Foundations of Data Science: K-Means Clustering in Python

Instructors: Dr Matthew Yee-King

What you'll 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'll gain

Details to know

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Assessments

39 assignments

Taught in English

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There are 5 modules in this course

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.

This week we will introduce you to the course and to the team who will be guiding you through the course over the next 5 weeks. The aim of this week's material is to gently introduce you to Data Science through some real-world examples of where Data Science is used, and also by highlighting some of the main concepts involved.

What's included

9 videos4 assignments3 discussion prompts

9 videosTotal 21 minutes

Welcome and Introduction2 minutesPreview module
Introduction to Data Science2 minutes
What is Data?1 minute
Types of Data1 minute
Machine Learning3 minutes
Supervised vs Unsupervised Learning2 minutes
K-Means Clustering4 minutes
Preparing your Data1 minute
A Real World Dataset0 minutes

4 assignmentsTotal 100 minutes

Types of Data – Review Information15 minutes
Supervised vs Unsupervised – Review Information15 minutes
K-Means Clustering – Review Information30 minutes
Week 1 Summative Assessment40 minutes

3 discussion promptsTotal 270 minutes

Welcome!30 minutes
Examples of Data120 minutes
Machine Learning in the News120 minutes

What's included

11 videos4 readings10 assignments1 peer review1 ungraded lab

11 videosTotal 36 minutes

2.0: Week 2 Introduction0 minutesPreview module
2.1 – Introduction to Mathematical Concepts of Data Clustering1 minute
2.2 – Mean of One Dimensional Lists2 minutes
2.3 – Variance and Standard Deviation3 minutes
2.4 Jupyter Notebooks6 minutes
2.5 Variables4 minutes
2.6 Lists4 minutes
2.7 Computing the Mean3 minutes
2.8 Better Lists: NumPy3 minutes
2.9 Computing the Standard Deviation6 minutes
Week 2 Conclusion0 minutes

4 readingsTotal 50 minutes

Population vs Sample, Bias10 minutes
Variability, Standard Deviation and Bias10 minutes
Python Style Guide10 minutes
Numpy and Array Creation20 minutes

10 assignmentsTotal 122 minutes

Population vs Sample – Review Information5 minutes
Mean of One Dimensional Lists – Review Information3 minutes
Variance and Standard Deviation – Review Information4 minutes
Jupyter Notebooks – Review Information20 minutes
Variables – Review Information10 minutes
Lists – Review Information10 minutes
Computing the Mean – Review Information10 minutes
Better Lists – Review Information10 minutes
Computing the Standard Deviation – Review Information10 minutes
Week 2 Summative Assessment40 minutes

1 peer reviewTotal 30 minutes

Use Jupyter Notebooks30 minutes

1 ungraded labTotal 15 minutes

Jupyter Notebook Environment15 minutes

What's included

16 videos10 readings15 assignments

16 videosTotal 52 minutes

Week 3 Introduction0 minutesPreview module
3.1 Multidimensional Data Points and Features2 minutes
3.2 Multidimensional Mean2 minutes
3.3 Dispersion: Multidimensional Variables3 minutes
3.4 Distance Metrics5 minutes
3.5 Normalisation1 minute
3.6 Outliers1 minute
3.7 Basic Plotting2 minutes
3.7a Storing 2D Coordinates in a Single Data Structure6 minutes
3.8 Multidimensional Mean4 minutes
3.9 Adding Graphical Overlays5 minutes
3.10 Calculating the Distance to the Mean3 minutes
3.11 List Comprehension3 minutes
3.12 Normalisation in Python5 minutes
3.13 Outliers and Plotting Normalised Data2 minutes
Week 3 Conclusion0 minutes

10 readingsTotal 120 minutes

Multidimensional Data Points and Features Recap10 minutes
Multidimensional Mean Recap10 minutes
Multidimensional Variables Recap10 minutes
Distance Metrics Recap10 minutes
Normalisation Recap10 minutes
Note on Matplotlib10 minutes
Matplotlib Scatter Plot Documentation20 minutes
Matplotlib Patches Documentation10 minutes
List Comprehension Documentation20 minutes
3.12 Errata10 minutes

