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.
By Brandon S•
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.
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
By Theodoros P•
This was for me the last course needed for the IBM Data Science Professional Certificate. To have a capstone really makes sense. However, having to use the Foursquare API is very restrictive and does not leave much room for different data science problems. The data provided by Foursquare for my location are only suited for certain types of problems and I had a lot of difficulty finding a rationale for a meaningful project using the Foursquare data. It would be very helpful if more data sources are explicitly provided to allow also ML projects with supervised learning. I also think that the 20h estimation for completing the final task is ridiculously optimistic. I needed at least 100h (time in total) to complete the final project, whereas in all the previous tasks of the 9 courses comprising the IBM Data Science Professional Certificate I needed less than the estimated time.
By Elvijs M•
On one hand there is some encouragement to experiment and come up with an interesting problem and I actually took some time to come up with something non-trivial before starting. But practically, there is no need to -- you can just take the example notebook from one of the prior weeks and change the city. The evaluation doesn't really reward you for doing something original or extra.
Sadly, the certificate of the specialization as a whole is worthless.
By Izabela K•
I hoped this course would be more complex but it mostly consist of Foursquare API requests. I had to focus how to fit other datasets in addition to Foursquare data to find something interesting but instead of using fancy algorithms and testing them I had to focus on data and it was really time consuming, I spent 2x more time on each assignment. Whole IBM DS path was great but final course was a big disappointment.
By Alperen K•
it was the worst Coursera course I've ever took. Course capstones pages are buggy, there are not adequate explanations at videos. The final project is very painful because there are not enough resources and you are forced to prepare "notebook", "report" and "presentation/blog post" can't we just prepare two of these items?
By PD T•
Issues with the lab sessions and untimely responses from the staff hindered my learning. Also, the students did not respond to my questions in the discussion forums, they seem to only be about themselves and having people grade their assignments.
By Zach S•
This course appears to be all but abandoned by IBM. Many components of the Applied Data Science Capstone are outdated, Pythons scripts won't load, with errors plaguing almost every assignment. The only assistance you'll find is two to three year old forum posts, unanswered, from students who had to overcome the same errors with strange workarounds. Not to mention, the instructions are abysmal. It's almost as if the creators of this course have never actually had a homework assignment, and simply don't know how to compose a homework assignment, or they didn't take the time to actually do it themselves. Again, lackluster data science course from IBM, that they themselves don't care about.
By PRZEMYSLAW G•
While I rate highly the first 4 classes of the course, the 5th which is the final project is a joke.
There, you have to gather the info and make the predicition. Yet, you have a sample of, warning, 90 records and using ML models for that is a WASTE!
Also, the 5th does not treat crucial parts of DS jobs like feature engineering, picking right models or smply fitting the data correctly. In some cases it is not even there.
I do am sorry to grade it as 1/5 but the gem course class should be done better, way better.
By Javier J•
There are a lot of things wrong with this course: The labs need to be updated, we are asking to do something that didn't work in the labs. We are also ask to submit links, and the submission box does not allow clickable links. The Quizzes that doesn't show the question, the all or nothing peer reviewers, all of this is in the discussion forums, but there is not enough support staff.
By Varun C•
One star for the fact that the Lab notebooks were flooded with errors, you would need more time fixing the errors rather than going through them,
Also, in peer graded assignments, some of the links submitted aren't processed as hyperlinks and cannot be copy pasted either, so there's no way to view them.
Very bad experience on this one.
By Dennis M K•
This obscene deadlines are impossible to say the least. If you wanted us to pay you the two months immediately you could have just stated it on the terms... I would rather pay all the three months worth of tuition immediately than wait almost to months to finish this course.
By Kendall L•
This was the most frustrating course I have taken on Coursera. The foursqure api is not up to date and I had to waste hours of my life fixing the bugs.
By Sohini S•
Unnecessairily complicated. Here to learn Python..not how to connect files from here and there
By Rashad B•
pushing ibm watson studio too hard, i lost all interested in this course.
By Takahiro Y•
cannot unlock and continue studying after resting the deadline.
By Gurvi 1•
peer reviewing is worst
By Paul A•
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.
By Marius-Liviu B•
A tough course! I'm not a 9 to 5 Data Scientist so I need to made a lot of research in order to finish the project. But in the end it deserved it. I've learn a lot of things: technologies, libraries and concepts. Even my current job is PHP Developer I was amayzed how many tools from my current domain activity I can use on Data Science (Git, programming, SQL, Web scraping, Office suite tools). It was a well spent time and looking back on the overall experience for IBM Data Science Professional Certificate my one word conclusion is: "professionalism". And the second word that defines IBM since I hear about it is "innovation".
By Oritseweyinmi H A•
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.
By Had G•
Interesting project using Folium and Foursquare API. I learned a lot about geolocalization, and also pandas tricks, as i if you choose a subject on your own, usually your data sources are not cleaned (most of the time you got it by scrapping). Plan to work more than 20hrs (rather 30 - 40h) in order to complete the programming part (notebook and data analysis), the report (15 - 20 pages) and finally the blog article.
Enjoy it all !
By Zhanna C•
Many thanks to all the teaching staffs for such interesting courses and diversified subjects. It was fascinating to learn something new each time. Sometimes there were technical issues with Watson Studio and code running mostly because of our lack of knowledge but sometimes real technical issues not depending on us. Thanks for teacher's help and peers' care to assist. I am satisfied with this 10 Module course.
By Aécio L•
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.
By Anthony S•
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.
By Marceline C M•
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.