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Learner Reviews & Feedback for Data Science Capstone by Johns Hopkins University

4.5
920 ratings
245 reviews

About the Course

The capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners....

Top reviews

NT

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.

SS

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!!!

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151 - 175 of 235 Reviews for Data Science Capstone

By Glener D M

Sep 19, 2019

Indispensable for those who want to master the R language along with data science. I highly recommend this 10 courses. Believe me you will not regret it.

By Manuel E

Nov 03, 2019

Hard, extremely satisfying.

By Benjamin M

Nov 03, 2019

Very nice course and there was a lot to learn. Awesome!!!

By Khalid S A

Nov 05, 2019

Great Course, great experience

By Alia E

Sep 05, 2019

Like diving in without learning to swim first - but man did I learn a lot.

By Joshua M

Sep 08, 2019

Very engaging and well-designed course. Many thanks to the instructors and SwiftKey!

By Ahmed Z

Oct 03, 2019

Great Course

By Charbel L

Oct 07, 2019

Interesting case study.

By Ekaterina S

Sep 26, 2019

It was a challegne for me, but also a fun!

Very interesting experience in a new promising field!

By Carlos R S D

Nov 19, 2019

I took this specialization a couple of months ago and did not comment as such. Now I turned around to remember some topics and started reading comments.

I found many comments that say the final project has nothing to do with the previous 9 courses and when I did it I thought the same.

Looking at it in perspective, I think the previous courses are absolutely necessary for the final project. The objective of carrying out a project with such characteristics is to apply the knowledge by oneself.

The first courses of programming in R, extraction and cleaning, and exploratory analysis are fundamental to understand the problem. In this case the cleaning has to do with the transformations using regular expressions and tokenization. The exploratory analysis should be done in any data science project, otherwise you may encounter surprises when implementing the models.

Statistical inference was necessary and closely linked to exploratory analysis, especially to select samples well and review distributions, since some machine learning methods may be affected by distributions. I must say that I did not see this when I took this course, but it was because of my lack of experience. Maybe there was a lack of guidance.

The algorithm I used was regression on the ngrams for simplicity, time and capacity of my computer, but it could have been combined with other methods such as neural networks or svm.

Implementing the model in shiny and then adjusting it because it was very heavy was also interesting.

As a summary, I really liked this specialization and although it was very hard and many times I did not know how to move forward (especially in the capstone), I think the challenge was important for my learning and I was very entertained.

By Muhammad Z H

Oct 21, 2019

Thanks Professor

By Elimane N

Nov 28, 2019

Thank you very much for this course

helpful

By Jeremi S

Dec 07, 2018

Challenging. The course could possibly offer a 'here's how it could be done' ideal example after final submission and pass.

By Kalyan S M

Nov 06, 2016

Really great course to apply all the techniques learned earlier in the specialization.

By 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.

By Marcus S

Sep 20, 2016

A good & fun idea to implement. Would have prefered implementing my own idea though.

By Kevin M

Jan 15, 2018

Very hard!

By Tiberiu D O

Sep 22, 2017

Interesting assignment!

By Artem V

Sep 14, 2017

Nice balance of focused and open-ended

By Angela W

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.

By Gary B

Sep 15, 2017

tough capstone and took a lot of time

By 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

By 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.

By 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.

By 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.