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Foundations of Data Science: K-Means Clustering in Python
  • Level Foundation
  • المدة 29 ساعات hours
  • الطبع بواسطة University of London
  • Offered by

عن

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.

الوحدات

Welcome and Introduction

    • 1 Videos
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1 Videos

  • Welcome and Introduction

Week 1 Introduction

    • 1 Videos
    • 1 Discussion
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1 Discussions

  • Welcome!

1 Videos

  • Introduction to Data Science

Data

    • 2 Videos
    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Types of Data – Review Information

1 Discussions

  • Examples of Data

2 Videos

  • What is Data?
  • Types of Data

Machine Learning

    • 1 Videos
    • 1 Discussion
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1 Discussions

  • Machine Learning in the News

1 Videos

  • Machine Learning

Supervised vs Unsupervised Learning

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Supervised vs Unsupervised – Review Information

1 Videos

  • Supervised vs Unsupervised Learning

Clustering with K-Means

    • 2 Videos
    • 1 Assignment
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1 Assignment

  • K-Means Clustering – Review Information

2 Videos

  • K-Means Clustering
  • Preparing your Data

Week 1 Outro and Assessment

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Week 1 Summative Assessment

1 Videos

  • A Real World Dataset

Week 2 Introduction

    • 1 Videos
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1 Videos

  • 2.0: Week 2 Introduction

Mean, Variance and Standard Deviation

    • 3 Videos
    • 2 Readings
    • 3 Assignment
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3 Assignment

  • Population vs Sample – Review Information
  • Mean of One Dimensional Lists – Review Information
  • Variance and Standard Deviation – Review Information

3 Videos

  • 2.1 – Introduction to Mathematical Concepts of Data Clustering
  • 2.2 – Mean of One Dimensional Lists
  • 2.3 – Variance and Standard Deviation

2 Readings

  • Population vs Sample, Bias
  • Variability, Standard Deviation and Bias

Development Environment: Jupyter Notebooks

    • 1 Videos
    • 1 PeerReview
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Jupyter Notebooks – Review Information

1 Peer Review

  • Use Jupyter Notebooks

1 Labs

  • Jupyter Notebook Environment

1 Videos

  • 2.4 Jupyter Notebooks

Computing Basic Statistics in Python

    • 5 Videos
    • 2 Readings
    • 5 Assignment
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5 Assignment

  • Variables – Review Information
  • Lists – Review Information
  • Computing the Mean – Review Information
  • Better Lists – Review Information
  • Computing the Standard Deviation – Review Information

5 Videos

  • 2.5 Variables
  • 2.6 Lists
  • 2.7 Computing the Mean
  • 2.8 Better Lists: NumPy
  • 2.9 Computing the Standard Deviation

2 Readings

  • Python Style Guide
  • Numpy and Array Creation

Week 2 Outro and Assessment

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Week 2 Summative Assessment

1 Videos

  • Week 2 Conclusion

Week 3 Introduction

    • 1 Videos
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1 Videos

  • Week 3 Introduction

Mathematics for Multidimensional Data

    • 6 Videos
    • 5 Readings
    • 6 Assignment
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6 Assignment

  • Multidimensional Data Points and Features – Review Information
  • Multidimensional Mean – Review Information
  • Dispersion: Multidimensional Variables – Review Information
  • Distance Metrics – Review Information
  • Normalisation – Review Information
  • Outliers – Review Information

6 Videos

  • 3.1 Multidimensional Data Points and Features
  • 3.2 Multidimensional Mean
  • 3.3 Dispersion: Multidimensional Variables
  • 3.4 Distance Metrics
  • 3.5 Normalisation
  • 3.6 Outliers

5 Readings

  • Multidimensional Data Points and Features Recap
  • Multidimensional Mean Recap
  • Multidimensional Variables Recap
  • Distance Metrics Recap
  • Normalisation Recap

Working with Multidimensional Data in Python

    • 8 Videos
    • 5 Readings
    • 8 Assignment
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8 Assignment

  • Basic Plotting – Review Information
  • Storing 2D Coordinates – Review Information
  • Multidimensional Mean – Review Information
  • Adding Graphical Overlays – Review Information
  • Calculating Distance – Review Information
  • List Comprehension – Review Information
  • Normalisation in Python – Review Information
  • Outliers – Review Information

8 Videos

  • 3.7 Basic Plotting
  • 3.7a Storing 2D Coordinates in a Single Data Structure
  • 3.8 Multidimensional Mean
  • 3.9 Adding Graphical Overlays
  • 3.10 Calculating the Distance to the Mean
  • 3.11 List Comprehension
  • 3.12 Normalisation in Python
  • 3.13 Outliers and Plotting Normalised Data

5 Readings

  • Note on Matplotlib
  • Matplotlib Scatter Plot Documentation
  • Matplotlib Patches Documentation
  • List Comprehension Documentation
  • 3.12 Errata

