John Hopkins University - Foundations of Data Science: K-Means Clustering in Python
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Foundations of Data Science: K-Means Clustering in Python at Coursera Overview
Duration | 29 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Foundations of Data Science: K-Means Clustering in Python at Coursera Highlights
- Earn a shareable certificate upon completion.
- Flexible deadlines according to your schedule.
- Earn a certificate from the University of London upon completion of course.
Foundations of Data Science: K-Means Clustering in Python at Coursera Course details
- 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.
- This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses. It focuses on the basic mathematics, statistics and programming skills that are necessary for typical data analysis tasks.
- You will consider these fundamental concepts on an example data clustering task, and you will use this example to learn basic programming skills that are necessary for mastering Data Science techniques. During the course, you will be asked to do a series of mathematical and programming exercises and a small data clustering project for a given dataset.
Foundations of Data Science: K-Means Clustering in Python at Coursera Curriculum
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
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