Its was great experience in completing the project using all skills that we learned in the course, thanks to coursera and IBM for giving me an opportunity to update my selft and also to test my skills
Very good capstone project. Learnt lot of insights on how to represent data through out this course.\n\nVery good starting point for ""Data Science" field. I would definitely recommend this course.
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This is the final course in the IBM Data Science Certificate and it is primarily focused around a project of your choosing. First, you learn how to scrape data and use the Foursquare API, which is quite helpful as these skills are generally transferrable. Then you'll need to come up with an idea that is loosely related to location data in some way. You'll have several weeks to implement your idea and write a report and a blog post/presentation. The final project is a lot of work.
In my opinion, the grading system could be better. You rely on peer reviews to pass the course, but only one peer looks at your work. Multiple sets of eyes would be fairer and hopefully generate more feedback. The discussion forum aspect could also be improved to promote collaboration and not simply requests to "please review my submission".
All in all, a decent guided Capstone course. Be prepared to do a lot of work on your own as there is not a lot of structure or hand-holding. I am very proud to have completed a formal project/report that demonstrates how much I learned over the course of the IBM Data Science Certificate.
By surya m p•
This course is excellent at teaching all the data science and machine learning skills from a practitioner's perspective. I would strongly recommend it to aspiring data science professionals. Other positives include free introduction to the IBM cloud platform.
Room for improvement include:
1. Improvement to reliability/availability of IBM Developer Skills Network (which was done towards the end of my course) or give it a miss (using IBM cloud platform instead) completely.
2. Assignments should be graded by instructors or through standardised testing. The current peer-graded system seems to be hit and miss. It is not ideal especially for such a long course.
By Dominic M L C L•
There were quite a number of tools/apis in the course material that were no longer working, meaning they need to be updated and shows the course material has not been touched in quite some time. For absolute beginners this is problematic as they are not unsure where to search for solutions, and asking in the Discussion Forums does not always return an answer. Aside from that, I found the Capstone Project to indeed be challenging for the level of skills we have obtained from the course, but also figured it forces learners to really search and source for solutions similar to how the real world would force you to do so.
By Barry P•
I had higher hopes for this....The videos are excellent, the labs are pretty good. The problem with the labs is they will just dump the code in and expect you to know what it all means. I spent a lot of time googling what the code means for when I apply my own analysis. Begs the question of the value.
Lastly, the capstone was more of the same. A lot of digging on my part and not much help from the videos/labs. Also, many of the labs are outdated and you have to search the forums to find out something was deprecated and to use a new function. JUST UPDATE THE LAB!
By Rafael T•
The course is good. It makes you think on all the knowledge acquired during all other 8 courses and make you put in practice.
The only drawback in my opinion is that the course relies on an unreliable platform for Jupyter Notebooks. Several times I wanted to access my notebooks to continue with the course and got a lot "Bad gateway" problems and slow responses in general. It was frustrating because the best part of the course are on the notebooks.
By Jeff G•
Overall, a very good value. Introduces new topics in Data Science well. Although the Capstone is suitably challenging, I still feel as though there must be a different format to help solidify the coding syntax for python pandas so that the learner doesn't have to rely so much on referencing StackOverflow or previous lessons. Those portions of the code remain a lot of cut and paste rather than truly building the knowledge base.