Overall this was excellent track. While there was a difference in level of difficulty between the individual courses, it is probably unavoidable given the range of subject areas.

I think it would be great improvement if there was a additional 'post-grad' 'course'-like few weeks to connect to industry that is hiring from this background and get those connections to lead the 'grads' into real job interviews; Also, more projects that are direct connection to the industry, like the capstone - where those project would be dine perhaps in some kind of cooperation with the industry reps, so that graduate student here has direct path and had already worked with people that might hire him/her, where the time spent working on the capstone project includes meeting with the reps from the industry whom would have interest in the work. Something along the lines of grants for university projects (not talking about money here) but of a connection to the needs of the industry. Students working on that if they deliver good and interesting results would have one foot into the new job. This would also allow for higher fees to be charged for the classes since there would be more tangible 'selling' path.

This course is unlike all the others. Although you will need information gained in the previous nine modules, the Capstone Project requires you to work on a long and difficult problem using your own initiative. Mentors, tutors and Swiftkey employees are lacking throughout this project.

I worked through many different R packages to generate the word prediction N-Grams because R has a tendency to run out of memory. Many students are forced to use a cut down version of the three million lines of text because of memory issues but I managed to find the proverbially needle in the R packages haystack that allowed me to use the entire dataset!

I had problems with publishing the presentation to RPubs - it just would not work using either RStudio or RConsole but at least I had a fall back position of placing the presentation on my own website.

It took me three attempts to complete this project, nine months (Jan-Sep 2016) and about 300 hours in total, I didn't give up so nor should you, you can do it! And Good Luck! Hope to chat with you on the Data Science Specialism LinkedIn Group for Completers!

Finally was it worth paying for all of the certificates. Yes, it was!

Really liked this overall course. I was able to get directly into data science aside from my job (quantitative analyst). This specialisation helped me makeing my way in quantitative finance with much more understanding in computing models; much more confidence in the way I will face (I am facing) datas/algorithm issues. Really struggled with the last course(capstone) I even sometime wanted to give up as I went really deep in NLP and was facing issues with my memory.

Finally I'm getting out with strenght, smile, confidence and the taste of hard work in data science projects.

Some other really important point is to learn to be humble :) . This capstone project shows us enough how far it's a constant work to be a data scientist.

Really glad to have completed all the courses; going from zero on R to near hero :)

Although this course was the most complicated part, it was a really good experience in implementing our understanding and try to develop a practical product. I really like the approach of providing a data product that is presentable to the other community other than data specialist. I will refer to the course content from time to time in the future. I would recommend the course set to my colleagues if they have interest on data science.

This course significantly challenged my skills in programming, probability, machine learning and applied mathematics (eg Katz's backoff theory-equations). The collaboration in the discussion forums and the information on-line is absolutely critical and is the only way you can succeed in this project. I appreciate all the help from my classmates and from those who took the time to post helpful information on-line.

One step at a time. Very confused in the beginning. Gradually understood and learned. Then, built something amazing (in my own standard). Finally, all hard works pay off. The techniques learned in this capstone and previous courses have benefitted me substantially. I have applied some of them in my works as auditors, and capstone provides me another tool to assess customer feedbacks in large scale.

I've read a few negative reviews, saying it's not guided enough etc. I actually enjoyed finding out on my own how to tackle the problem and building a solution on my own. Also, the peer-reviews assignment was of a much higher quality, and with no plagiarism, with interesting remarks, it was nice to see other student's work and approach. I enjoyed this course, and the entire specialization!

The task is really hard, but it should be. You are a data scientist now, be ready to deal with new analyses and new topics. It's a bit tough since topic in NLP and we haven't discussed much that in previous courses, but you will learn something new and apply the knowledge you gained in the specialization. Thank you Brian, Jeff and Roger for making this specialization.

The capstone project was a good way to analyze and solve a more complex problem with some structure provided. It would have been nice to have had a machine learning component as well, but that would have likely made the course even longer and more difficult to grade. This capstone project did give me a data product that I have already demonstrated in an interview.

This project was somewhat challenging, yet relevant with what it came before it. Completion of all the ten courses were so much fun and definitely better than wasting money on a traditional education. I've learn way more from online educational platform, in comparison with the traditional universities/colleges that I have attended. Thank you, this was so much fun.

