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Foundations of Data Science: K-Means Clustering in Python
Beginner
Online
3 Weeks
Free Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 2436 | yes | Coursera |
The Syllabus
Videos
- 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
Practice Exercise
- Types of Data – Review Information
- Supervised vs Unsupervised – Review Information
- K-Means Clustering – Review Information
- Week 1 Summative Assessment
Videos
- 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
Readings
- Population vs Sample, Bias
- Variability, Standard Deviation and Bias
- Python Style Guide
- Numpy and Array Creation
Practice Exercise
- 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
Videos
- 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
Readings
- 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
Practice Exercise
- 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
Videos
- 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
Readings
- 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
Practice Exercise
- 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
Videos
- 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
Readings
- Week 5 Code Resource – the Dataset for our Project
- Saving plt.scatter Outputs as Figures
- Additional Recommended Reading for Week 5
Practice Exercise
- How Would You Help? – Review Information
- Python – Review Information
- Week 5 Summative Assessment