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
von
University of London
Goldsmiths, University of London
University of London
Über diesen Kurs
Was Sie lernen werden
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.
Kompetenzen, die Sie erwerben
K-Means ClusteringMachine LearningProgramming in Python
Lehrplan - Was Sie in diesem Kurs lernen werden
Woche
17 Stunden zum Abschließen
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 Videos (Gesamt 22 min), 4 Quiz
9 Videos
Introduction to Data Science2m
What is Data?1m
Types of Data1m
Machine Learning3m
Supervised vs. Unsupervised Learning2m
K-means Clustering4m
Preparing your Data1m
A Real World Dataset53
4 praktische Übungen
Types of Data - Review Information15m
Supervised vs. Unsupervised - Review Information15m
K-means Clustering - Review Information30m
Week 1 Summative Assessment40m
Woche
24 Stunden zum Abschließen
Week 2: Means and Deviations in Mathematics and Python
11 Videos (Gesamt 37 min), 2 Lektüren, 11 Quiz
11 Videos
2.1 - Introduction to Mathematical Concepts of Data Clustering1m
2.2 - Mean of One Dimensional Lists2m
2.3 - Variance and Standard Deviation3m
2.4 Jupyter Notebooks6m
2.5 Variables4m
2.6 Lists4m
2.7 Computing the Mean3m
2.8 Better Lists: Numpy3m
2.9 Computing the Standard Deviation6m
Week 2 Conclusion31
2 Lektüren
Python Style Guide10m
Numpy and Array Creation20m
10 praktische Übungen
Population vs Sample - Review Information5m
Mean of One Dimensional Lists - Review Information3m
Variance and Standard Deviation - Review Information4m
Jupyter Notebooks - Review Information20m
Variables - Review Information10m
Lists - Review Information10m
Computing the Mean - Review Information10m
Better Lists - Review Information10m
Computing the Standard Deviation - Review Information10m
Week 2 Summative Assessment40m
Woche
33 Stunden zum Abschließen
Week 3: Moving from One to Two Dimensional Data
16 Videos (Gesamt 53 min), 3 Lektüren, 15 Quiz
16 Videos
3.1 Multidimensional Data Points and Features2m
3.2 Multidimensional Mean2m
3.3 Dispersion: Multidimensional Variables3m
3.4 Distance Metrics5m
3.5 Normalisation1m
3.6 Outliers1m
3.7 Basic Plotting2m
3.7a Storing 2D Coordinates in a Single Data Structure6m
3.8 Multidimensional Mean4m
3.9 Adding Graphical Overlays5m
3.10 Calculating the Distance to the Mean3m
3.11 List Comprehension3m
3.12 Normalisation in Python5m
3.13 Outliers and Plotting Normalised Data2m
Week 3 Conclusion30
3 Lektüren
Matplotlib Scatter Plot Documentation20m
Matplotlib Patches Documentation10m
List Comprehension Documentation20m
15 praktische Übungen
Multidimensional Data Points and Features - Review Information3m
Multidimensional Mean - Review Information3m
Dispersion: Multidimensional Variables - Review Information5m
Distance Metrics - Review Information6m
Normalisation - Review Information3m
Outliers - Review Information4m
Basic Plotting - Review Information5m
Storing 2D Coordinates - Review Information4m
Multidimensional Mean - Review Information4m
Adding Graphical Overlays - Review Information6m
Calculating Distance - Review Information6m
List Comprehension - Review Information4m
Normalisation in Python - Review Information4m
Outliers - Review Information2m
Week 3 Summative Assessment25m
Woche
45 Stunden zum Abschließen
Week 4: Introducing Pandas and Using K-means to Analyse Data
8 Videos (Gesamt 37 min), 6 Lektüren, 8 Quiz
8 Videos
4.1: Using the Pandas Library to Read csv Files5m
4.1a: Sorting and Filtering Data Using Pandas8m
4.1b: Labelling Points on a Graph4m
4.1c: Labelling all the Points on a Graph3m
4.2: Eyeballing the Data5m
4.3: Using K-means to Interpret the Data8m
Week 4: Conclusion35
6 Lektüren
Week 4 Code Resources5m
Pandas Read_CSV Function15m
More Pandas Library Documentation10m
The Pyplot Text Function10m
For Loops in Python10m
Documentation for sklearn.cluster.KMeans10m
7 praktische Übungen
Using the Pandas Library to Read csv Files - Review Information5m
Sorting and Filtering Data Using Pandas - Review Information10m
Labelling Points on a Graph - Review Information5m
Labelling all the Points on a Graph - Review Information5m
Eyeballing the Data - Review Information5m
Using K-means to Interpret the Data - Review Information5m
Week 4 Summative Assessment40m
Woche
510 Stunden zum Abschließen
Week 5: A Data Clustering Project
9 Videos (Gesamt 30 min), 4 Lektüren, 6 Quiz
9 Videos
5.1 Can a Machine Detect Fake Notes?1m
5.2 Working for a Client4m
5.3 How to Organize Work on Your Project3m
5.4 Dealing With Difficulties3m
5.5 No Data no Data Science: Introduction of the Dataset4m
5.6 Modelling4m
5.7 Presenting the Project Results3m
5.8 Concluding Remarks1m
4 Lektüren
The Banknote Authentication Dataset10m
Week 5 Code Resource - the Dataset for our Project10m
Saving plt.scatter Outputs as Figures10m
Additional Recommended Reading for Week 55m
3 praktische Übungen
What Would You Advise? - Review Information10m
Python - Review Information4m
Week 5 Summative Assessment30m
Dozenten
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
Über University of London
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
Über 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.
Häufig gestellte Fragen
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Was bekomme ich, wenn ich das Zertifikat erwerbe?
Wenn Sie ein Zertifikat erwerben, erhalten Sie Zugriff auf alle Kursmaterialien, einschließlich bewerteter Aufgaben. Nach Abschluss des Kurses wird Ihr elektronisches Zertifikat zu Ihrer Seite „Errungenschaften“ hinzugefügt – von dort können Sie Ihr Zertifikat ausdrucken oder es zu Ihrem LinkedIn Profil hinzufügen. Wenn Sie nur lesen und den Inhalt des Kurses anzeigen möchten, können Sie kostenlos als Gast an dem Kurs teilnehmen.
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