. They lack structure and feel confusing. I'm not even sure if the instructors tried to implement the proposed project themselves to have a base of reference. Feels like they were already growing tired of the whole thing and put the capstone project together in a hurry without much thought or care.
The theme of the project is indeed interesting (text-mining and NLP), but I think that would have been more productive for me to take a NLP course instead. You are going to use very little from what you have learned from the other courses in the specialization (for the most part the data product course) and you will need to learn text-mining and NLP from scratch on your own to complete the capstone (no videos nor materials available in the course on these subjects).
Also, if I was going to implement the same app on my own these days, I would probably use RNNs, not Katz Back-off and Markov Transition Matrices as in the capstone and I would probably use SparkR. Heck, I might not even use R, probably Scala or Python with Spark instead. In short, data science moves fast and this course already feels very outdated...
The instructors seem quite experienced in statistical analysis, so it's a shame that they decided to focus so heavily on R programming instead... That would have made the specialization more resilient to technological innovations in the field...
The specialization surely could be improved and these issues corrected, but all courses seem pretty much abandoned by the instructors. Most of the courses still have active "mentors" (volunteers not associated with Coursera nor Johns Hopkins) , but "mentors" seem to have lost contact with the instructors: For example, a couple of assignments require data that is no longer available (dead links) and "mentors" have provided this data in the discussion fora. I reckon that if "mentors" could contact instructors, the dead links would have been fixed in the materials by now...
The peer-grading doesn't work so well... Most of the submissions I graded were painful to review (extremely low quality). Not surprisingly, the graders were also pretty low-skilled. They can't even understand the requirements (and I suspect not even the English language) and they will take points from correct submissions.
I urge any employers to look at the actual code for this capstone from candidates given the general incompetence and poor skills of the students I graded. The grading criteria is pretty relaxed, so even though I would like to fail them, I still had to give them a passing grade. Such a weak grading criteria is detrimental to all people who actually have the skills and put hard work on their submissions. Many undeserving people will, unfortunately, pass and receive a certificate.
교육 기관: Jose A V C•
Apr 16, 2016
Very disappointed with this final course. Little to no support. Discussion Forum provides some level of help but you are basically on your own.
Very challenging to come up to speed with Natural Language Processing techniques if you have never taken any class about it.
My recommendation to JHU and Coursera is to add a separate course for NLP where you cover all the basics and then have the Capstone.
교육 기관: Thej K R•
Jul 31, 2019
I spent 80 hrs on this course. I hated so many things. 1. There was lot of uncertainty in the course. For example we didn't know how far to go with NLP. And I constantly came across in the forum where people were complaining about how there was 0 guidance and had no idea what to do. Saviours were those few people who put up help posts on the forum and sharded thier trecherous experience going down different paths. 3. The topic was already hard enough NLP, something I had no clue about and then there was this additional problem all the fucing time about memory. Jesus! One of the most painful courses primarily due to overload, lack of clear instructions and their refusal to edit one letter in the course since 5 years! Fuck them!
교육 기관: Piyush V•
Mar 26, 2018
On the Capstone Course, those who are reading this review I would say, skip everything (videos) and directly start writing codes and building the app. Otherwise this course is somewhat unnecessarily stretched too much, it could have been cut way short. I will tell you what I did: I skipped everything, got the gist of the objective, scanned through the codes and worked on my idea.
I started the specialization in December of 2015 and I am ending it today, March of 2018. I remember struggling with R in the beginning (I was a novice programmer writing dirty codes). Now I can't stop thinking about plethora of data product opportunities surrounding me.
교육 기관: Paul R•
Mar 22, 2019
The project topic itself is interesting, but longer (structured as 7 weeks); not much guidance until you find the right threads from mentors in the discussion forum from a few years ago or repeatedly google stackoverflow; it is much more technical than the rest of the course; and doesn't really use much of what was learned during the meat of the specialization's statistics/regression/ML courses, other than data science principles and tools (though new R libraries were needed). These issues aside, the project was an interesting challenge to complete nonetheless. Overall this specialization is now a few years old, and the plethora of 4 and 5 star reviews across all courses seem generous and out-dated. Materials are not being updated, forums are a mess of years-old threads with not much current activity; there is a feeling of waning interest and participation. This was clearly cutting edge material and course back in 2014-6, if JH/Coursera intend to continue offering it, the material needs some refresh and reordering, tougher grading rubrics (I saw a lot of inconsistency and poor quality which met the rubric criteria, alongside great quality work), and more active involvement from lecturers and mentors (and, please fix the typos).
