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Foundations of Data Science: K-Means Clustering in Python

Foundations of Data Science: K-Means Clustering in Python

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9.5/10 (Our Score)
Product is rated as #1 in category Machine Learning

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.

Instructor Details

Dr Matthew Yee-King is a Lecturer in Computing at Goldsmiths, University of London. He is an expert in creative digital signal processing and computer music and has worked with some of the leading names in the UK experimental music scene. He teaches on the BSc Creative Computing topics such as audio signal processing and synthesis, programming in Processing and audio development for the Android platform.

Specification: Foundations of Data Science: K-Means Clustering in Python

Duration

30 hours

Year

2019

Level

Beginner

Certificate

Yes

Quizzes

Yes

15 reviews for Foundations of Data Science: K-Means Clustering in Python

4.5 out of 5
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  1. Aditya

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

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  2. Anton S

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

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  3. Guillermo A R

    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.

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  4. Bhawna D

    More time should be given in the coding part.

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  5. Jesus R

    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.

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  6. Stephen K

    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.

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  7. federico a

    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

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  8. Pedro N D B

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

    Helpful(0) Unhelpful(0)You have already voted this
  9. Amin

    Thank you

    Helpful(0) Unhelpful(0)You have already voted this
  10. Nitin T

    Everything is good for a beginner in this course.

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  11. Samson C E

    was well explained and a good insight provided.

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  12. Katlin S

    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.

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  13. Fan K N

    Excellent course !!!

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  14. Sayali P

    Amazing Course!

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  15. Keith B

    Loved the Python and the Mathematical explanations.

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

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