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: 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.
교육 기관: HIN-WENG W•
Aug 27, 2017
Challenging real life project that apply the academic knowledge
교육 기관: Artem V•
Sep 14, 2017
Nice balance of focused and open-ended
교육 기관: Angela J•
Apr 17, 2018
Overall, I was semi-satisfied with the capstone project:
On the negative side, my foremost issue is that the project has very little to do with what we learned in the nine courses before. I get that you will always see new data formats as a data scientist, but having the whole course cover numeric data and then having the final project be on text data where you can't apply what you learned seems sub-optimal. Also, to me it seemed that the accuracy increased mostly with how much data you train your algorithms on, and not so much how you design your algorithm. My second issue is that the class only starts every two months, and the assignments are blocked before the session starts so you can't see them if you're trying to get a head start. What happened to everyone learning at their own pace? I have a lot to do and had to switch sessions at least once for most classes, and this class was really stressful for me because I didn't want to move my completion back by two months. Lastly, I really hate RPresenter and that the instructors force us to use it, but maybe that's just me.
On the positive side, I did learn a lot: The basics of text prediction, how to do parallel programming in R and how to set up an RStudio instance on AWS (the latter two are not very hard, I recommend them to anyone struggling with gigantic runtimes, as long as you're willing to invest like $40 or so for the computing power). I liked that the guidelines were very broad, so there was a lot of room for creativity. I also finally found out how to make an pretty(-ish) presentation in R, though I would always choose Powerpoint in real life.
I really enjoyed the series as a whole and learned a great deal.
교육 기관: Robert C•
Aug 03, 2018
I wish that either there were a choice of capstone projects, or that there were a more numerical component to the analysis than such a pure text based assignment.
교육 기관: Josh M•
Oct 12, 2016
Good scenario and a good learning opportunity. I don't think the quizzes related well to the problem we were trying to solve and introduced a red herring, however. Predicting the next best word is not the same as predicting the relative probability of 4 words where one is the "right answer" but not necessarily the best prediction of a text prediction algorithm.
교육 기관: Marcus S•
Sep 20, 2016
A good & fun idea to implement. Would have prefered implementing my own idea though.
교육 기관: Sandeep A•
Sep 13, 2017
Very good Course as a beginner course for Data science , you will learn a lot of stuff and the capstone is a very good starter for Natural Language processing
교육 기관: Gary B•
Sep 15, 2017
tough capstone and took a lot of time
교육 기관: 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.
교육 기관: Dwayne D•
Sep 02, 2017
Completion of this project requires most (all?) of the skills you will have learned in completing the prerequisite courses. If you've worked to ensure you truly understand the concepts, tools and techniques presented in the prerequisite courses, you will be able to complete this project. The problem domain is a little different from most of the examples in the prerequisite courses. I find that a good thing. Whenever I learn something I believe to be useful, I always wonder how it applies in other contexts. This course was an exercise in doing just that — applying what you've learned to a "new" (i.e., new to me) a domain.
Heads up / Be aware: If you're "like me" — inexperienced with NLP, and one of those people who doesn't feel quite right about using a recommended toolset or algorithm until I understand why it's the right tool for the job — you should start reading up on the basics of text mining, NLP and next-word prediction models 1-2 weeks before you start the course. For some, that might be overkill; but I'm a slow reader at the end of a workday (we all have day jobs, right!?). Given this foundational understanding, I felt comfortable making tradeoffs among the state-of-the-art and the practical, given the project objectives, my own time constraints, etc. Reading the course forums and reviews, I think some who had trouble completing the project weren't able to take sufficient time to get oriented with this domain before attempting to build their first word prediction model.
Note: By "foundational", I mean enough to intuitively grasp why what's accepted as best practice is that. When I've read about someone's approach to solving a problem, and I'm able to say "makes sense, but I probably don't need to do X or Y to meet the need for this effort", then that's often enough… But :-) because I at times overthink things (don't we all!), I get a little more comfortable when I at least skim over descriptions of how a couple others have solved a similar problem; and I can see patterns of convergence… I do NOT mean enough to write your own thesis, unless that's what you really want to do. Whatever floats your boat! LOL
I have a software development background (and completed the previous courses in the specialization), so translating approaches I found described in various sources into code wasn't "easy"; but it wasn't a barrier, either. I was helped along GREATLY by the existence of R packages such as tm and tokenizers, and I was always able to find guidance on addressing thorny issues via "good ole Google Search". Most often, my searches would lead me to StackOverflow or write-ups from capstone project alumni. While I did my own write-ups and wrote my own code, I benefited in a big way from lessons learned by others who've already tackled similar problems.
I would recommend the Data Science Specialization by JHSU, which (as it should be) is a package deal with the capstone project. Applying what I learned to a new domain really solidified my understanding and has whet my appetite for the next challenge.
교육 기관: Yew C C•
Jul 20, 2016
Good and interesting project.
교육 기관: Michal S•
Mar 03, 2018
The course project was very interesting. It can be challenging if you want to do it properly or easy if you just want to pass. I tried to do it properly for which I had to repeat the course 3 times, but in the end it was good - I think I learned a lot.
교육 기관: Emi H•
Jun 22, 2017
Good project. Got me to think outside the box and really challenge myself.
교육 기관: Juan M•
Jun 08, 2016
This is a great capstone project. It requires the student to really have an understanding of the concepts learned throughout the specialization and apply them to build a prediction app. There is very little guidance asides from the discussion forum which could be discouraging at first. Otherwise I would've rated it 5 stars.
교육 기관: Wesley E•
Aug 11, 2016
Overall a good course that makes you learn a lot on your own (unlike the rest of the series). Maybe a bit too much self learning. However, if you can complete it does give you a lot of learning especially in some text analysis which hasn't been covered before.
교육 기관: Yong-Meng G•
Jun 20, 2017
The capstone will be much easier if participants have hands-on experience and understanding of how R works. For those who have managed to overcome the steep learning curve, the reward from the learning experience is well worth it.
교육 기관: Yoga A Y•
Oct 11, 2016
The Capstone Project makes you summarizes what you have learnt so far and take it to the next level, natural language processing . Besides, the ability to create a working app is a reward by itself.
It is challenging and interesting at the same time.
교육 기관: Greig R•
Mar 16, 2018
A tricky end to the specialisation - but quite a lot of fun.
교육 기관: shashank s•
Sep 16, 2017
It was a challenging project and really pushes you to learn and manage on your own. It also pushes you to build and end to end product within time and memory constraints. Learned a lot during this project!!
교육 기관: Chonlatit P•
Jun 27, 2019
Project is good for practice what you've learnt
교육 기관: Neeraj A•
Sep 08, 2019
Feeling proud after completing all the courses under Data Science Specialization. This was not an easy task to complete especially if you are not familiar with the Statistics. Requires continuous dedication and motivation to follow and complete. Course is well designed and cover most of the topics. Its just stats part can be enhanced further to cover some basic aspects. Thanks for all the support
교육 기관: 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.
교육 기관: Antonio E C•
Dec 30, 2016
It's been a challenge to learn all these new concepts and package them into a working product in such a small period of time. I am glad of the things I learned. Also, in my opinion the materials / resources given to this course are scarce compared with previous courses of the specialization.
교육 기관: Pradnya C•
Apr 14, 2016
Most stressful but interesting. Not enough material was provided