Acerca de este Curso
21,017 vistas recientes

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel principiante

You will need mathematical and statistical knowledge and skills at least at high-school level.

Aprox. 23 horas para completar

Sugerido: 5 Weeks of study, 5-6 hours per week...

Inglés (English)

Subtítulos: Inglés (English)

Qué aprenderás

  • Check

    Define and explain the key concepts of data clustering

  • Check

    Demonstrate understanding of the key constructs and features of the Python language.

  • Check

    Implement in Python the principle steps of the K-means algorithm.

  • Check

    Design and execute a whole data clustering workflow and interpret the outputs.

Habilidades que obtendrás

K-Means ClusteringMachine LearningProgramming in Python

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel principiante

You will need mathematical and statistical knowledge and skills at least at high-school level.

Aprox. 23 horas para completar

Sugerido: 5 Weeks of study, 5-6 hours per week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
7 horas para completar

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 (Total 22 minutos), 4 quizzes
9 videos
Types of Data1m
Machine Learning3m
Supervised vs Unsupervised Learning2m
K-Means Clustering4m
Preparing your Data1m
A Real World Dataset53s
4 ejercicios de práctica
Types of Data – Review Information15m
Supervised vs Unsupervised – Review Information15m
K-Means Clustering – Review Information30m
Week 1 Summative Assessment40m
Semana
2
4 horas para completar

Week 2: Means and Deviations in Mathematics and Python

...
11 videos (Total 37 minutos), 2 readings, 11 quizzes
11 videos
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 Conclusion31s
2 lecturas
Python Style Guide10m
Numpy and Array Creation20m
10 ejercicios de práctica
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
Semana
3
3 horas para completar

Week 3: Moving from One to Two Dimensional Data

...
16 videos (Total 53 minutos), 3 readings, 15 quizzes
16 videos
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 Conclusion30s
3 lecturas
Matplotlib Scatter Plot Documentation20m
Matplotlib Patches Documentation10m
List Comprehension Documentation20m
15 ejercicios de práctica
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
Semana
4
5 horas para completar

Week 4: Introducing Pandas and Using K-Means to Analyse Data

...
8 videos (Total 37 minutos), 6 readings, 8 quizzes
8 videos
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: Conclusion35s
6 lecturas
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 ejercicios de práctica
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
5.0
1 revisionesChevron Right

Principales revisiones sobre Foundations of Data Science: K-Means Clustering in Python

por AAJun 4th 2019

This course is at right level for a beginner (python and analytics) while going into details around K means clustering

Instructores

Avatar

Dr Matthew Yee-King

Lecturer
Computing Department, Goldsmiths, University of London
Avatar

Dr Betty Fyn-Sydney

Lecturer in Mathematics
Department of Computing, Goldsmiths, University of London
Avatar

Dr Jamie A Ward

Lecturer in Computer Science
Department of Computing, Goldsmiths, University of London
Avatar

Dr Larisa Soldatova

Reader in Data Science
Department of Computing, Goldsmiths, University of London

Acerca de Universidad 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...

Acerca de 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....

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.

¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.