Great course for beginners. I really enjoyed the data science projects and I wish we had few more of the projects to use the knowlege gained.

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

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.

Great for starter and those who have no experience in data science and phyton.

I really enjoyed the course and learned a lot

Good

The course is nice and well explained. Some of the mathematical concepts are explained into more detail than necessary for the scope of the course, and at the same time with not enough detail to understand them properly. Also, I expected the course to show some professional software, but it only touches Python. Still, I enjoyed the course and I think it does it's job as an itroduction to the subject.

I was impressed by the amount of informative, relevant and in-depth material, taught and built up form a very basic level. The video lectures and practical exercises have really helped to cement the material covered in this course. In areas, there are quite a few mistakes (content, wording, etc), which can be confusing and come across as a bit sloppy.

It was not fully explained how to use Python, therefore I should have looked for the more information through internet by myself.

However, it was quite interesting to understand why data science could be important and how to use with K-means clustering.

Overall what you get for the price is about right. Sometimes there are glaring gaps in the presentation of concepts, and how sections fit together. In the end it served its purpose to me - to see if I want to continue exploring data science a a field.

The lessons based on maths had a lot of text; it would have been better to base it more on graphics or imagery, since it was confusing to follow speech and text on video at the same time.

Good intro into K-means clustering. Some great introductory math tutorials and basic python programming.

It is a good one for the beginner who is ready to give dedicated time.

useful. it will be better if you have some basic knowledge on python

More time should be given in the coding part.

Good course.. But self study also needed...

It is one of the best-organized and mathematical-oriented courses in Data Science and the general concept of Unsupervised Statistical Learning. After successfully attending and doing the capstone project, I could say that it is an intensive coursework-micro master of the topics that are covered. Above all, it is not an introductory course in Statistics. It requires some important basis in Mathematics and Statistics as well to understand some special topics of Metrics Topology that help to visualize in your mind the algorithm of k-means. Thank you, Coursera and partners, for this wonderful journey in the Mathematics of Machine Learning.

P.S. Peer assignments are completed on time only if people try to review each other's assignment.

This specific online coursera lesson, the way that it was designed, presented and delivered was a huge and great first step for me personally into learning and becoming familiar with the field of data science and i would like to thank all the Factors and the professors of the University of London for achieving this kind of online data science structure lesson. All of their videos, lectures and programming lessons were very interesting, informative , well understood and i would also like suggest anyone that is thinking of deepening his knowledge into this field, to follow this specific lesson.

I would like to thank the entire team at the University of London for providing this excellent introductory course on the use of the k-means algorithm. Perhaps it would be a good idea to include concepts such as the elbow method, which aims to determine the ideal number of clusters, and the confusion matrix for model evaluation. Thank you for everything, and you have significantly encouraged me to continue on my path in data science.

Method of instruction was easy to understand yet the content was very rich. The last project really openned my thinking in many respects. It pushed me to read a lot of other materials beyond the scope of the course.

Resetting deadlines fascility in this course is very helpful. I was during this course not able to attend course due to work commitments but I was able to reset deadlines and resume. It was great.

I totally recommend the great excellence of the course material, methods, tests and instructors. Thanks Matthew Yee-King, Betty Fynsydney and Dr. Larisa Soldatova. In this course we can learn more about K-Means Clustering, Statistic, Dimensional and Multidimensional Mean, Variance, Standard Deviation, Normalisation, Outliers, Numpy, Matplotlib and Pandas, where every thing was done using Python.

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.