교육 기관: Roberto G•
Dec 02, 2017
This class is challenging and a lot of people complained so I'll tell you my approach since I was able to complete it on the first try in my free time from my full time job. Not having any knowledge of Natural Language Programming, I found Youtube videos and presentations from the Stanford class taught by Dan Jurafsky and Christopher Manning. Study it up to the explanation of n-grams, it should be enough for the class. I completed the first weeks in few days so I had more time to actually build the model and the app (you'll need more than the scheduled weeks if you have no prior experience). I found valuable resources in the course forum. Then you're pretty much on your own, identify the best packages, how to use them, look on Stack Overflow when you get stuck. Start using a very small set of data so you can quickly build the model and the app until you get something that works. After that you can improve the model by using more data, finding the balance between processing time, app time response and prediction accuracy. Everyone understands the limitation of the project so give importance to quickness rather than accuracy.
My overall evaluation of the project is a mixed bag. The positive is that it introduces you to a new topic (NLP) and the goal is reasonable, it takes a lot of effort but it's not impossible and it forces you to learn something meaningful (something easier would have not made me learn something valuable). The negative is that there is no explanation whatsoever about NLP, which was never mentioned in the previous courses, so there's not much teaching or guidance. The involvement of Swiftkey is limited to providing the data.
교육 기관: Wenjing L•
Apr 26, 2019
The final project is interesting. Text input prediction is a very flexible topic. It could be deep, or simple. I hope in the future more practical models will be introduced during the course. Now we are asked to explore it almost solely by ourselves, which usually isn't the case at work, where one would seldom have to research on or develop something from scratch. Also I hope it will focus more on data analysis and visualization than developing an actual app. Shiny is a good tool to do interactive plotting, but not handy enough for UI development. I believe most people will never be asked to develop UI in Shiny at work. Finally I'd like to thank all the instructors who designed and delivered these 10 Data Science courses. I have learnt a lot from them.
교육 기관: 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.
교육 기관: 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.
교육 기관: 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!
교육 기관: Saurabh Y•
Nov 19, 2018
Good project. One can learn a lot ,doing this project
교육 기관: Ryan J•
Dec 07, 2018
Great course and fun project!
교육 기관: Shubham B•
Dec 12, 2018
Great for Beginners
교육 기관: Vinod•
Nov 21, 2018
Very well structured. Recommend! Learnt a lot!
교육 기관: Subha G•
Nov 21, 2018
It was really helpful
교육 기관: Luis F P A•
Jan 08, 2019
Challenging, you are almost left alone by yourself, but i cannot deny that i learned a lot by doing it
교육 기관: Samuel Q•
Jan 21, 2019
Great way to end the specialization because it forces students to think on their own and be resourceful. It is a totally different type of analysis than on any previous course so it was a great learning experience
교육 기관: Raunak S•
Feb 14, 2019
a very good concluding course for those have learnt the fundamentals of Data Science.
교육 기관: Keidzh S•
Feb 17, 2019
Brilliant course, the final chapter for the data science specialization. Spent lot of time making my final project, but it wirth it. Glad that I found this specialization a year ago.
교육 기관: Efejiro A•
Feb 23, 2019
교육 기관: Mohammad A•
Mar 15, 2019
This specialization was great and its capstone project was challenging and awesome.
교육 기관: Prabhakar B•
Jan 15, 2019
교육 기관: Javier E S•
Dec 02, 2018
교육 기관: Praveen S•
Dec 01, 2018
Amazing course for Data Science Enthusiasts
교육 기관: Praveen S•
Jul 29, 2018
The Capstone Project was very informative