Learner Reviews & Feedback for Foundations of Data Science: K-Means Clustering in Python by University of London

I would highly recommend the course to those who have no background in Data Science. I started without any knowledge about Python and upgraded it with the help of this course. Videos are short and informative. Assignments are short and related to the videos discussed before. It's easy to finish the course before deadlines.

The only drawback is that the course doesn't have any Financial Aid.

This course gives us a good balance between theory and practice. I wish there was an intermediate or advanced level to continue.

The course was very well layed out and divided into short lessons. Things were explained and I found them easy to follow. There was also plenty of focus on practice. Assignments were peer reviews which made the process quite fast. The assignment made me understand the bigger picture and pushed me to do further reading/research. I very much enjoyed the experience

The only problem I had was with the app. I could not use it to upload, submit quizzes or properly view peer's work or my own feedback.

Excellent and very well designed course. The way the course exponentially takes you from the very basics of the topic to a certain level of mastery is commendable. If you know the basics of data science and Python programming, this course can easily be completed in less than a week. The peer reviews can be more productive if fellow learners actively participate more often and leave valuable feedback, rather than only responding to mandatory radio buttons for feedback.

I felt that the instructors were passionate about the subject and it made me want to learn more. The course assumes that you don't know any python, which was good for me as that was exactly my situation when I started. However, if students did have a more advanced knowledge of data science concepts and python they could show this off in the assignments.

This is a very good and useful course for learning about the basics of data science. I highly recommend it if you want to start learning about this field. Basics skills of coding are recommended

184/5000

Conferences of very good quality, and the platform for practices is really useful to put the theory into practice. I recommend this course if you want to start in data science.

I liked it, very usefull and objective guide to implemt the algorithm, I also liked the format, many short videos wich is great to keep concentration

Excellent course! It was well distributed, videos and theorical content, and then, practical videos and cases. Recommended!

This course is at right level for a beginner (python and analytics) while going into details around K means clustering

It is a very apt course for beginners. All the concepts have been taught and discussed properly

Great introduction to clustering. Week 5 material could be improved - not as good as 1-4.

Really enjoyable and well thought through. As someone new to data science I learnt a lot!

Thanks for this course. It was good experience and content of course was also very nice.

Good

K Mean algorimth need to be explained in more detail with 2 to 3 examples

The course was well until the last week.

i am giving this note because they read theirs textes. it is not a teaching method. ı could read myself as well. i don't' understand why they do like this. in addition, the project is not well planned.

Not much details were covered

Started from scratch and excellent progression! The sad part is that we only learnt K-means, would like to learn more topics via this structure :) Discussion forum commmunty was also super helpful! But, of course, you might have a different experience. Had to find some code on my own, yes, that's how it is in real life, but I'm lucky that I had generous and communicative peers who were open to sharing their code! I shared too - here's my link: https://www.coursera.org/learn/data-science-k-means-clustering-python/discussions/weeks/5/threads/MtHzBf6_EeqUdwo2TDNrdw Also, took a while for my assignments to get graded, I don't know why, but you can trade peer reviews with your peers in the forums (i.e. grade their submission in return for them reviewing yours) :)

Learned much from this course thanks to all great instructors. It will be better if learners have some basic Python knowledge otherwise may have some difficulty in the coding of the assignments. As with all MOOC there are always rooms for improvement. For this course my thinking is that some sections need to be revised for better clarity e.g. the Mathematical explanation on Euclidean distance where it can be overwhelming and the learners may find it difficult to relate it relevance to K-Means amongst all the mathematical jargon. Overall this course provides good insight for beginners into understanding K-Means using Python, and an overview of performing proper data science project.

The course content is great. I especially enjoyed Week 4—the lecturer was concise and engaging. There are lots of people plagiarising others' work by screenshotting their charts though and re-submitting their work which is sad. This course would benefit from a summary at the end of week 5 explaining what conclusions we might or should have come to.

This is a perfect course for those who just dove into the ocean of data science. This course teaches you with the very beginning and you can easily tackle everything even you don't know anything about data science. I highly recommend everyone to start with this foundation level course if you want to excel in the field fo Data Science.

A very enjoyable course that has opened my eyes to data science. Not coming from a data / mathematical background is not a huge drawback, but be prepared to read around the subject matter if you want to gain a better appreciation of the course slides and videos. Very happy that I found this course.

I think this is a very good course for beginners in Data Science and Python. I know how to implement K-means algorithm but wanted to learn Python, so I took this course. It is well organized, and videos are short and concise. I am looking forward to more intermediate / advanced courses of this.