4.6
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512 ratings

Dr Matthew Yee-King +3 more instructors

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Offered By

University of London

Goldsmiths, University of London

Foundations of Data Science: K-Means Clustering in Python

University of London

About this Course

115,031 recent views

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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Beginner Level

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

Approx. 29 hours to complete

English

What you will 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 will gain

K-Means Clustering
Machine Learning
Programming in Python

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Beginner Level

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

Approx. 29 hours to complete

English

Instructors

Instructor rating

4.64/5 (199 Ratings)

Offered by

University of London

The University of London is a federal University which includes 17 world leading Colleges. With extensive experience in distance learning since 1858, University of London has enriched the lives of thousands of students, delivering high quality degrees across the globe. Today, University of London is a global leader in flexible study, offering degree programmes to over 45,000 students in over 190 countries, delivering world-leading research across the world. To find out more about University of London, visit www.london.ac.uk

Goldsmiths, University of London

Championing research-rich degrees that provoke thought, stretch the imagination and tap into tomorrow’s world, at Goldsmiths we’re asking the questions that matter now in subjects as diverse as the arts and humanities, social sciences, cultural studies, computing, and entrepreneurial business and management. We are a community defined by its people: innovative in spirit, analytical in approach and open to all.

Syllabus - What you will learn from this course

Content Rating95%(11,906 ratings)

Week

Week 1

7 hours to complete

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

7 hours to complete

9 videos (Total 22 min)

Week

Week 2

4 hours to complete

Week 2: Means and Deviations in Mathematics and Python

4 hours to complete

11 videos (Total 37 min), 4 readings, 11 quizzes

11 videos

2.0: Week 2 Introduction35s

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

4 readings

Population vs Sample, Bias10m

Variability, Standard Deviation and Bias10m

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

Week 3

8 hours to complete

Week 3: Moving from One to Two Dimensional Data

8 hours to complete

16 videos (Total 53 min), 10 readings, 15 quizzes

16 videos

Week 3 Introduction37s

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

10 readings

Multidimensional Data Points and Features Recap10m

Multidimensional Mean Recap10m

Multidimensional Variables Recap10m

Distance Metrics Recap10m

Normalisation Recap10m

Note on Matplotlib10m

Matplotlib Scatter Plot Documentation20m

Matplotlib Patches Documentation10m

List Comprehension Documentation20m

3.12 Errata10m

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 Information30m

Basic Plotting – Review Information5m

Storing 2D Coordinates – Review Information30m

Multidimensional Mean – Review Information30m

Adding Graphical Overlays – Review Information30m

Calculating Distance – Review Information30m

List Comprehension – Review Information30m

Normalisation in Python – Review Information30m

Outliers – Review Information30m

Week 3 Summative Assessment25m

Week

Week 4

4 hours to complete

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

4 hours to complete

8 videos (Total 37 min), 6 readings, 8 quizzes

8 videos

Week 4 Introduction50s

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

Reviews

4.6

162 reviews

5 stars
72.65%
4 stars
21.48%
3 stars
3.32%
2 stars
1.36%
1 star
1.17%

TOP REVIEWS FROM FOUNDATIONS OF DATA SCIENCE: K-MEANS CLUSTERING IN PYTHON

by SSApr 30, 2020

It is a very detailed and well planned course. However, there could have been a few lectures at the end on training set, testing set etc.

by CKMay 30, 2020

It was a great journey to get through it. Thanks a lot to all the instructors for their valuable job and effort :)

by MPJan 2, 2022

This course was excellent. Though I already knew the concepts in this course and the programing skills, it has inspired me to 100% take the MSC provided. Excellent lectures. Just Excellent.

by NCMay 16, 2020

This course gives us a good balance between theory and practice. I wish there was an intermediate or advanced level to continue.

View all reviews

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I purchase the Certificate?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Foundations of Data Science: K-Means Clustering in Python

Foundations of Data Science: K-Means Clustering in Python

About this Course

What you will learn

Skills you will gain

Instructors

Dr Matthew Yee-King

Dr Betty Fyn-Sydney

Dr Jamie A Ward

Dr Larisa Soldatova

Offered by

University of London

Goldsmiths, University of London

Syllabus - What you will learn from this course

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

Week 2: Means and Deviations in Mathematics and Python

Week 3: Moving from One to Two Dimensional Data

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

Week 5: A Data Clustering Project

Reviews

TOP REVIEWS FROM FOUNDATIONS OF DATA SCIENCE: K-MEANS CLUSTERING IN PYTHON

Frequently Asked Questions

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Foundations of Data Science: K-Means Clustering in Python

Foundations of Data Science: K-Means Clustering in Python

About this Course

What you will learn

Skills you will gain

Instructors

Dr Matthew Yee-King

Dr Betty Fyn-Sydney

Dr Jamie A Ward

Dr Larisa Soldatova

Offered by

University of London

Goldsmiths, University of London

Syllabus - What you will learn from this course

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

Week 2: Means and Deviations in Mathematics and Python

Week 3: Moving from One to Two Dimensional Data

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

Week 5: A Data Clustering Project

Reviews

TOP REVIEWS FROM FOUNDATIONS OF DATA SCIENCE: K-MEANS CLUSTERING IN PYTHON

Frequently Asked Questions

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Start or advance your career

Popular Courses and Certifications

Popular collections and articles

Earn a degree or certificate online

Coursera

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