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⇱ Statistics and Clustering in Python | Coursera

Statistics and Clustering in Python

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Statistics and Clustering in Python

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Gain insight into a topic and learn the fundamentals.
4.5

22 reviews

Beginner level
No prior experience required
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.5

22 reviews

Beginner level
No prior experience required
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • In this course you will engage in a variety of mathematical and programming exercises while completing a data clustering project.

Details to know

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Assessments

34 assignments¹

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Data Science Foundations Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This course is the sixth of eight courses. This project provides an in-depth exploration of key Data Science concepts focusing on algorithm design. It enhances essential mathematics, statistics, and programming skills required for common data analysis tasks. You will engage in a variety of mathematical and programming exercises while completing a data clustering project using the K-means algorithm on a provided dataset.

This week, we will delve into the core concepts of mean, variance, and other basic statistics, laying the groundwork for a solid understanding of data analysis principles. Through hands-on exercises and demonstrations in Python and Jupyter notebooks, we'll explore practical techniques for calculating and interpreting statistical measures.

What's included

10 videos7 readings10 assignments1 peer review1 ungraded lab

10 videos•Total 38 minutes
  • Introduction to this course in the specialisation•2 minutes
  • Introduction to Mathematical Concepts of Data Clustering•2 minutes
  • Mean of One Dimensional Lists•2 minutes
  • Variance and Standard Deviation•4 minutes
  • Jupyter Notebooks•6 minutes
  • Variables•4 minutes
  • Lists•5 minutes
  • Computing the Mean•3 minutes
  • Better Lists: NumPy•4 minutes
  • Computing the Standard Deviation•6 minutes
7 readings•Total 75 minutes
  • Course Syllabus•10 minutes
  • Getting ready for this course•10 minutes
  • Population vs Sample, Bias•10 minutes
  • Variability, Standard Deviation and Bias•10 minutes
  • How to back-up your virtual lab work•5 minutes
  • Python Style Guide•10 minutes
  • Numpy and Array Creation•20 minutes
10 assignments•Total 84 minutes
  • Population vs Sample – Review Information •10 minutes
  • Mean of One-Dimensional Lists – Review Information•3 minutes
  • Variance and Standard Deviation – Review Information•3 minutes
  • Jupyter Notebooks – Review Information•5 minutes
  • Variables – Review Information •5 minutes
  • Lists – Review Information •5 minutes
  • Computing the Mean – Review Information•3 minutes
  • Better Lists – Review Information •5 minutes
  • Computing the Standard Deviation – Review Information•5 minutes
  • Week 1 Summative Assessment•40 minutes
1 peer review•Total 30 minutes
  • Use Jupyter Notebooks•30 minutes
1 ungraded lab•Total 15 minutes
  • Jupyter Notebook Environment•15 minutes

This week, we will explore mathematics for multidimensional data. You will also learn how to work with multidimensional data in Python.

What's included

14 videos10 readings14 assignments

14 videos•Total 52 minutes
  • Multidimensional Data Points and Features•2 minutes
  • Multidimensional Mean•3 minutes
  • Dispersion: Multidimensional Variables•3 minutes
  • Distance Metrics•5 minutes
  • Normalisation•1 minute
  • Outliers•1 minute
  • Basic Plotting•3 minutes
  • Storing 2D Coordinates in a Single Data Structure•6 minutes
  • Multidimensional Mean•5 minutes
  • Adding Graphical Overlays•6 minutes
  • Calculating the Distance to the Mean•4 minutes
  • List Comprehension•4 minutes
  • Normalisation in Python•6 minutes
  • Outliers and Plotting Normalised Data•3 minutes
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
  • Errata•10 minutes
14 assignments•Total 110 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•10 minutes
  • Normalisation – Review Information•5 minutes
  • Outliers – Review Information•5 minutes
  • Basic Plotting – Review Information•6 minutes
  • Storing 2D Coordinates – Review Information•5 minutes
  • Multidimensional Mean – Review Information•5 minutes
  • Adding Graphical Overlays – Review Information•10 minutes
  • Calculating Distance – Review Information•5 minutes
  • Normalisation in Python – Review Information•5 minutes
  • Outliers – Review Information•3 minutes
  • Week 2 Summative Assessment•40 minutes

This week, we will explore data manipulation and visualisation with Python's Pandas library. We will dive deep into the versatile capabilities of Pandas, empowering you to efficiently manipulate, analyse, and interpret data.

What's included

6 videos6 readings7 assignments1 peer review

6 videos•Total 36 minutes
  • Using the Pandas Library to Read csv Files•5 minutes
  • Sorting and Filtering Data Using Pandas•8 minutes
  • Labelling Points on a Graph•4 minutes
  • Labelling all the Points on a Graph•3 minutes
  • Eyeballing the Data•6 minutes
  • Using K-Means to Interpret the Data•9 minutes
6 readings•Total 60 minutes
  • 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 68 minutes
  • Using the Pandas Library to Read csv Files – Review Information•5 minutes
  • Sorting and Filtering Data Using Pandas – Review Information•5 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•3 minutes
  • Week 3 Summative Assessment•40 minutes
1 peer review•Total 60 minutes
  • Create a Labelled Plot of the Happiness Data•60 minutes

This week, we will embark on a journey through the fascinating world of unsupervised learning, where patterns emerge from data without explicit guidance. You will implement the K-means algorithm to solve a real-world problem.

What's included

8 videos3 readings3 assignments3 peer reviews5 discussion prompts

8 videos•Total 28 minutes
  • Can a Machine Detect Fake Notes?•2 minutes
  • Working for a Client•5 minutes
  • How to Organize Work on Your Project•4 minutes
  • Dealing With Difficulties•3 minutes
  • No Data no Data Science: Introduction of the Dataset•5 minutes
  • Modelling•5 minutes
  • Presenting the Project Results•3 minutes
  • End of course•1 minute
3 readings•Total 25 minutes
  • Week 4 Code Resource – the Dataset for our Project•10 minutes
  • Saving plt.scatter Outputs as Figures•10 minutes
  • Additional Recommended Reading for Week 4•5 minutes
3 assignments•Total 22 minutes
  • How Would You Help? – Review Information•2 minutes
  • Python – Review Information•5 minutes
  • Week 4 Summative Assessment •15 minutes
3 peer reviews•Total 180 minutes
  • Exploratory Data Analysis•60 minutes
  • Clustering•60 minutes
  • Your Report•60 minutes
5 discussion prompts•Total 70 minutes
  • What Is Required to Train a Machine to Detect Fake Notes?•10 minutes
  • Your Project Plan•30 minutes
  • Self-reflection•10 minutes
  • Tips for Other Learners•10 minutes
  • Do You have Data Science Plans?•10 minutes

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Instructor

University of London
5 Courses•20,995 learners

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.