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.

This course is great as they give you a guideline and teach you how to approach the project. I like how it was open-ended and you can decide on your own project scope so it's up to you on how in-depth you want to go. However, perhaps more help and guidance could be provided as open-ended projects means there's too much to explore on your own

Overall, it was a challenging capstone course that required a fair amount of effort. I am satisfied with the content, even if there were some glitches. Having to repeatedly type in YES when submitting the final assignment was not only annoying, but also quite distracting as it caused numerous errors due to insufficient characters.

If you are expecting a follow the leader style class where they go over a methodology and you follow behind, this is not your course. The teachers go over basic stuff in video and challenge you with much harder problems to make you go out and find answers on the internet and delve into you problem deeper.

Very good course. There were some serious downtimes in the IBM Developer Skills network which delayed my progress significantly 5. I had to extend my course time by a month and the next month's fees kicked in. That said, it was a great experience doing this course and the professional certificate overall.

A real challenge! There were moments of frustration, particularly when libraries wouldn't work on different Jupyter notebooks. However, I have learnt so much. All the courses in this professional certificate have prepared me well... but there are also some very knowledgeable people in the data community.

Great set of courses to help experienced data scientists have a refreshers on the latest tools and methods. I really enjoyed the concepts and frameworks that I can apply in my future career. One way, I think that help improve this is a topic on ROC curves and how to plot them for model evaluation.

Good subject to analyze. It could have been a little harder to force the students to learn more. As it is, you have to be self-motivated to explore and go beyond the basics.

I think the amount of learning is commensurate with the amount of effort.

I would recommend the course.

I think that the geocoding and APIs need to be sorted out much better. You should not present students with location APIs, like google, that do not actually work, explain that they are available intermittently, then give them a csv file and call it good enough.

I felt like the course did what it was supposed and gave a broad overview of many skills and tools all data scientists should be aware of. I thought that the lab exercises and quizzes could have been a bit more in depth.

Valuable course to know more about Foursquare API, but short in content (more focused on assigning a final project). Maybe more tools or even templates for reports and/or presentations would make it more useful.

Very good to tie everything together with a Capstone project.

However, I had a tough time with the limitations of the Fourthsquare APIs.

So as a suggestion, recommend to use another API in the labs/projects.

Overall a solid course. I wish there was a little more explanation of the code that was used but this capstone definitely pushed me to learn and get better. Thanks IBM and Coursera for the course!