15 assignmentsTotal 290 minutes

Multidimensional Data Points and Features – Review Information3 minutes
Multidimensional Mean – Review Information3 minutes
Dispersion: Multidimensional Variables – Review Information5 minutes
Distance Metrics – Review Information6 minutes
Normalisation – Review Information3 minutes
Outliers – Review Information30 minutes
Basic Plotting – Review Information5 minutes
Storing 2D Coordinates – Review Information30 minutes
Multidimensional Mean – Review Information30 minutes
Adding Graphical Overlays – Review Information30 minutes
Calculating Distance – Review Information30 minutes
List Comprehension – Review Information30 minutes
Normalisation in Python – Review Information30 minutes
Outliers – Review Information30 minutes
Week 3 Summative Assessment25 minutes

What's included

8 videos6 readings7 assignments1 peer review

8 videosTotal 36 minutes

Week 4 Introduction0 minutesPreview module
4.1: Using the Pandas Library to Read csv Files5 minutes
4.1a: Sorting and Filtering Data Using Pandas8 minutes
4.1b: Labelling Points on a Graph4 minutes
4.1c: Labelling all the Points on a Graph3 minutes
4.2: Eyeballing the Data5 minutes
4.3: Using K-Means to Interpret the Data8 minutes
Week 4: Conclusion0 minutes

6 readingsTotal 60 minutes

Week 4 Code Resources5 minutes
Pandas Read_CSV Function15 minutes
More Pandas Library Documentation10 minutes
The Pyplot Text Function10 minutes
For Loops in Python10 minutes
Documentation for sklearn.cluster.KMeans10 minutes

7 assignmentsTotal 75 minutes

Using the Pandas Library to Read csv Files – Review Information5 minutes
Sorting and Filtering Data Using Pandas – Review Information10 minutes
Labelling Points on a Graph – Review Information5 minutes
Labelling all the Points on a Graph – Review Information5 minutes
Eyeballing the Data – Review Information5 minutes
Using K-Means to Interpret the Data – Review Information5 minutes
Week 4 Summative Assessment40 minutes

1 peer reviewTotal 60 minutes

Create a Labelled Plot of the Happiness Data60 minutes

What's included

9 videos3 readings3 assignments3 peer reviews5 discussion prompts

9 videosTotal 29 minutes

Introduction to Week 51 minutePreview module
5.1 Can a Machine Detect Fake Notes?1 minute
5.2 Working for a Client4 minutes
5.3 How to Organize Work on Your Project3 minutes
5.4 Dealing With Difficulties3 minutes
5.5 No Data no Data Science: Introduction of the Dataset4 minutes
5.6 Modelling4 minutes
5.7 Presenting the Project Results3 minutes
5.8 Concluding Remarks1 minute

3 readingsTotal 25 minutes

Week 5 Code Resource – the Dataset for our Project10 minutes
Saving plt.scatter Outputs as Figures10 minutes
Additional Recommended Reading for Week 55 minutes

3 assignmentsTotal 44 minutes

How Would You Help? – Review Information10 minutes
Python – Review Information4 minutes
Week 5 Summative Assessment30 minutes

3 peer reviewsTotal 180 minutes

Exploratory Data Analysis60 minutes
Clustering60 minutes
Your Report60 minutes

5 discussion promptsTotal 130 minutes

What Is Required to Train a Machine to Detect Fake Notes?40 minutes
Your Project Plan60 minutes
Self-reflection10 minutes
Tips for Other Learners10 minutes
Do You have Data Science Plans?10 minutes

Instructors

Instructor ratings

4.6 (295 ratings)

Dr Matthew Yee-King

University of London

21 Courses413,666 learners

Dr Betty Fyn-Sydney

University of London

1 Course72,715 learners

Offered by

University of London

Goldsmiths, University of London

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4.6

695 reviews

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Reviewed on Sep 12, 2020

It was a well-taught course. I felt that during the final project-making, students were not spoon-fed, instead, pushed us to become more creative.

Reviewed on Jun 28, 2020

Very interesting course! The lecturers explain concepts thoroughly which makes the concepts easy to understand even for people without much knowledge in Data Science

Reviewed on Jun 3, 2019

This course is at right level for a beginner (python and analytics) while going into details around K means clustering

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