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.
Foundations of Data Science: K-Means Clustering in Python
proposé par
Université de Londres
Goldsmiths, Université de Londres
Université de Londres
À propos de ce cours
Ce que vous allez apprendre
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.
Compétences que vous acquerrez
K-Means ClusteringMachine LearningProgramming in Python
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
17 heures pour terminer
Week 1: Foundations of Data Science: K-means Clustering in Python
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.
9 vidéos (Total 22 min), 4 quiz
9 vidéos
Introduction to Data Science2 min
What is Data?1 min
Types of Data1 min
Machine Learning3 min
Supervised vs. Unsupervised Learning2 min
K-means Clustering4 min
Preparing your Data1 min
A Real World Dataset53s
4 exercices pour s'entraîner
Types of Data - Review Information15 min
Supervised vs. Unsupervised - Review Information15 min
K-means Clustering - Review Information30 min
Week 1 Summative Assessment40 min
Semaine
24 heures pour terminer
Week 2: Means and Deviations in Mathematics and Python
11 vidéos (Total 37 min), 2 lectures, 11 quiz
11 vidéos
2.1 - Introduction to Mathematical Concepts of Data Clustering1 min
2.2 - Mean of One Dimensional Lists2 min
2.3 - Variance and Standard Deviation3 min
2.4 Jupyter Notebooks6 min
2.5 Variables4 min
2.6 Lists4 min
2.7 Computing the Mean3 min
2.8 Better Lists: Numpy3 min
2.9 Computing the Standard Deviation6 min
Week 2 Conclusion31s
2 lectures
Python Style Guide10 min
Numpy and Array Creation20 min
10 exercices pour s'entraîner
Population vs Sample - Review Information5 min
Mean of One Dimensional Lists - Review Information3 min
Variance and Standard Deviation - Review Information4 min
Jupyter Notebooks - Review Information20 min
Variables - Review Information10 min
Lists - Review Information10 min
Computing the Mean - Review Information10 min
Better Lists - Review Information10 min
Computing the Standard Deviation - Review Information10 min
Week 2 Summative Assessment40 min
Semaine
33 heures pour terminer
Week 3: Moving from One to Two Dimensional Data
16 vidéos (Total 53 min), 3 lectures, 15 quiz
16 vidéos
3.1 Multidimensional Data Points and Features2 min
3.2 Multidimensional Mean2 min
3.3 Dispersion: Multidimensional Variables3 min
3.4 Distance Metrics5 min
3.5 Normalisation1 min
3.6 Outliers1 min
3.7 Basic Plotting2 min
3.7a Storing 2D Coordinates in a Single Data Structure6 min
3.8 Multidimensional Mean4 min
3.9 Adding Graphical Overlays5 min
3.10 Calculating the Distance to the Mean3 min
3.11 List Comprehension3 min
3.12 Normalisation in Python5 min
3.13 Outliers and Plotting Normalised Data2 min
Week 3 Conclusion30s
3 lectures
Matplotlib Scatter Plot Documentation20 min
Matplotlib Patches Documentation10 min
List Comprehension Documentation20 min
15 exercices pour s'entraîner
Multidimensional Data Points and Features - Review Information3 min
Multidimensional Mean - Review Information3 min
Dispersion: Multidimensional Variables - Review Information5 min
Distance Metrics - Review Information6 min
Normalisation - Review Information3 min
Outliers - Review Information4 min
Basic Plotting - Review Information5 min
Storing 2D Coordinates - Review Information4 min
Multidimensional Mean - Review Information4 min
Adding Graphical Overlays - Review Information6 min
Calculating Distance - Review Information6 min
List Comprehension - Review Information4 min
Normalisation in Python - Review Information4 min
Outliers - Review Information2 min
Week 3 Summative Assessment25 min
Semaine
45 heures pour terminer
Week 4: Introducing Pandas and Using K-means to Analyse Data
8 vidéos (Total 37 min), 6 lectures, 8 quiz
8 vidéos
4.1: Using the Pandas Library to Read csv Files5 min
4.1a: Sorting and Filtering Data Using Pandas8 min
4.1b: Labelling Points on a Graph4 min
4.1c: Labelling all the Points on a Graph3 min
4.2: Eyeballing the Data5 min
4.3: Using K-means to Interpret the Data8 min
Week 4: Conclusion35s
6 lectures
Week 4 Code Resources5 min
Pandas Read_CSV Function15 min
More Pandas Library Documentation10 min
The Pyplot Text Function10 min
For Loops in Python10 min
Documentation for sklearn.cluster.KMeans10 min
7 exercices pour s'entraîner
Using the Pandas Library to Read csv Files - Review Information5 min
Sorting and Filtering Data Using Pandas - Review Information10 min
Labelling Points on a Graph - Review Information5 min
Labelling all the Points on a Graph - Review Information5 min
Eyeballing the Data - Review Information5 min
Using K-means to Interpret the Data - Review Information5 min
Week 4 Summative Assessment40 min
Semaine
510 heures pour terminer
Week 5: A Data Clustering Project
9 vidéos (Total 30 min), 4 lectures, 6 quiz
9 vidéos
5.1 Can a Machine Detect Fake Notes?1 min
5.2 Working for a Client4 min
5.3 How to Organize Work on Your Project3 min
5.4 Dealing With Difficulties3 min
5.5 No Data no Data Science: Introduction of the Dataset4 min
5.6 Modelling4 min
5.7 Presenting the Project Results3 min
5.8 Concluding Remarks1 min
4 lectures
The Banknote Authentication Dataset10 min
Week 5 Code Resource - the Dataset for our Project10 min
Saving plt.scatter Outputs as Figures10 min
Additional Recommended Reading for Week 55 min
3 exercices pour s'entraîner
What Would You Advise? - Review Information10 min
Python - Review Information4 min
Week 5 Summative Assessment30 min
Enseignants
Dr Matthew Yee-King
LecturerComputing Department, Goldsmiths, University of London
Dr Betty Fyn-Sydney
Lecturer in MathematicsDepartment of Computing, Goldsmiths, University of London
Dr Jamie A Ward
Lecturer in Computer ScienceDepartment of Computing, Goldsmiths, University of London
Dr Larisa Soldatova
Reader in Data ScienceDepartment of Computing, Goldsmiths, University of London
À propos de Université de Londres
The University of London is a federal University which includes 18 world leading Colleges. Our distance learning programmes were founded in 1858 and have enriched the lives of thousands of students, delivering high quality University of London degrees wherever our students are across the globe. Our alumni include 7 Nobel Prize winners. Today, we are a global leader in distance and flexible study, offering degree programmes to over 50,000 students in over 180 countries. To find out more about studying for one of our degrees where you are, visit www.london.ac.uk
À propos de Goldsmiths, Université de Londres
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.
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