Avis et commentaires pour l'étudiant pour Applied Data Science Capstone par IBM

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!

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'.

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.

Quizzes are poorly designed. Evaluates only memorized information.

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.

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.

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.

A must take capstone project. Enroll for it and you will be moved by the project... Very interesting !

Very easy to understand and remember this material

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.

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.

Felt a bit constrained by the requirement to include the Foursquare API.

This is really challenging course, especially that you get hint on how to use a RESTful API (of Foursquare), how to create heat maps, or create different marking on a map using folium. The Capstone was really challenging, because you can practice what you have learned during the courses of the specialization, like how to start from the scratch a project, how to apply the data science methodology, like business understanding, gathering, analyzing, and cleaning data (most of your time you will spend on this), applying the right machine learning algorithm to solve the problem (modeling), using Jupyter Notebook on IBM cloud and using github. In the end you should also prepare your final report including the business understanding, describing your data, presenting your result, and placing a discussion section in the end. It took me 4 full days to complete the capstone, but I learned a lot.

Very Good. This is my first contact with data science with python and associated packages.

In the end of the course I'm able to deal with data using python and a lot of tools that helps the job and let this job more fun.

A very well organized and balanced course with videos and very good material for practical labs.

I have a Swisse Knife with me to deal with future researches on data science.

Thanks.

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

Did not like Foursquare and was obliged to use it to complete the course

Some things were outdated and did not work properly for me.

Peer-graded assignments where one has to create a github repository is totally unnecessary.

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 course did not provide enough learning materials for students to complete the project. For example, it asks students to scrape a web page and parse the table on the website and put it into a pandas data frame in a Jupiter notebook.

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)

Amazing :)

This was a really cool project that really applies everything I learned during the specialization.