4.6
stars
524 ratings

Dr Matthew Yee-King +3 more instructors

47,609 already enrolled

Offered By

University of London

Goldsmiths, University of London

Foundations of Data Science: K-Means Clustering in Python

University of London

About this Course

169,461 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.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

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.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

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 (207 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%(12,790 ratings)

Week1

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)

Week2

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 Introduction35s2.1 – Introduction to Mathematical Concepts of Data Clustering1m2.2 – Mean of One Dimensional Lists2m2.3 – Variance and Standard Deviation3m2.4 Jupyter Notebooks6m2.5 Variables4m2.6 Lists4m2.7 Computing the Mean3m2.8 Better Lists: NumPy3m2.9 Computing the Standard Deviation6mWeek 2 Conclusion31s

4 readings

Population vs Sample, Bias10mVariability, Standard Deviation and Bias10mPython Style Guide10mNumpy and Array Creation20m

10 practice exercises

Population vs Sample – Review Information5mMean of One Dimensional Lists – Review Information3mVariance and Standard Deviation – Review Information4mJupyter Notebooks – Review Information20mVariables – Review Information10mLists – Review Information10mComputing the Mean – Review Information10mBetter Lists – Review Information10mComputing the Standard Deviation – Review Information10mWeek 2 Summative Assessment40m

Week3

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 Introduction37s3.1 Multidimensional Data Points and Features2m3.2 Multidimensional Mean2m3.3 Dispersion: Multidimensional Variables3m3.4 Distance Metrics5m3.5 Normalisation1m3.6 Outliers1m3.7 Basic Plotting2m3.7a Storing 2D Coordinates in a Single Data Structure6m3.8 Multidimensional Mean4m3.9 Adding Graphical Overlays5m3.10 Calculating the Distance to the Mean3m3.11 List Comprehension3m3.12 Normalisation in Python5m3.13 Outliers and Plotting Normalised Data2mWeek 3 Conclusion30s

10 readings

Multidimensional Data Points and Features Recap10mMultidimensional Mean Recap10mMultidimensional Variables Recap10mDistance Metrics Recap10mNormalisation Recap10mNote on Matplotlib10mMatplotlib Scatter Plot Documentation20mMatplotlib Patches Documentation10mList Comprehension Documentation20m3.12 Errata10m

15 practice exercises

Multidimensional Data Points and Features – Review Information3mMultidimensional Mean – Review Information3mDispersion: Multidimensional Variables – Review Information5mDistance Metrics – Review Information6mNormalisation – Review Information3mOutliers – Review Information30mBasic Plotting – Review Information5mStoring 2D Coordinates – Review Information30mMultidimensional Mean – Review Information30mAdding Graphical Overlays – Review Information30mCalculating Distance – Review Information30mList Comprehension – Review Information30mNormalisation in Python – Review Information30mOutliers – Review Information30mWeek 3 Summative Assessment25m

Week4

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 Introduction50s4.1: Using the Pandas Library to Read csv Files5m4.1a: Sorting and Filtering Data Using Pandas8m4.1b: Labelling Points on a Graph4m4.1c: Labelling all the Points on a Graph3m4.2: Eyeballing the Data5m4.3: Using K-Means to Interpret the Data8mWeek 4: Conclusion35s

6 readings

Week 4 Code Resources5mPandas Read_CSV Function15mMore Pandas Library Documentation10mThe Pyplot Text Function10mFor Loops in Python10mDocumentation for sklearn.cluster.KMeans10m

7 practice exercises

Using the Pandas Library to Read csv Files – Review Information5mSorting and Filtering Data Using Pandas – Review Information10mLabelling Points on a Graph – Review Information5mLabelling all the Points on a Graph – Review Information5mEyeballing the Data – Review Information5mUsing K-Means to Interpret the Data – Review Information5mWeek 4 Summative Assessment40m

Reviews

4.6

163 reviews

5 stars
72.57%
4 stars
21.14%
3 stars
3.61%
2 stars
1.33%
1 star
1.33%

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

by AAJun 3, 2019

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

by MPSep 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.

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.

by TFJul 11, 2022

I learnt alot, a very good foundation course. It made me have more interest in learning more in Data Science particularly using Python language

View all reviews

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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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Learn Something New

Popular Data Science Courses

Popular Computer Science & IT Courses

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Community

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