The topic is so far away from what we normally face in the business world that I found it irrelevant. Also, the whole course material (entire Data Science Professional Cert program) needs to be ''audited" for correctness, for example some quiz answers are wrong (therefore when you click the right answer, you get marked wrong), then there are instances of lack of guidance or misguidance in certain lab assignments, leading to hours of wasted time until you figure it out. Anyway it facilitates learning, albeit in a time-intensive way. Lastly, in case you need to quickly contact some instructor to point out anything, it's totally hopeless. Just give up. They make sure you cannot get in touch with a human being live. On the other hand, the course is organized well in terms of topic by topic development; a lot of effort has gone into creating the videos etc. I learned a lot and am happy with what I learned.

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.

On overall IBM data science professional certificate track: P​ros: Content is just good enough, instructors are good. Cons: IBM watson and the platform given to practise on is awful and has terrible performance and reliability issues, most often doesnt work and had an impact on my test deliverables. I personally overcame those issues to some extent with kaggle's and google colab jupyter notebook environments.

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

Overall course is good but as you get closer and closer to the final project in the hints and assignments are not explained very well. I had to go through a lot of back and forth on the dicussion forum to solve problems, often over multiple day.

G​ood material but instructions are very unclear. The final project instructions were not at all well laid out.

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.

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.

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.

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?

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.

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.

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.

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.

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.

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.

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.

Unnecessairily complicated. Here to learn Python..not how to connect files from here and there

pushing ibm watson studio too hard, i lost all interested in this course.

cannot unlock and continue studying after resting the deadline.

peer reviewing is worst

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.

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".

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.