About this Course
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Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Beginner Level

You will need mathematical and statistical knowledge and skills at least at high-school level.

Approx. 23 hours to complete

Suggested: 5 Weeks of study, 5-6 hours per week...

English

Subtitles: English

What you will learn

  • Check

    Define and explain the key concepts of data clustering

  • Check

    Demonstrate understanding of the key constructs and features of the Python language.

  • Check

    Implement in Python the principle steps of the K-means algorithm.

  • Check

    Design and execute a whole data clustering workflow and interpret the outputs.

Skills you will gain

K-Means ClusteringMachine LearningProgramming in Python

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Beginner Level

You will need mathematical and statistical knowledge and skills at least at high-school level.

Approx. 23 hours to complete

Suggested: 5 Weeks of study, 5-6 hours per week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
7 hours to complete

Week 1: Foundations of Data Science: K-Means Clustering in Python

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.

...
9 videos (Total 22 min), 4 quizzes
9 videos
Introduction to Data Science2m
What is Data?1m
Types of Data1m
Machine Learning3m
Supervised vs Unsupervised Learning2m
K-Means Clustering4m
Preparing your Data1m
A Real World Dataset53s
4 practice exercises
Types of Data – Review Information15m
Supervised vs Unsupervised – Review Information15m
K-Means Clustering – Review Information30m
Week 1 Summative Assessment40m
Week
2
4 hours to complete

Week 2: Means and Deviations in Mathematics and Python

...
11 videos (Total 37 min), 2 readings, 11 quizzes
11 videos
2.1 – Introduction to Mathematical Concepts of Data Clustering1m
2.2 – Mean of One Dimensional Lists2m
2.3 – Variance and Standard Deviation3m
2.4 Jupyter Notebooks6m
2.5 Variables4m
2.6 Lists4m
2.7 Computing the Mean3m
2.8 Better Lists: NumPy3m
2.9 Computing the Standard Deviation6m
Week 2 Conclusion31s
2 readings
Python Style Guide10m
Numpy and Array Creation20m
10 practice exercises
Population vs Sample – Review Information5m
Mean of One Dimensional Lists – Review Information3m
Variance and Standard Deviation – Review Information4m
Jupyter Notebooks – Review Information20m
Variables – Review Information10m
Lists – Review Information10m
Computing the Mean – Review Information10m
Better Lists – Review Information10m
Computing the Standard Deviation – Review Information10m
Week 2 Summative Assessment40m
Week
3
3 hours to complete

Week 3: Moving from One to Two Dimensional Data

...
16 videos (Total 53 min), 3 readings, 15 quizzes
16 videos
3.1 Multidimensional Data Points and Features2m
3.2 Multidimensional Mean2m
3.3 Dispersion: Multidimensional Variables3m
3.4 Distance Metrics5m
3.5 Normalisation1m
3.6 Outliers1m
3.7 Basic Plotting2m
3.7a Storing 2D Coordinates in a Single Data Structure6m
3.8 Multidimensional Mean4m
3.9 Adding Graphical Overlays5m
3.10 Calculating the Distance to the Mean3m
3.11 List Comprehension3m
3.12 Normalisation in Python5m
3.13 Outliers and Plotting Normalised Data2m
Week 3 Conclusion30s
3 readings
Matplotlib Scatter Plot Documentation20m
Matplotlib Patches Documentation10m
List Comprehension Documentation20m
15 practice exercises
Multidimensional Data Points and Features – Review Information3m
Multidimensional Mean – Review Information3m
Dispersion: Multidimensional Variables – Review Information5m
Distance Metrics – Review Information6m
Normalisation – Review Information3m
Outliers – Review Information4m
Basic Plotting – Review Information5m
Storing 2D Coordinates – Review Information4m
Multidimensional Mean – Review Information4m
Adding Graphical Overlays – Review Information6m
Calculating Distance – Review Information6m
List Comprehension – Review Information4m
Normalisation in Python – Review Information4m
Outliers – Review Information2m
Week 3 Summative Assessment25m
Week
4
5 hours to complete

Week 4: Introducing Pandas and Using K-Means to Analyse Data

...
8 videos (Total 37 min), 6 readings, 8 quizzes
8 videos
4.1: Using the Pandas Library to Read csv Files5m
4.1a: Sorting and Filtering Data Using Pandas8m
4.1b: Labelling Points on a Graph4m
4.1c: Labelling all the Points on a Graph3m
4.2: Eyeballing the Data5m
4.3: Using K-Means to Interpret the Data8m
Week 4: Conclusion35s
6 readings
Week 4 Code Resources5m
Pandas Read_CSV Function15m
More Pandas Library Documentation10m
The Pyplot Text Function10m
For Loops in Python10m
Documentation for sklearn.cluster.KMeans10m
7 practice exercises
Using the Pandas Library to Read csv Files – Review Information5m
Sorting and Filtering Data Using Pandas – Review Information10m
Labelling Points on a Graph – Review Information5m
Labelling all the Points on a Graph – Review Information5m
Eyeballing the Data – Review Information5m
Using K-Means to Interpret the Data – Review Information5m
Week 4 Summative Assessment40m
5.0
1 ReviewsChevron Right

Top reviews from Foundations of Data Science: K-Means Clustering in Python

By AAJun 4th 2019

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

Instructors

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Dr Matthew Yee-King

Lecturer
Computing Department, Goldsmiths, University of London
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Dr Betty Fyn-Sydney

Lecturer in Mathematics
Department of Computing, Goldsmiths, University of London
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Dr Jamie A Ward

Lecturer in Computer Science
Department of Computing, Goldsmiths, University of London
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Dr Larisa Soldatova

Reader in Data Science
Department of Computing, Goldsmiths, University of London

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Frequently Asked Questions

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  • When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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