Amazing course! The course really tested me and pushed me to move past my limits to achieve something remarkable. I highly advise this course for anyone looking to pick up data science skills in R programming language or even just the data science skills

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!!!

This course was great. I went from having a decent grasp on statistics and a little knowledge of software like SPSS to being employed as a data analyst where most of my job is using R.

Well-paced, highly structured Capstone that allowed me to put to the test the skills I honed during the 9 previous courses in the JHU Data Science specialization. Strongly recommended.

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.

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.

The main reason for my rating is because the course is so "loose" on what your are supposed to achieve incrementally every week that it can lead to some hard situations.

Just to give my example: the first week was piece of cake and I didn't feel like it really contribute for the following weeks. Then, I was struggling with the suggested library (tm) until I got support through the discussion forums and someone suggested me to use quanteda.

Then thinks started to run smoothly, or so I thought. When implementing the language model (which, at first, I thought was supposed to be KBO), I got stuck for a long period. Not because my model was wrong (I was able to implement it and to check it against some hand-written and proved examples - which I should probably thank again), but because I was not able to make it run efficiently enough for the given constraints.

Being stuck in this stage for longer than I wanted, I had to sacrifice another important steps of data analysis pipeline in order to not jeopardize my final delivery by not meeting the final due date. I know that this is exactly what will happen in the "real" life, but I think that some better guidance could guarantee the students spent a more even amount of time in across all steps.

All things considered, I think the Capstone was really interesting and likely took more than the 4-9 hours per week, but most of this is probably because of the problems I faced.

I believe that with a better guidance on the paths to follow or maybe some suggested libraries to use, a lot of "noise" (useless difficulty) could be removed and this course would definitely get more starts.

A capstone is typically defined as integrating key material from a course. This capstone did not require material from key courses, specifically the machine learning, regression models, and statistical inference courses. That was a great shame. Instead, it threw us into a completely new area, Natural Language Processing.

There were many complaints about that, and I agree. However, it was a challenging task to explore an area in data science we didn't touch on, and challenging in terms of the programming and enormous data file sizes. In that sense it was probably good prep for unexpected challenges in the workplace and therefore good training to make us real data scientists. Still, I would like to see the capstone rejigged to include material from the missing courses. As for NLP, some students claim it is not a useful area to study, but in my case it is exactly the right thing for me to study as I work with analyzing user queries in the form of tickets in a CRM. I found it especially trying to try to integrate some material such as Kneser-Ney theory and opted for a more basic approach. My learning experience would have been better with some proper instruction in that area.

I felt this course was the weakest of the series. The capstone focuses on building an NLP application, which although I find interesting, does not make for a good final problem as NLP was not really covered in the specialization and NLP is particularly challenging in R. That said, the series as a whole is well worth the time and effort.

I would say, if we could introduce a capstone project more related to the first

The amount of background knowledge you need in order to get through this course is astonishing compared to the amount of knowledge you gain from the other 9 courses in the specialization. The first two tasks are really easy. The third one (creating a model) is ridiculous. It's nothing like the models we built in the modeling course. The number of people who have given up at that point and dropped is absurdly high. I spent a month and a half trying to get enough background to get a working model up and running and eventually just decided it's not worth it for just a Coursera certificate. No offense, but this is more like a final project for a master's degree.

I understand that the idea is to push people to be able to deal with different kinds of data, but you give us all the tools we need for a specific kind of data and then drop a completely different kind of data from a completely different field, and NONE of the modeling techniques or even the stats actually apply to this new type of data.

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.

The course is really just a structure for the final project. Most learning and programming techniques for the capstone are self taught and require intense research and experimentation. This entire certificate is more or less in the same vein. Only attempt this if you are confident of your skills as a self directed learner. Overall I found most of the courses to be disappointing in the series though I did finish.

Class offers nothing more to the previous 9 courses. The curators of the course seem to have given up at this point, basically telling us to do something on our own (to be graded amongst ourselves).

I wish the course trained you to do anything with the capstone. Unfortunately the course teaches you how to build a bike and then the capstone asks for you to build a car.