Week 3 Outro and Assessment

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Week 3 Summative Assessment

1 Videos

  • Week 3 Conclusion

Week 4 Introduction

    • 1 Videos
    • 1 Readings
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1 Videos

  • Week 4 Introduction

1 Readings

  • Week 4 Code Resources

Using the Pandas Library to Read, Sort and Filter Data

    • 2 Videos
    • 2 Readings
    • 2 Assignment
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2 Assignment

  • Using the Pandas Library to Read csv Files – Review Information
  • Sorting and Filtering Data Using Pandas – Review Information

2 Videos

  • 4.1: Using the Pandas Library to Read csv Files
  • 4.1a: Sorting and Filtering Data Using Pandas

2 Readings

  • Pandas Read_CSV Function
  • More Pandas Library Documentation

Plotting and Labelling the Data

    • 2 Videos
    • 2 Readings
    • 2 Assignment
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2 Assignment

  • Labelling Points on a Graph – Review Information
  • Labelling all the Points on a Graph – Review Information

2 Videos

  • 4.1b: Labelling Points on a Graph
  • 4.1c: Labelling all the Points on a Graph

2 Readings

  • The Pyplot Text Function
  • For Loops in Python

Interpreting the Data

    • 2 Videos
    • 1 Readings
    • 2 Assignment
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2 Assignment

  • Eyeballing the Data – Review Information
  • Using K-Means to Interpret the Data – Review Information

2 Videos

  • 4.2: Eyeballing the Data
  • 4.3: Using K-Means to Interpret the Data

1 Readings

  • Documentation for sklearn.cluster.KMeans

Week 4 Outro and Assessment

    • 1 Videos
    • 1 PeerReview
    • 1 Assignment
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1 Assignment

  • Week 4 Summative Assessment

1 Peer Review

  • Create a Labelled Plot of the Happiness Data

1 Videos

  • Week 4: Conclusion

Welcome and Introduction

    • 1 Videos
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1 Videos

  • Introduction to Week 5

Understanding Your Task

    • 2 Videos
    • 1 Discussion
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1 Discussions

  • What Is Required to Train a Machine to Detect Fake Notes?

2 Videos

  • 5.1 Can a Machine Detect Fake Notes?
  • 5.2 Working for a Client

Organising Your Work on a Data Science Project

    • 2 Videos
    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • How Would You Help? – Review Information

1 Discussions

  • Your Project Plan

2 Videos

  • 5.3 How to Organize Work on Your Project
  • 5.4 Dealing With Difficulties

Doing the Project

    • 2 Videos
    • 1 Readings
    • 2 PeerReview
    • 1 Assignment
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1 Assignment

  • Python – Review Information

2 Peer Review

  • Exploratory Data Analysis
  • Clustering

2 Videos

  • 5.5 No Data no Data Science: Introduction of the Dataset
  • 5.6 Modelling

1 Readings

  • Week 5 Code Resource – the Dataset for our Project

Presenting the Results of Your Project

    • 1 Videos
    • 1 Readings
    • 1 PeerReview
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1 Peer Review

  • Your Report

1 Videos

  • 5.7 Presenting the Project Results

1 Readings

  • Saving plt.scatter Outputs as Figures

Week 5 Outro and Assessment

    • 1 Videos
    • 1 Readings
    • 3 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Week 5 Summative Assessment

3 Discussions

  • Self-reflection
  • Tips for Other Learners
  • Do You have Data Science Plans?

1 Videos

  • 5.8 Concluding Remarks

1 Readings

  • Additional Recommended Reading for Week 5

Auto Summary

Dive into the world of data science with the "Foundations of Data Science: K-Means Clustering in Python" course, brought to you by Coursera in collaboration with Goldsmiths, University of London. This foundational course is designed to quickly introduce you to the essential concepts of data science, making it a perfect starting point for those aspiring to advance their skills in this ever-growing field. In this course, you will explore the core mathematics, statistics, and programming skills necessary for conducting typical data analysis tasks, all through the lens of a practical data clustering project. Learn and apply the K-Means Clustering algorithm to a real-world dataset, honing your abilities in Python programming along the way. Spanning 1740 minutes of comprehensive content, this course offers a blend of theoretical knowledge and hands-on exercises, ensuring you gain a solid grounding in data science. By the end of the course, you will have completed a series of mathematical and programming exercises, culminating in a small data clustering project. Subscription options include Starter, Professional, and Paid plans, catering to different learning needs and budgets. This course is ideal for beginners who are eager to build a strong foundation in data science, as well as professionals looking to refresh their skills or transition into the data-driven world. Embark on your data science journey today and unlock the potential of data to inform decisions and drive insights across various domains including finance, retail, marketing, social science, medicine, and government.

Dr Matthew Yee-King
Instructors

Dr Matthew Yee-King

Dr Betty Fyn-Sydney
Instructors

Dr Betty Fyn-Sydney

Dr Jamie A Ward
Instructors

Dr Jamie A Ward

Dr Larisa Soldatova
Instructors

Dr Larisa Soldatova