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Вернуться к Foundations of Data Science: K-Means Clustering in Python

Отзывы учащихся о курсе Foundations of Data Science: K-Means Clustering in Python от партнера Лондонский университет

4.7
звезд
Оценки: 340
Рецензии: 115

О курсе

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....

Лучшие рецензии

GK
31 авг. 2021 г.

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.

AH
3 июня 2020 г.

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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1–25 из 115 отзывов о курсе Foundations of Data Science: K-Means Clustering in Python

автор: Raushan U

19 июля 2020 г.

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.

автор: Sevinc S

30 июня 2020 г.

A well presented and interesting course. It would have been good to have some more complex examples with the thinking behind them - the exploratory bit/intelligent bit of the process.

автор: Guillermo A R

10 сент. 2019 г.

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.

автор: Nilson S d C

17 мая 2020 г.

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

автор: Md A I

19 июня 2020 г.

The course was well until the last week.

автор: Katlin S

5 февр. 2020 г.

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.

автор: Kaushik G

31 авг. 2020 г.

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.

автор: Stephen K

22 окт. 2019 г.

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.

автор: Uriel C

7 апр. 2020 г.

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

автор: Ben E

4 июля 2020 г.

Highly recommended to anyone who wants to delve into data science. The instruuctors, the universities and Coursera team are well dedicated and the course is of high quality.

автор: federico a

25 окт. 2019 г.

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

автор: Juan D C N

23 апр. 2020 г.

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

автор: Aditya B

3 июня 2019 г.

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

автор: Navya S

24 апр. 2020 г.

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

автор: Jesper O

18 апр. 2020 г.

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

автор: Amy S

22 февр. 2020 г.

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

автор: Harshit R

26 апр. 2020 г.

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

автор: Ankara s

9 апр. 2020 г.

Good

автор: David N

18 мар. 2021 г.

Very good course, even for someone who isn't a beginner at data science. Filled in some holes I had in machine learning, plotting and statistics. I like the way a few mistakes by lecturers were left in the material and then a point was made to talk about the errors and why they were wrong, etc., which added to the learning.

автор: satish k

13 мая 2020 г.

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

автор: Naveed K

27 сент. 2020 г.

It's a good course but data cleaning should also be included...

автор: KUTLU

17 апр. 2020 г.

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.

автор: RITIK B

17 сент. 2020 г.

Not much details were covered

автор: Hawra F A

27 сент. 2020 г.

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) :)

автор: mike

27 авг. 2020 г.

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.