Foundations of Data Science: K-Means Clustering in Python
- 4.6
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Approx. 29 hours to complete
Course Summary
This course is designed to teach students how to use k-means clustering in Python for data science applications. Students will learn how to implement k-means clustering algorithms, evaluate their results, and apply them to real-world problems.Key Learning Points
- Learn how to use k-means clustering in Python for data science applications
- Implement k-means clustering algorithms
- Evaluate the results of k-means clustering and apply it to real-world problems
Related Topics for further study
Learning Outcomes
- Implement k-means clustering algorithms using Python
- Evaluate the effectiveness of k-means clustering
- Apply k-means clustering to real-world problems
Prerequisites or good to have knowledge before taking this course
- Basic understanding of Python programming
- Familiarity with data science concepts
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Video lectures
- Hands-on exercises
Similar Courses
- Machine Learning with Python
- Applied Data Science with Python
- Data Mining
Related Education Paths
Related Books
Description
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.
Knowledge
- 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.
Outline
- Week 1: Foundations of Data Science: K-Means Clustering in Python
- Welcome and Introduction
- Introduction to Data Science
- What is Data?
- Types of Data
- Machine Learning
- Supervised vs Unsupervised Learning
- K-Means Clustering
- Preparing your Data
- A Real World Dataset
- Types of Data – Review Information
- Supervised vs Unsupervised – Review Information
- K-Means Clustering – Review Information
- Week 1 Summative Assessment
- Week 2: Means and Deviations in Mathematics and Python
- 2.0: Week 2 Introduction
- 2.1 – Introduction to Mathematical Concepts of Data Clustering
- 2.2 – Mean of One Dimensional Lists
- 2.3 – Variance and Standard Deviation
- 2.4 Jupyter Notebooks
- 2.5 Variables
- 2.6 Lists
- 2.7 Computing the Mean
- 2.8 Better Lists: NumPy
- 2.9 Computing the Standard Deviation
- Week 2 Conclusion
- Population vs Sample, Bias
- Variability, Standard Deviation and Bias
- Python Style Guide
- Numpy and Array Creation
- Population vs Sample – Review Information
- Mean of One Dimensional Lists – Review Information
- Variance and Standard Deviation – Review Information
- Jupyter Notebooks – Review Information
- Variables – Review Information
- Lists – Review Information
- Computing the Mean – Review Information
- Better Lists – Review Information
- Computing the Standard Deviation – Review Information
- Week 2 Summative Assessment
- Week 3: Moving from One to Two Dimensional Data
- Week 3 Introduction
- 3.1 Multidimensional Data Points and Features
- 3.2 Multidimensional Mean
- 3.3 Dispersion: Multidimensional Variables
- 3.4 Distance Metrics
- 3.5 Normalisation
- 3.6 Outliers
- 3.7 Basic Plotting
- 3.7a Storing 2D Coordinates in a Single Data Structure
- 3.8 Multidimensional Mean
- 3.9 Adding Graphical Overlays
- 3.10 Calculating the Distance to the Mean
- 3.11 List Comprehension
- 3.12 Normalisation in Python
- 3.13 Outliers and Plotting Normalised Data
- Week 3 Conclusion
- Multidimensional Data Points and Features Recap
- Multidimensional Mean Recap
- Multidimensional Variables Recap
- Distance Metrics Recap
- Normalisation Recap
- Note on Matplotlib
- Matplotlib Scatter Plot Documentation
- Matplotlib Patches Documentation
- List Comprehension Documentation
- 3.12 Errata
- Multidimensional Data Points and Features – Review Information
- Multidimensional Mean – Review Information
- Dispersion: Multidimensional Variables – Review Information
- Distance Metrics – Review Information
- Normalisation – Review Information
- Outliers – Review Information
- Basic Plotting – Review Information
- Storing 2D Coordinates – Review Information
- Multidimensional Mean – Review Information
- Adding Graphical Overlays – Review Information
- Calculating Distance – Review Information
- List Comprehension – Review Information
- Normalisation in Python – Review Information
- Outliers – Review Information
- Week 3 Summative Assessment
- Week 4: Introducing Pandas and Using K-Means to Analyse Data
- Week 4 Introduction
- 4.1: Using the Pandas Library to Read csv Files
- 4.1a: Sorting and Filtering Data Using Pandas
- 4.1b: Labelling Points on a Graph
- 4.1c: Labelling all the Points on a Graph
- 4.2: Eyeballing the Data
- 4.3: Using K-Means to Interpret the Data
- Week 4: Conclusion
- Week 4 Code Resources
- Pandas Read_CSV Function
- More Pandas Library Documentation
- The Pyplot Text Function
- For Loops in Python
- Documentation for sklearn.cluster.KMeans
- Using the Pandas Library to Read csv Files – Review Information
- Sorting and Filtering Data Using Pandas – Review Information
- Labelling Points on a Graph – Review Information
- Labelling all the Points on a Graph – Review Information
- Eyeballing the Data – Review Information
- Using K-Means to Interpret the Data – Review Information
- Week 4 Summative Assessment
- Week 5: A Data Clustering Project
- Introduction to Week 5
- 5.1 Can a Machine Detect Fake Notes?
- 5.2 Working for a Client
- 5.3 How to Organize Work on Your Project
- 5.4 Dealing With Difficulties
- 5.5 No Data no Data Science: Introduction of the Dataset
- 5.6 Modelling
- 5.7 Presenting the Project Results
- 5.8 Concluding Remarks
- Week 5 Code Resource – the Dataset for our Project
- Saving plt.scatter Outputs as Figures
- Additional Recommended Reading for Week 5
- How Would You Help? – Review Information
- Python – Review Information
- Week 5 Summative Assessment
Summary of User Reviews
Discover the power of k-means clustering in data science with this comprehensive course on Coursera. Gain practical skills in Python and learn how to implement k-means clustering algorithms for real-world applications. Highly recommended for anyone looking to advance their knowledge in data science.Key Aspect Users Liked About This Course
Many users found the course to be well-structured and easy to follow.Pros from User Reviews
- Clear and concise explanations of k-means clustering concepts.
- Hands-on exercises and projects to practice implementing k-means clustering algorithms.
- The course provides a good balance between theory and practical applications.
- Instructors are knowledgeable and responsive to questions.
- Excellent resource for beginners and intermediate learners in data science.
Cons from User Reviews
- Some users found the pace of the course to be too slow.
- The course could benefit from more advanced topics and applications of k-means clustering.
- No certification is offered upon completion of the course.
- Some users experienced technical difficulties with the online platform.
- The course may not be suitable for users looking for a deep dive into the mathematical underpinnings of k-means clustering.
Dr Matthew Yee-King
4.6 Raiting
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