Instead of only peers review, I think it would be better if someone professional also review our capstone project and gives us feedback

Had to provide a credit card for FourSquare which I did not appreciate

I was not too happy with the way peer grade assignment was done and it is being used as a mechanism to grade the course. My submissions were not reviewed correctly and because of that I felt that the course duration prolonged for one more month, and I had to pay my subscription for one more month for no reason.

Utilizes skills learned throughout previous courses and puts it all together in Capstone assignment. Found the instructions to be lacking but it is a Capstone so not totally unexpected. I did find some of the instructors comments in the forum to be somewhat unprofessional so maybe some coaching should be done on how to respond to students who pay to take the course; even those that frustrate you!

Capstone implies apply what you learned throughout the program. I appreciate that you were able to work in things you learned of your choice if you wanted to, but the sole requirement and focus of the capstone shouldn't be to include something that was just introduced. Also, the graded lab that isn't part of the final project cannot be completed with the knowledge provided by this course. I should NEVER receive instructions to check out more help on YouTube from a course for which I am paying. I will not recommend this course to anyone.

This a capstone project as part of my IBM Data Science professional certificate. The course was intended in my view as a recollection of the material and practices taught in the course all rolled into a nano project.

In my view the topic and objectives for this course were very loose but the main focus assumes to be foursquare API's which were not mentioned until this course. The topics were loose which meant issues encountered in data collection via scraping or visualization might not have been covered within the course.

For me it was a learning experience but I am not sure about the value of the certificate offered by IBM. For starters the grading is peer reviewed which of course means the grade are not quality controlled but a reflection of your peers grading abilities regardless of whether they would like to shoulder those responsibilities.

There is no proctor administered for graded examination or office spaces for internship like experience and assumes some higher power is reviewing your honor code and motivation.

As far my application with this certificate has not given me a technical advantage even within IBM and I am not sure if the third party career counseling offered via Coursera have a success story.

I enjoyed the course even though the course and its material very rough and marketing oriented. The course was supposed to provide me with a practical advantage in the area of Data Science as a new entrant and I have not observed any advantages so far with my applications on linked in.

Stay away. Instead of taking the time to teach the material requested in assignments it tells you to go to youtube and teach yourself. It's a pathetic finish to a certification course with such a prestigious company name attached to it.

I liked working on a project from beginning to end - finding a problem to solve, acquiring data, creating & testing hypotheses. It really puts what you've learned to test. I also learned some techniques that were not covered in the course and other skills like creating Medium posts.

I didn't like the review aspect. You only have to review 1 project and receive a review from 1 person which is not enough. I reviewed multiple submissions and found it very educational. The review criteria are vague and mostly cover just the presentation of results. They don't assess the quality of analysis itself (and it's difficult for an unexperienced person to do without guidelines). Getting feedback from more people would be interesting.

The discussion forums are not helpful since they're spammed with "Please review" threads - and the staff doesn't do anything about it. This applies to the whole specialization.

Utterly useless. It mandates a learner to work on geo data related project only.

And that too using a very specific api and data.

No option to work or submit meaningful capstone projects.

Quizzes are poorly designed. Evaluates only memorized information.

The only problem is that I ran out of hours using IBM watson and the same thing happened with Foursquare when I reached the maximum numerber of records per day and per hour.

We as students should have tools where we can make mistakes without reaching 'limits of usage'.

There should be a clear tutorial on how to scrap a website. The project should be more open and not tight to using Foursquare, and should not be reviewed by other students.

This was challenging and a project is always the only way to really learn anything and struggle through. I did however feel that the forum for this course was not useful and that the mentors/lecturer's let us down. They hardly reply to relevant questions leaving students to feel abandoned. With a project you need some support as many students are not 100% comfortable with the code. One of the other courses has the forum broken up into two separate forums: One for the tech questions and another for requests from students to review - this was clever and worked well. Im giving this 2 stars based on the support and lack of direction for the project. May have also been nice to get options for a dataset - not easy to find a dataset in the public domain (spend hours looking for good ones)

Mentors won't teach how to use foursquare API and how to apply different ML algorithms. As the certificate names professional Data Science there is no introduction or explanation about neural networks. A deployment part is needed.

I wrote this review after I finished all four courses of Applied Data Science Specialization. Overall speaking, the specialization is good and fairly easy (especially the first two courses). In terms of the Capstone Course, it looks intimidating but it won't be a big problem if you follow the materials closely. The final project is a great opportunity to be creative and to utilize all kinds of sources (and get to know the city you are interested in better). Nevertheless, I think the specialization should include more coding exercises/assignments instead of simple quizzes at the beginning. More hands-on exercises should be added to the introductory courses. Personally speaking, I'd like to get trained more on data cleaning and writing loops/functions.

The real advantage of completing this course goes far beyond learning the skills that data scientists use every day. The capstone project requires learners to integrate skills, along with domain knowledge of meaningful use cases.Then, with a significant goal in mind, plan the project and execute successfully for peer-review. I think this course comes very close to replicating the actual work products that data scientists do in the real world to a high degree. Of course, dealing with other individuals and project teams are not possible in this format. Having said all this, the real advantage of achieving the certificate is validating to oneself that the basic data science skill set has been mastered.

This course certainly made me put in the work!! The project requires alot of planning to figure out exactly what you want to focus your analysis on. It definitely forced me to do alot of self-learning in order to complete it. StackOverflow became my best friend when I would get stuck and not know the proper python syntax to execute my desired outcome. Having to create a report and blogpost to document my analysis were definitely two new skill sets I appreciated that this course helped me to learn.

Must take to complete this wonderful specialization.

You will have a change to apply everything you learned. And you have the freedom to choose the topic that you are interested in.

After this course, you will have a report, a blogpost and a notebook with complete code. With which you can showcase your achievement along the certificate.

Very good capstone project. Learnt lot of insights on how to represent data through out this course.

Very good starting point for ""Data Science" field. I would definitely recommend this course.

Think this one should have been marked by the course moderators and not fellow students.

The assignment is in and of itself nice, but it is too free. I would have liked a more restricted assignment. During the specialization we mostly had to watch how stuff was done, without much practice on our own, so the step to this assignment was quite large, especially if you are graded by your peers. We were also graded on stuff that was not part of the course, such as reports and presentations. I understand that this is important, but not the aim of the course. Furthermore, we were graded by peers, who are the same level. How can they grade a submission if they themselves do not yet know what is good and what is not?

The worst course by far in the 9 courses bundle

Please consider remove this course from the bundle

Started with no programming knowledge, took this while working a full time job, it was not always easy, but i am really glad that i took the decision to go out from my confort zone. Today i can handle topics like machine learning, data analysis and visualization with python. thanks for the IBM team who really has done an amazing job on this course.

If I have to say one thing about Coursera or IBM Data Science Professional Certificate course, I would say it as a Fantastic thing happened in my life, I am so happy with it, and I am not going to leave Coursera for ever.

Very difficult to manage the scope, but it is a self-learning process. Recommend extending the Capstone course another week or two, to encourage the students to go all in on their work.