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 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.
par 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.
par Guilherme B D J•
Mar 24, 2017
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.
par John D M•
Sep 20, 2019
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.
par Andrew S•
Jun 26, 2017
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.
par 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.
par John K•
Jan 31, 2020
Poorly defined, and the course sets the student up to use the wrong tools.
par Zoran K•
Jun 19, 2017
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.
par MEKIE Y R K•
Mar 08, 2020
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 :)
par Zhen ( W•
May 13, 2016
I had no experience in natural language processing before I took this course, and now I'm kind of in love with it! Some of my fellow learners complained about the new data type and little information provided, but I feel this is a good simulation of real world experience as a data scientist! The field is constantly changing, so we have to be ready to cope with unfamiliar problems and come up with creative solutions. Due to other commitments, I was once 3 weeks behind the weekly deadlines, but finally poured all my efforts into this and deployed an App in time... You never know how much you can accomplish before you are forced to do a "Mission Impossible" ;-) I think I've improved my hacking + googling skills, and built more confidence over completion of this course. Thank you, JHU and Coursera!
par Lucas S T•
Jun 27, 2020
The idea of the final project is superb! It really pushes you to your limit, but giving resources through the way so you don't get lost, which is certainly pretty easy.
This course differs a lot from the previous ones because you are pretty much on your own, with only guidelines and references on what they suggest you should do.
The intention is clearly to mimic real life projects, where you have a basic goal, your and others expectations, and knowledge PLUS hardware limitations. And you will have to overcome it in order to be a good Data Scientist!
I really recommend this specialization!
par Md A I•
Oct 24, 2020
This specialization was like a box of chocolate. I at first thought it was easy, then found the difficulties and struggled a bit. Then again I urged myself to finish this slowly which was a good decision ultimately. At present, I am now relating the things I learnt with my study in the field of Economics. Thank you very much for organizing this specialization. This has been a great experience for me. Gracious!
par ONG P S•
Oct 03, 2020
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.
par Olivia U•
Jun 21, 2020
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!
par Jose A R N•
Jan 20, 2017
My name is Jose Antonio. I am looking for a new Data Scientist career (https://www.linkedin.com/in/joseantonio11)
I did this specialization to get new knowledge about Data Science and better understand the technology and your practical applications.
The course was excellent and the classes well taught by teachers.
Congratulations to Coursera team and Instructors.
par Eric R•
Apr 03, 2017
For me this project was harder than all other courses combined but, because of that, also more rewarding! The theory is very scarce so you're on your own, that's what makes it hard. Once you get the theory right the rest is easy. I learned a lot of NLP and let me practice "Pitchs" using diferent R tools.
par Desiré D W•
Sep 25, 2016
The Capstone starts well with sufficient guidance. The second part gives much freedom however, it might be overwhelming and unclear at times what to do next. However, I loved going out on my own and slowly learning more and more on the subject.
You can pretty much choose how much effort you put into it.
par Rongbin Y•
Jun 04, 2020
Great learning experiences with multiple meaningful projects and lessons. The whole concentration has been well-designed and well-founded. I had built a solid foundation of understanding for subject. Thank you for teaching this concentration, Professor Chen， Professor Leek and Professor Caffo.
Aug 24, 2018
In this last module I have learned a lot. It was demanding and quite tricky as you were asked to take your own decisions as there is no best answer at all. I learned to decide what I want and to create an appropriate solution. The best lesson so far along this specialization track.
par Nirav D•
Jul 03, 2016
I loved doing the capstone project for the Data Science specialisation. I applied all the skills I learnt during the length of the specialisation on Coursera. Having completed this project, I feel more confident about my skills as a data scientist in solving real world problems.
par Alma S•
Jun 20, 2017
Really challenging but satisfying enough!
Thank you for Cousera team who patiently developed such a beautiful program for upskilling us, the so-called data scientist! :)
The journey to accomplish this Data Science Capstone is something I'd remember & cherish, indeed.
par Parmida B•
Sep 12, 2017
Awesome specialization! Super happy to be done with 100% on all the courses and 95% on the capstone. I would love to be a part of this great team, maybe as a mentor. Thank you to all the instructors for great lectures and to mentors who helped with the forums.
par Akthem R•
Mar 18, 2017
A very stimulating and challenging capstone. It is stretching and puts all the 9 specialization courses material to use. It also gives the student a glimpse of what Data Science in real life is and touches on Natural Language Processing as part of AI.
par Shreeram I•
Jun 26, 2016
The sequence of activities in execution of the project envisages multiple interactions with your peers and unfolds your creative aspect to churn out a solution to put all the learning into practice!
Cheers, DSS team - Brian, Jeff and Roger 😁
par Javier A D•
Oct 11, 2018
It was a new world for me. To hard trying to dive in the subject. But the bases and the effort to research in literature and in the foros let me develop a model of a beginner but with great knowledge to apply in new developments in my work.