The whole certificate is fantastic. It teaches you a great overview in many data science skills.

However, if you live in Germany, it will not help you get a job in the field at all - I mean AT ALL. It's completely worthless. You either need work experience in the filed or a Bachelors or Masters degree in statistics or computer science, but then again, you wouldn't need this certificate. So it's a great course to learn things, but it's a poor course to enhance your job opportunities.

This capstone course is what you make of it. I reviewed several final submissions (we are required to review one and optionally can review several). Some people made the extra investment to do more than just apply the labs to other cities, they really learned by applying all the past learning and doing a project to apply and showcase their learning. There is lots to learn and apply here. My advice is for you to invest all you can so you can showcase your abilities!

A great course that tests your skills to apply your cumulative knowledge on Data Science since you embarked on the Programme.

As for me, even though I don't intend to become a full-fledged Data Science, this course along with the Specialisation Programme provide sufficient understanding and practical hands-on learning to better appreciate benefits and constraints of Data Science, particularly on the importance of data and machine learning ability.

I really like using everything I learned into my own example built from the beginning to end. I felt a little bit unsecured with the assignment at the beginning, because I was really a beginner in Python before I started this course. But it encouraged me a lot to finish it and to learn more. Now I have a basis and a lot of information, that I need to sort out. I am planning to do more Python courses and continue practicing.

Great to put into action the theoretical knowledge acquired in the previous 8 classes. What could be improved? The peer-rating system can be very slow. A common practice is to go on the forum to beg for a rating by another learner. This can be tiring.

Dengan mengikuti kelas ini, saya mengerti tentang hal yang berkaitan dengan data science, dan menyelesaikan kasus yang nyata pada bidang ini. Tools yang digunakan juga bagus dan mudah dipelajari. Terima kasih Coursera <3

It's a great course to get a comprehensive background on Data Science (including ML) and lays the foundation for more advanced courses. It touches on all the areas that are required for data science.

The IBM Data Science Professional Certificate is a very good course. lam glad to have covered the course with the help of Coursera. I will put to use all concepts learnt in this Data Science series.

Excellent chance to push in every concept learnt so far. Best thing about the course is the chance to self identify a problem and find the solutions all by ourselves. Great Content. Thanks !

This course is instructive and challenging at the same time. Now I do wish I know a bit more about python and pandas before I jump in this course. But in the end I managed to get through.

Thank You for such a great end to end project, learnt a lot !! Presentation was damn lengthy though

Hi, I didn't receive the Badge and certification too. Please, Can You solve it for me?

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

Very easy to understand and remember this material

I would have liked to come up with a capstone project which did not encourage me to use location data from Foursquare, and rather use other data to come up with problems and solutions which do not necessarily have to do with location data. But all in all, it was an interesting course and great knowledge was gained

Some things need to be updated. Such and such is "Deprecated" errors, typo errors, and less and less thought put on in designing the exercises. I can feel the preparers dropping the ball towards the end of this specialization. Current visualization trends should be included like Tableau.

Overall, the course is very practical, you have the chance to do a project use the technology you have learned so far, and get a feeling of what Data Science work looks like. Recommend for those who interested in Data Science Area.

This project is beneficial by providing the chance to explore Foursquare API and generate some realistic results. However the discussion forum is not well used, questions posted there are rarely answered by teach staff.

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.

The assignments are either correct or incorrect and the scores are depends on your classmates grade to you.

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

Very challenging course, much more challenging than the previous IBM Data Science courses. Although all of the topics that become relevant in the Capstone project have been addressed in previous courses it almost feels as if you have learned nothing before. So you HAVE to go back to the old courses. More often than not you realize that before you had only a superficial understanding of the concepts and the details. On the other hand, it also shows that some of the previous courses just were not good enough to have a lasting impact. This is especially true for the "Data Visualisation" course (and here in particular the Dashboard topic).

So while in the end it was a good learning experience to go through the pain of going back and forth between the project and old courses (or other sources), I would have wished that there is a more seamless flow within the whole IBM Data Science course.

Creating a 50 page final Report is quite a challenge but it certainly helped to tie everything together. In the end, it was worth it and I am actually somewhat proud that I worked my way through it.

With all the critical points I mentioned I don't want to miss the opportunity to thank all instructors for setting up this course. I can imagine that this itself is a Herculean task, with all the technical hurdles that needed to be overcome. While there were a few hiccups that were only addressed in the discussion forum, from a technical perspective, I think this course actually worked quite well.

So thanks again - keep up the good work.

The whole course desperately needs a proof read. There are sections where sentences don't make any sense. The scripting and dialogue can be really clunky as well - phrases are repeated over and over . There are also parts where the exam section or final coursework has been updated with new content but the rest of the course has not been. For example one section you are asked to construct a pie chart the help links it gives you are for a bar chart. The videos are much more of an introduction and the real practice takes place in work sheets. I would like to see more videos explaining the processes in the worksheets as well as intro videos. Also Watson is pointless you make us sign up for a free account but nothing is really done with the programme, it is all jupiter or dash. Make everything in Watson or take the whole thing out.

However, there is a lot of information in the work sheets and it is a good starting point for Data Science. The writers are clearly passionate about Data Science and that comes through in the course. It gives you a very good start point for a career in Data Science.

As much as I would like to recommend this course, I was really disappointed by the poor quality of the videos in this lecture. They contained a lot of spelling mistakes and were not aligned with what you find in the corresponding notebooks which made it very cumbersome to take notes while watching the lectures. Also the capstone project, while being interesting and challenging, was not properly motivated by the course itself.

I think the extend was good and concept fine but it needs a lot of fine-tuning to be an outstanding course which wants you to continue an do the next one immediately after.

I think I was a tougher critic of my own project than anyone else was going to be; the rubric for peer-grading was almost entirely about presentation with little emphasis on the data analysis itself. The requirement to use Foursquare's API was a limitation on the possible topics for the project, and Foursquare's documentation of endpoints fails to disclose that some fields such as Rating are the result of proprietary, unusual calculations that are unlikely to correlate strongly with any simple data.