Data Science: K-Means Clustering in Python
Learn Python-based data clustering with K-means algorithm. Master fundamental concepts in data science through hands-on practice with real-world datasets.
Learn Python-based data clustering with K-means algorithm. Master fundamental concepts in data science through hands-on practice with real-world datasets.
This comprehensive course introduces the core concepts of Data Science through practical implementation of K-means clustering in Python. Designed by experts from Goldsmiths, University of London, it covers essential mathematics, statistics, and programming skills needed for data analysis. Students learn through hands-on exercises and a practical data clustering project, making it ideal for beginners wanting to build a strong foundation in data science techniques.
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What you'll learn
Define and explain the key concepts of data clustering
Demonstrate understanding of key constructs and features of Python language
Implement the principle steps of K-means algorithm in Python
Design and execute a complete data clustering workflow
Interpret clustering outputs effectively
Use Pandas for data manipulation and analysis
Skills you'll gain
This course includes:
174 Minutes PreRecorded video
39 assignments
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There are 5 modules in this course
This course provides a comprehensive introduction to data science fundamentals through the lens of K-means clustering. Students learn essential mathematical and statistical concepts, Python programming basics, and data analysis techniques. The curriculum covers data preprocessing, visualization, and implementation of the K-means algorithm. Through practical exercises and a final project, learners gain hands-on experience with real-world datasets, mastering both theoretical concepts and their practical applications.
Week 1: Foundations of Data Science: K-Means Clustering in Python
Module 1 · 6 Hours to complete
Week 2: Means and Deviations in Mathematics and Python
Module 2 · 4 Hours to complete
Week 3: Moving from One to Two Dimensional Data
Module 3 · 7 Hours to complete
Week 4: Introducing Pandas and Using K-Means to Analyse Data
Module 4 · 3 Hours to complete
Week 5: A Data Clustering Project
Module 5 · 6 Hours to complete
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Instructors
Expert in Pure Mathematics and Education
Dr. Betty Fyn-Sydney is a pure mathematician and associate lecturer in Mathematics at Goldsmiths, University of London, where she has been teaching since September 2016. In addition to her role at Goldsmiths, she serves as a teaching fellow at the University of Birmingham and a lecturer at Greenwich University. Her research focuses on group theory, representation theory, and coding theory, highlighting her commitment to advancing the field of mathematics. Dr. Fyn-Sydney conducts tutorials and workshops on various subjects, including Foundations of Problem Solving and Mathematical Modelling, while supervising undergraduate projects at Birmingham and leading tutorials at Greenwich. Her teaching experience spans modules in Linear Algebra, Group Theory, Number Theory, Calculus, and Statistics.
Innovator in Creative Computing and Digital Signal Processing
Dr. Matthew Yee-King is a Lecturer in Computing at Goldsmiths, University of London, specializing in creative digital signal processing and computer music. He has collaborated with prominent figures in the UK experimental music scene, contributing to the advancement of innovative sound technologies. In his teaching, Dr. Yee-King covers a range of topics within the BSc Creative Computing program, including audio signal processing and synthesis, programming in Processing, and audio development for the Android platform. His expertise not only enriches the academic experience for students but also fosters a deeper understanding of the intersection between technology and artistic expression. Through his work, Dr. Yee-King continues to push the boundaries of how digital tools can enhance creative practices in music and sound design.
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4.6 course rating
675 ratings
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