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

Very easy to understand and remember this material

I finished all he 9 courses in this specialisation. Therefore, this comment basically applies most of the 9 courses.

The video contents and the practice exercises are very good and on point. Instructors are great. However, there are serious problems with the assessment mechanism and this is the reason why I am giving a 4 start.

If you work hard on the assignments, meaning that you study and research well to understand the code, you might end up getting a low score on assignments. This is because of peer-graded assignments. Your work might be graded by someone who doesn't understand the material as much as you do, or even someone who submitted a blank file just to see others' work. You rarely get feedback for the missing marks. As a simple example, I once submitted my work and received 4/11 with no feedback. I instantly re-submitted the same work and received 9/11 from another peer.

Another problem is that you get to see the rubric only after you submit. Some assignments are not clear on what the specific expectations are. The rubric must be clear before you submit the work. Even if you try to be flexible in your solution to address the vagueness, the peers may not show the same flexibility although you do the work properly.

And finally, the biggest issue.. Plagiarism! When I say plagiarism here, I mean copying someone else's work line by line all the way. It is utterly disgusting that it is more widespread than I initially thought. Such cases have been posted multiple times in the forum. I encountered at least 2 cases of plagiarism. The only thing you can do is to flag the submission, but obviously this doesn't stop anyone. What's worse is that those people who plagiarised someone else's work line by line get to peer-grade your own work.

Assessment section of these courses is a mess and has to be seriously re-evaluated. Peer graded assignments can be accepted to a certain extend but not for assignments that require hours and hours of our effort.

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

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.

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

the idea and all that was great, but despite doing all the previous courses, it was very difficult for me to do the project ... I felt I had many cheese holes and got stuck many times ... note, I had no experience with DS, programming before, but coming from a finance background ... I would say the entire specialisation is not for a true beginner ... but the course package overall is very strong I think

To much focus on the use of the Foursquare API, which is outdated is bit. Other techniques learned in the program are not used, only clustering with K-means. On the other hand, you are forced to experience hands-on reality, when things are just working different then expected (which is meant to be possitive feebback). Week 4 and 5 take a lot of time (far over expected 30 hours, more like 60-80).

This course's content is out of date. Students have to rely on the posts of other students to work around issues with the course. This is a real shame as the other courses required to receive the certification are well maintained.

This course is full of bugs (outdated) and lack of explanation for certain matter is seen in Discussion Forums.

When I first started this capstone, it felt a bit disjointed compared with the rest of the courses. But after really biting into it, I realized the content makes sense: it allowed me to put to the test what I learned on the course. Being constrained to use the Foursquare API on the capstone feels a bit odd, but at the end using an API to get information works really well. I tried scrapping the information my self and the workload I put on my self became significant.

The only new machine learning tool introduced for this final part is K-means clustering, it's the most abstract concept on the entire specialization and I think it's the only one that could have been presented somewhat better. What I noticed while reviewing my peers is that for the assignments everyone (me included) would just copy the k-means clustering algorithm and repeat the same analysis used on the labs. Which is kind of a shame, I just wish K-means clustering could have been developed better, beyond copy-paste.

At then end it's you; the person taking this specialization, the one who decides how much work you're going to put into this.

Tough but ultimately very rewarding as you see your own data science project through from inception, to data pre-preprocessing, modelling and finally presenting. All in all, great course and a perfect way to round out an amazing specialization! Thank you IBM and thank you to all the course lecturers who contributed to make this high calibre program. This has given me the grounding and the confidence needed on my path towards being a data scientist.

O curso em si é bom, porém pede conhecimentos que não são passados durante o programa, e isso é algo inesperado, porém todos que querem trabalhar como cientista de dados devem estar preparados para contornar e vencer desafios diariamente. Então vejo como positivo essa estratégia de solicitar algo fora do escopo do treinamento. Pra mim foi muito bom, tive oportunidade de aprender coisas novas o tempo todo.

This course was challenging but rewarding. The setting up of the course was really quite frustrating but as everything continued I found myself incredibly engaged with what is going on. This course specialization was my first exposure to what are essentially programming skills and use of the Python language so it was really valuable to be able to produce a piece of work to demonstrate what I learned.

Thank you Coursera and IBM. I can now do statistical programming using different Python libraries, geospatial analysis using folium, notebook sharing via Github or IBM Cloud. Thank you for empowering me through sharpening my data management,manipulation,analysis and presentation skills. Certainly, the IBM Data Science Capstone has been one of the most worthwhile things I've done in 2020.

This Course is excellent and it gives Data Science,Methodology,python programming,Machine learning algorithims,Labs Excercises,Assignments and finally Capstone Project is worth to gain very good skills and knowledge.With this course I gained very strong skills and am now very confident in this Data Scientist field. Thank You Coursera and IBM for this course.

This is wonderful platform for enthusiastic Data Scientists aspirants to learn sophisticated empirical analysis to understand and make predictions about complex systems. Demonstrated methods and tooling from probability and statistics, mathematics, and computer science and primarily focus on extracting insights from data.

ThankYou

Hari Kumar G

Fantastic culmination of the Data Science Professional Certificate! This course provided excellent review in creating maps and using machine learning tools, and the final project is a great opportunity to practice many of the skills learned in previous courses as you analyze a real world data set of your choosing. Challenging and rewarding.

This is an excellent course. I had learnt the real-world applications of foursquare API and how we can find out the restaurants, coffee shops, shopping malls etc. within a particular neighbourhood and how to cluster them. I am completely satisfied with the course and content is of very good quality and the lab sessions are excellent too!

Great Specialization. I thank the whole team of IBM and Coursera for providing me this valuable knowledge. This specialization is my first stepping stone towards my aim of becoming a Data Scientist.

Also, I would love to convey thanks to Coursera for this wonderful Financial aid program, only because of which I completed this course.

The capstone project lets the learner to apply their Data Science skills learned through out the course. All the assignments are peer graded. It is good in one sense, however periodical review from the course admins might make the course grades more authentic and not left to the mercy of the peer reviewers.

It's a high technical course includes new professional and market need tools. In addition to that it review the previous courses tools and used to build our final capstone project.

Thank you, IBM, Thank you, Coursera, Thank you, all Instructors and students who participate in the discussion of courses forum.

I really like the hands-on exercises in this course, very interesting. And the capstone project make me review all skills I've leart in this course. I hope I could become an volunteer TA for this Data Science course and contribute to our learner community. Thank you for providing this course!