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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.

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.

>>Positive things:

-As well as in the other courses, the material is of high quality and the labs to practice are enough to get a good understanding of the core topics.

-In this last course, the knowledge of the other 9 courses was clearly applied. One can call it a very good recap course.

>>Point to improve:

Please make the practice quices more difficult. Instead of asking if the operations were performed, with only yes/no option as answer, the practice quices can be more similar to the graded quices but a bit easier.

Why? In my opinion, the certifications will gain more prestige among Data Scientists because the effort to finish the course is higher.

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.

This course has been a real challenge for me. I've spent many more hours than planned to complete assignments of week1 and 2. I don't know if that is because of the topic I chose or because of the problems I had with Watson.

In the middle of the course, Watson stopped working ("monthly capacity reached"?). After asking for help in the forum, I didn't get any until 10 days later (and, by the way, what I was expecting). I somehow managed to install Juyputerlab as an alternative solution, but to do it properly and being able to publish some data into Github added more complexity to the challenge.

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!

This Capstone course takes you through all the knowledge areas from the beginning of the course. From Github, to Watson Studio, to IBM Skill Network Labs to outright use of your own resources. I wish all the resources to learn all you have to is concentrated in one location. However, I think the course contents need to be overhauled for improvement by addressing some issues pointed out by learners. Overall, it's been great but be ready to learn hard while using the learners forum.

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.

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.

To whom it may concern,

the course overall is very targeted to useful tasks and knowledge. One thing that could be improved is the final project with perhaps several options that are more specifically defined than the current form of the project. I understand that this will encourage copycats to participate. Another thought would be an interaction with other peers.

Thank you for the consideration.

A good course but this last "Capstone" course takes a lot more time than is allocated. I had to look up many Python coding instructions and do a lot of research myself. I feel like the initial 8 courses do not prepare you sufficiently for the final assignment. I probably spent twice the hours allocated on this assignment but had a great feeling of accomplishment at the end.

The inconsistencies among the datasets used in different files should be explained. For instance, why the dataset for SQL queries has 101 launches whether the data for model development has 90 launches and the data used for the dashboard only has 56 launches. I have confirmed my code several times and I don't see any reason for this inconsistency on my side.

Some material was a bit too basic, and not all subjects were covered in enough detail for me to feel completely qualified to jump right into data science work professionally, but this was a fantastic series of courses to get familiar with data science concepts and workflows in Python. It's given me the tools and knowledge to continue learning more on my own.