Foundations of Data Science: K-Means Clustering in Python

Foundations of Data Science: K-Means Clustering in Python

Instructors: Professor Matthew Yee-King

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5 modules

Gain insight into a topic and learn the fundamentals.

734 reviews

Beginner level

Recommended experience

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

95%

Most learners liked this course

5 modules

Gain insight into a topic and learn the fundamentals.

734 reviews

Beginner level

Recommended experience

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

95%

Most learners liked this course

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

Tools you'll learn

Details to know

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Assessments

39 assignments¹

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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 videos Total 22 minutes

Welcome and Introduction 3 minutes
Introduction to Data Science 3 minutes
What is Data? 2 minutes
Types of Data 1 minute
Machine Learning 4 minutes
Supervised vs Unsupervised Learning 3 minutes
K-Means Clustering 4 minutes
Preparing your Data 2 minutes
A Real World Dataset 1 minute

4 assignments Total 100 minutes

Week 1 Summative Assessment 40 minutes
Types of Data – Review Information 15 minutes
Supervised vs Unsupervised – Review Information 15 minutes
K-Means Clustering – Review Information 30 minutes

3 discussion prompts Total 270 minutes

Welcome! 30 minutes
Examples of Data 120 minutes
Machine Learning in the News 120 minutes

What's included

11 videos4 readings10 assignments1 peer review1 ungraded lab

11 videos Total 37 minutes

2.0: Week 2 Introduction 1 minute
2.1 – Introduction to Mathematical Concepts of Data Clustering 2 minutes
2.2 – Mean of One Dimensional Lists 2 minutes
2.3 – Variance and Standard Deviation 4 minutes
2.4 Jupyter Notebooks 6 minutes
2.5 Variables 4 minutes
2.6 Lists 5 minutes
2.7 Computing the Mean 3 minutes
2.8 Better Lists: NumPy 4 minutes
2.9 Computing the Standard Deviation 6 minutes
Week 2 Conclusion 1 minute

4 readings Total 50 minutes

Population vs Sample, Bias 10 minutes
Variability, Standard Deviation and Bias 10 minutes
Python Style Guide 10 minutes
Numpy and Array Creation 20 minutes

10 assignments Total 122 minutes

Week 2 Summative Assessment 40 minutes
Population vs Sample – Review Information 5 minutes
Mean of One Dimensional Lists – Review Information 3 minutes
Variance and Standard Deviation – Review Information 4 minutes
Jupyter Notebooks – Review Information 20 minutes
Variables – Review Information 10 minutes
Lists – Review Information 10 minutes
Computing the Mean – Review Information 10 minutes
Better Lists – Review Information 10 minutes
Computing the Standard Deviation – Review Information 10 minutes

1 peer review Total 30 minutes

Use Jupyter Notebooks 30 minutes

1 ungraded lab Total 15 minutes

Jupyter Notebook Environment 15 minutes

What's included

16 videos10 readings15 assignments

16 videos Total 53 minutes

Week 3 Introduction 1 minute
3.1 Multidimensional Data Points and Features 2 minutes
3.2 Multidimensional Mean 3 minutes
3.3 Dispersion: Multidimensional Variables 3 minutes
3.4 Distance Metrics 5 minutes
3.5 Normalisation 1 minute
3.6 Outliers 1 minute
3.7 Basic Plotting 3 minutes
3.7a Storing 2D Coordinates in a Single Data Structure 6 minutes
3.8 Multidimensional Mean 5 minutes
3.9 Adding Graphical Overlays 6 minutes
3.10 Calculating the Distance to the Mean 4 minutes
3.11 List Comprehension 4 minutes
3.12 Normalisation in Python 6 minutes
3.13 Outliers and Plotting Normalised Data 3 minutes
Week 3 Conclusion 1 minute

10 readings Total 120 minutes

Multidimensional Data Points and Features Recap 10 minutes
Multidimensional Mean Recap 10 minutes
Multidimensional Variables Recap 10 minutes
Distance Metrics Recap 10 minutes
Normalisation Recap 10 minutes
Note on Matplotlib 10 minutes
Matplotlib Scatter Plot Documentation 20 minutes
Matplotlib Patches Documentation 10 minutes
List Comprehension Documentation 20 minutes
3.12 Errata 10 minutes

