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.
Offert par
Université de Londres
Goldsmiths, Université de Londres
Foundations of Data Science: K-Means Clustering in Python
Université de LondresÀ propos de ce cours
76,453 consultations récentes
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau débutant
You will need mathematical and statistical knowledge and skills at least at high-school level.
Approx. 29 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol
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
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau débutant
You will need mathematical and statistical knowledge and skills at least at high-school level.
Approx. 29 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol
Offert par
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
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.
Programme du cours : ce que vous apprendrez dans ce cours
7 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.
7 heures pour terminer
9 vidéos (Total 22 min)
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
4 heures pour terminer
Week 2: Means and Deviations in Mathematics and Python
4 heures pour terminer
11 vidéos (Total 37 min), 4 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
4 lectures
Population vs Sample, Bias10 min
Variability, Standard Deviation and Bias10 min
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
8 heures pour terminer
Week 3: Moving from One to Two Dimensional Data
8 heures pour terminer
16 vidéos (Total 53 min), 10 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
10 lectures
Multidimensional Data Points and Features Recap10 min
Multidimensional Mean Recap10 min
Multidimensional Variables Recap10 min
Distance Metrics Recap10 min
Normalisation Recap10 min
Note on Matplotlib10 min
Matplotlib Scatter Plot Documentation20 min
Matplotlib Patches Documentation10 min
List Comprehension Documentation20 min
3.12 Errata10 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 Information30 min
Basic Plotting – Review Information5 min
Storing 2D Coordinates – Review Information30 min
Multidimensional Mean – Review Information30 min
Adding Graphical Overlays – Review Information30 min
Calculating Distance – Review Information30 min
List Comprehension – Review Information30 min
Normalisation in Python – Review Information30 min
Outliers – Review Information30 min
Week 3 Summative Assessment25 min
4 heures pour terminer
Week 4: Introducing Pandas and Using K-Means to Analyse Data
4 heures pour terminer
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
Avis
Meilleurs avis pour FOUNDATIONS OF DATA SCIENCE: K-MEANS CLUSTERING IN PYTHON
par AH3 juin 2020
I love this course as it gives me the foundations of learning the Python coding program and relevant statistical methods that used for data analysis. It's really interesting course to attend to.
par GR9 sept. 2019
184/5000 Conferences of very good quality, and the platform for practices is really useful to put the theory into practice. I recommend this course if you want to start in data science.
par SS29 juin 2020
A well presented and interesting course. It would have been good to have some more complex examples with the thinking behind them - the exploratory bit/intelligent bit of the process.
par BO3 juil. 2020
Highly recommended to anyone who wants to delve into data science. The instruuctors, the universities and Coursera team are well dedicated and the course is of high quality.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
À quoi ai-je droit si j'achète le Certificat ?
Is financial aid available?
Puis-je obtenir des crédits universitaires si je réussis le Cours ?
D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.
