Foundations of Data Science: K-Means Clustering in Python
Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.
Foundations of Data Science: K-Means Clustering in Python
76,961 already enrolled
Included with
Ask Coursera
735 reviews
Recommended experience
735 reviews
Recommended experience
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
See how employees at top companies are mastering in-demand skills
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
Explore more from Machine Learning
- Status: Free TrialU
University of London
Course
- Status: Free Trial
Course
- Status: PreviewU
University of Leeds
Course
- Status: Free Trial
Specialization
Why people choose Coursera for their career
Learner reviews
- 5 stars
72.65%
- 4 stars
20%
- 3 stars
4.35%
- 2 stars
1.08%
- 1 star
1.90%
Showing 3 of 735
Reviewed on Jan 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.
Reviewed on Jul 26, 2021
Very informative course. You can learn how to cluster and clisfy data and how to write down report on the given statistical analysis.
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
Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. 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.
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.
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
Financial aid available,
¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.