Interesting projects included!

Useful course for starter

Little bit hard

Good exposure.

真的难，对我这个初学者来说

great course

good course

Fun course!

very good

Excellent

Average

Good !

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.

The assignments were interesting especially when we had to think of our own problems to solve. It would've been really helpful if the course was regularly updated, specifically when it comes to the first assignment where a lot of students experienced challenges with their machines and the course was designed with old operating machines in mind. the discussion forums would help a lot if instructors actually answered the questions and not directed students to links that were of no assistance at all. The course material could really do with an upgrade.

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 github guide was very helpful and informative. I wished there would have been more explanations how to interprete the results of evaluated models and about machine models in general, when to use which model for example. Also some details were missing like how to balance imbalanced data, should the data be balanced and then visualized or vice versa? Fitting a model is easily done, but it's the details that make the difference.

The course gives the learners a perfect platform to practice the concepts learnt throughout the Data Science specialization. Final assignment is unique and interesting and the course makes sure you practice enough before taking it up. A good experience, but issues with Foursquare now and then makes it a little hectic to get done with the course.

A nice course though. Liked it!

The journey was very informative but at the end of the course and submitting all of my assignments before the deadline , i got my course certificate with no instructor signature . A very disappointing fact as I worked very hard to complete the specialization within my 7 day free trial which is till 8 pm today I.S.T

Never expected such kind of irresponsibility

The capstone content needs to be thoroughly edited for clarity, especially in the instructions on what exactly the instructors are looking for in the final deliverables. Some of the peer-reviewers seem to be confused, and I'll admit I was a little confused myself reading through the instructions the first time.

The final capstone felt like a mis-match with the previous 3 courses of the specialization. The capstone was also the final project for other specialization which would explain the mis-match.

Even though I learned a lot, it wasn't entirely clear what was actually required from me.

This course is ok. Not too challenging and not too easy. It definitely needs an update. Things have changed over the last couple of years and it appears that the course developers have not made an update since 2018. WIth a refresh this course will be a 5 star course for sure.

Labs could be improved verifying all the commands work well. Videos could be improved make them more informative as in other Data Science courses part of the Specialization. Many things are left to the labs without sufficient previous explanation.

The challenges presented have been really good however I'd reccomend that the prceding modules be evaluated for the changes that have come in over the years,maybe lay more emphasis on 'what is used more frequently' in a real life situation !!

This course was extremely difficult and it took me the longest. There were errors in the lab so I could not finish any of the labs, but I had asked for help and the response for help was very poor for the lab or for the assignment.

It is good because you get to know many tools to get data and it has very good examples of how a project should look, but i think this is a project that must not be reviewed by other students, so we get useful feedback.