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Retour à Foundations of Data Science: K-Means Clustering in Python

Avis et commentaires pour d'étudiants pour Foundations of Data Science: K-Means Clustering in Python par Université de Londres

397 évaluations
125 avis

À propos du cours

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....

Meilleurs avis


31 août 2021

This course has great potential for future Data Scientists and it gives a breif explination of what we are dealing in the companies by giving us real life problems and making us solve those problems.


3 juin 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.

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101 - 125 sur 125 Avis pour Foundations of Data Science: K-Means Clustering in Python

par Miguel S

28 janv. 2022

f​oundations ' course

par Fan K N

12 févr. 2020

Excellent course !!!

par Paul L

13 juil. 2020

Very good quality.

par pritee k

23 juin 2021

very informative

par Harsh P

29 mars 2020

Amazing Course!

par Naeema T

22 juil. 2020

amazing course

par Anna W

8 mai 2021

useful course

par Gerald D

27 nov. 2020

Great course!

par Josabet A A G

2 juil. 2021

Good Course

par Amin

14 janv. 2020

Thank you

par vijaya r

25 mai 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.

par Justin M J

11 mai 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.

par Daniel e

1 avr. 2021

First 4 weeks very good (you learn enough and there are interesting topics), last week very "backloaded" half the course I would say. Also you dont get to do your ow project but is given a topic which i did not like. very good overall.

par Saleh A

28 avr. 2022

Clear instructions and explanations. Could be a little more details on the algorithm. Makes it a very good course for "getting things done". If you are interested in what goes on under the hood though this might not be for you.

par Chintoo K

31 mai 2020

It was a great journey to get through it. Thanks a lot to all the instructors for their valuable job and effort :)

par Rednam M

23 mars 2022

one can learn from basics . and thet can gain knowledge

par Yeung K Y

14 oct. 2020

Good content and I would recommend my friends for it.

par Leo G

28 juin 2021

A​n introductory course all together.

par Jaison M

30 avr. 2020

Very good if new to data science


12 juil. 2020

Nice course for New learner


13 mai 2020


par Anton S

17 août 2019

Good introduction to k-means clustering using Python. Easy for follow.

par Margaret M L

20 janv. 2021

You should be able to transcribe the code which is presented into Jupyter Notebooks as is. This is now the second time I have done so and I am guessing some of the needed code was left out as the code does not work. How can students be expected to complete assignments when the Instructor's code is not reproducible?