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.
Foundations of Data Science: K-Means Clustering in Python
Foundations of Data Science: K-Means Clustering in Python
Instructors: Dr Matthew Yee-King
Access provided by Taipei Medical University [C4CB]
74,100 already enrolled
Gain insight into a topic and learn the fundamentals.
4.6
(710 reviews)
Beginner level
Recommended experience
Flexible schedule
Approx. 29 hours
Learn at your own pace
95%
Most learners liked this course
What you'll learn
Define and explain the key concepts of data clustering
Demonstrate understanding of the key constructs and features of the Python language.
Implement in Python the principle steps of the K-means algorithm.
Design and execute a whole data clustering workflow and interpret the outputs.
Skills you'll gain
Details to know
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Assessments
39 assignments
Taught in English
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There are 5 modules in this course
This week we will introduce you to the course and to the team who will be guiding you through the course over the next 5 weeks. The aim of this week's material is to gently introduce you to Data Science through some real-world examples of where Data Science is used, and also by highlighting some of the main concepts involved.
What's included
9 videos4 assignments3 discussion prompts
What's included
11 videos4 readings10 assignments1 peer review1 ungraded lab
What's included
16 videos10 readings15 assignments
What's included
8 videos6 readings7 assignments1 peer review
What's included
9 videos3 readings3 assignments3 peer reviews5 discussion prompts
Instructors
Instructor ratings
4.6 (301 ratings)
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4.6
710 reviews
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Showing 3 of 710
SS
5
Reviewed on Jun 29, 2020
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.
PM
4
Reviewed on Dec 19, 2022
Overall, a great experience but labs could have been better, and few instructors were not very detailed in their approach.
MP
5
Reviewed on Sep 12, 2020
It was a well-taught course. I felt that during the final project-making, students were not spoon-fed, instead, pushed us to become more creative.
Recommended if you're interested in Data Science
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