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.
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Foundations of Data Science: K-Means Clustering in Python
University of LondonAbout this Course
You will need mathematical and statistical knowledge and skills at least at high-school level.
What you will 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 will gain
- K-Means Clustering
- Machine Learning
- Programming in Python
You will need mathematical and statistical knowledge and skills at least at high-school level.
Syllabus - What you will learn from this course
Week 1: Foundations of Data Science: K-Means Clustering in Python
Week 2: Means and Deviations in Mathematics and Python
Week 3: Moving from One to Two Dimensional Data
Week 4: Introducing Pandas and Using K-Means to Analyse Data
Reviews
- 5 stars72.55%
- 4 stars21.30%
- 3 stars3.64%
- 2 stars1.34%
- 1 star1.15%
TOP REVIEWS FROM FOUNDATIONS OF DATA SCIENCE: K-MEANS CLUSTERING IN PYTHON
I learnt alot, a very good foundation course. It made me have more interest in learning more in Data Science particularly using Python language
Very interesting course! The lecturers explain concepts thoroughly which makes the concepts easy to understand even for people without much knowledge in Data Science
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.
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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