15 assignments Total 290 minutes

Week 3 Summative Assessment 25 minutes
Multidimensional Data Points and Features – Review Information 3 minutes
Multidimensional Mean – Review Information 3 minutes
Dispersion: Multidimensional Variables – Review Information 5 minutes
Distance Metrics – Review Information 6 minutes
Normalisation – Review Information 3 minutes
Outliers – Review Information 30 minutes
Basic Plotting – Review Information 5 minutes
Storing 2D Coordinates – Review Information 30 minutes
Multidimensional Mean – Review Information 30 minutes
Adding Graphical Overlays – Review Information 30 minutes
Calculating Distance – Review Information 30 minutes
List Comprehension – Review Information 30 minutes
Normalisation in Python – Review Information 30 minutes
Outliers – Review Information 30 minutes

What's included

8 videos6 readings7 assignments1 peer review

8 videos Total 37 minutes

Week 4 Introduction 1 minute
4.1: Using the Pandas Library to Read csv Files 5 minutes
4.1a: Sorting and Filtering Data Using Pandas 8 minutes
4.1b: Labelling Points on a Graph 4 minutes
4.1c: Labelling all the Points on a Graph 3 minutes
4.2: Eyeballing the Data 6 minutes
4.3: Using K-Means to Interpret the Data 9 minutes
Week 4: Conclusion 1 minute

6 readings Total 60 minutes

Week 4 Code Resources 5 minutes
Pandas Read_CSV Function 15 minutes
More Pandas Library Documentation 10 minutes
The Pyplot Text Function 10 minutes
For Loops in Python 10 minutes
Documentation for sklearn.cluster.KMeans 10 minutes

7 assignments Total 75 minutes

Week 4 Summative Assessment 40 minutes
Using the Pandas Library to Read csv Files – Review Information 5 minutes
Sorting and Filtering Data Using Pandas – Review Information 10 minutes
Labelling Points on a Graph – Review Information 5 minutes
Labelling all the Points on a Graph – Review Information 5 minutes
Eyeballing the Data – Review Information 5 minutes
Using K-Means to Interpret the Data – Review Information 5 minutes

1 peer review Total 60 minutes

Create a Labelled Plot of the Happiness Data 60 minutes

What's included

9 videos3 readings3 assignments3 peer reviews5 discussion prompts

9 videos Total 30 minutes

Introduction to Week 5 1 minute
5.1 Can a Machine Detect Fake Notes? 2 minutes
5.2 Working for a Client 5 minutes
5.3 How to Organize Work on Your Project 4 minutes
5.4 Dealing With Difficulties 3 minutes
5.5 No Data no Data Science: Introduction of the Dataset 5 minutes
5.6 Modelling 5 minutes
5.7 Presenting the Project Results 3 minutes
5.8 Concluding Remarks 1 minute

3 readings Total 25 minutes

Week 5 Code Resource – the Dataset for our Project 10 minutes
Saving plt.scatter Outputs as Figures 10 minutes
Additional Recommended Reading for Week 5 5 minutes

3 assignments Total 44 minutes

Week 5 Summative Assessment 30 minutes
How Would You Help? – Review Information 10 minutes
Python – Review Information 4 minutes

3 peer reviews Total 180 minutes

Exploratory Data Analysis 60 minutes
Clustering 60 minutes
Your Report 60 minutes

5 discussion prompts Total 130 minutes

What Is Required to Train a Machine to Detect Fake Notes? 40 minutes
Your Project Plan 60 minutes
Self-reflection 10 minutes
Tips for Other Learners 10 minutes
Do You have Data Science Plans? 10 minutes

Instructors

Instructor ratings

(311 ratings)

Professor Matthew Yee-King

University of London

24 Courses 434,357 learners

Dr Betty Fyn-Sydney

University of London

1 Course 76,772 learners

Offered by

University of London

Goldsmiths, University of London

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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

Reviewed on Jun 29, 2020

A well presented and interesting course. It would have been good to have some more complex examples with the thinking behind them - the exploratory bit/intelligent bit of the process.

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