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

178 ratings
59 reviews

About the Course

Organisations all around the world are using data to predict behaviours and extract valuable real-world insights to inform decisions. Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government. This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses. It focuses on the basic mathematics, statistics and programming skills that are necessary for typical data analysis tasks. You will consider these fundamental concepts on an example data clustering task, and you will use this example to learn basic programming skills that are necessary for mastering Data Science techniques. During the course, you will be asked to do a series of mathematical and programming exercises and a small data clustering project for a given dataset....

Top reviews


Jun 04, 2020

I love this course as it gives me the foundations of learning the Python coding program and relevant statistical methods that used for data analysis. It's really interesting course to attend to.


Sep 10, 2019

184/5000\n\nConferences 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.

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26 - 50 of 61 Reviews for Foundations of Data Science: K-Means Clustering in Python

By Ihsan N R H

Jun 17, 2020

This course its good if you want to learn about data science K-Means Clustering

By Salim A

May 26, 2020

Excellent instructors, easy to learn and good quality course design.

By Syed S A

May 29, 2020

This is a great course to get started with Data Science and Python.

By Humberto L

Jul 09, 2020

Excellent course, very well explained all topics, thanks a lot!!

By Pedro N D B

Dec 06, 2019

Excellent!. Very well explained. Step by Step. Great Instructors

By Keith B

Mar 29, 2020

Loved the Python and the Mathematical explanations.

By Liza P P

Jul 04, 2020

It is a very useful course.

I can only recommend.

By Nitin T

Jan 29, 2020

Everything is good for a beginner in this course.

By Samson C E

Feb 01, 2020

was well explained and a good insight provided.

By Dieter N

May 02, 2020

I hope there will be another AI-curse

By Martin W

May 25, 2020

Excellent course, really enjoyed it !

By Vincent

Mar 16, 2020

Very useful for foundation knowledge.

By Dario R

Feb 26, 2020

great course, i 've learned a lot.

By Anshul G

Jun 02, 2020

Very good course for beginners!!!

By Somanathi S R t

May 23, 2020

learned a lot ,thanks!!


May 11, 2020

Well structured course!

By Farhad A

Apr 27, 2020

everything was perfect

By Fan K N

Feb 12, 2020

Excellent course !!!

By Paul L

Jul 13, 2020

Very good quality.

By Harsh P

Mar 29, 2020

Amazing Course!

By Amin

Jan 14, 2020

Thank you


May 25, 2020

This course starts from fundamental level. The instructors clearly explains statistical methods such as mean, variance, standard deviation, variance etc with python source code on a simple data set. Then they have explained plotting with labels and finally how to apply k-means clustering on bank note authentication dataset.


May 10, 2020

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.

By Justin M J

May 11, 2020

I would highly recommend this course for any beginners. it simply suits both the first timers and people who wish to further existing knowledge and understanding of Python and data science to another level. enjoyable homebased learning.

By Jesus R

Sep 25, 2019

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.