4.6
estrellas
512 calificaciones

95 %

Dr Matthew Yee-King +3 instructores más

43.641 ya inscrito

Ofrecido Por

Universidad de Londres

Goldsmiths, University of London

Foundations of Data Science: K-Means Clustering in Python

Universidad de Londres

Acerca de este Curso

113.353 vistas recientes

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.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Nivel principiante

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

Aprox. 29 horas para completar

Inglés (English)

Qué aprenderás

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.

Habilidades que obtendrás

K-Means Clustering
Machine Learning
Programming in Python

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Nivel principiante

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

Aprox. 29 horas para completar

Inglés (English)

Instructores

Calificación del instructor

4,64/5 (199 calificaciones)

Ofrecido por

Universidad de Londres

The University of London is a federal University which includes 17 world leading Colleges. With extensive experience in distance learning since 1858, University of London has enriched the lives of thousands of students, delivering high quality degrees across the globe. Today, University of London is a global leader in flexible study, offering degree programmes to over 45,000 students in over 190 countries, delivering world-leading research across the world. To find out more about University of London, visit www.london.ac.uk

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.

Programa - Qué aprenderás en este curso

Calificación del contenido95%(11,938 calificaciones)

Semana

Semana 1

7 horas para completar

Week 1: Foundations of Data Science: K-Means Clustering in Python

7 horas para completar

9 videos (Total 22 minutos)

9 videos

Welcome and Introduction2m

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 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

Semana 2

4 horas para completar

Week 2: Means and Deviations in Mathematics and Python

4 horas para completar

11 videos (Total 37 minutos), 4 lecturas, 11 cuestionarios

11 videos

2.0: Week 2 Introduction35s

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 Conclusion31s

4 lecturas

Population vs Sample, Bias10m

Variability, Standard Deviation and Bias10m

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

Semana 3

8 horas para completar

Week 3: Moving from One to Two Dimensional Data

8 horas para completar

16 videos (Total 53 minutos), 10 lecturas, 15 cuestionarios

16 videos

Week 3 Introduction37s

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 Conclusion30s

10 lecturas

Multidimensional Data Points and Features Recap10m

Multidimensional Mean Recap10m

Multidimensional Variables Recap10m

Distance Metrics Recap10m

Normalisation Recap10m

Note on Matplotlib10m

Matplotlib Scatter Plot Documentation20m

Matplotlib Patches Documentation10m

List Comprehension Documentation20m

3.12 Errata10m

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 Information30m

Basic Plotting – Review Information5m

Storing 2D Coordinates – Review Information30m

Multidimensional Mean – Review Information30m

Adding Graphical Overlays – Review Information30m

Calculating Distance – Review Information30m

List Comprehension – Review Information30m

Normalisation in Python – Review Information30m

Outliers – Review Information30m

Week 3 Summative Assessment25m

Semana

Semana 4

4 horas para completar

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

4 horas para completar

8 videos (Total 37 minutos), 6 lecturas, 8 cuestionarios

8 videos

Week 4 Introduction50s

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: Conclusion35s

6 lecturas

Week 4 Code Resources5m

Pandas Read_CSV Function15m

Reseñas

4.6

162 reseña

5 stars
72,65 %
4 stars
21,48 %
3 stars
3,32 %
2 stars
1,36 %
1 star
1,17 %

Principales reseñas sobre FOUNDATIONS OF DATA SCIENCE: K-MEANS CLUSTERING IN PYTHON

por GK31 de ago. de 2021

This course has great potential for future Data Scientists and it gives a breif explination of what we are dealing in the companies by giving us real life problems and making us solve those problems.

por MP2 de ene. de 2022

This course was excellent. Though I already knew the concepts in this course and the programing skills, it has inspired me to 100% take the MSC provided. Excellent lectures. Just Excellent.

por BO3 de jul. de 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.

por CK30 de may. de 2020

It was a great journey to get through it. Thanks a lot to all the instructors for their valuable job and effort :)

Ver todas las reseñas

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si compro el Certificado?
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.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

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

Foundations of Data Science: K-Means Clustering in Python

Foundations of Data Science: K-Means Clustering in Python

Acerca de este Curso

Qué aprenderás

Habilidades que obtendrás

Instructores

Dr Matthew Yee-King

Dr Betty Fyn-Sydney

Dr Jamie A Ward

Dr Larisa Soldatova

Ofrecido por

Universidad de Londres

Goldsmiths, University of London

Programa - Qué aprenderás en este curso

Week 1: Foundations of Data Science: K-Means Clustering in Python

Week 2: Means and Deviations in Mathematics and Python

Week 3: Moving from One to Two Dimensional Data

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

Week 5: A Data Clustering Project

Reseñas

Principales reseñas sobre FOUNDATIONS OF DATA SCIENCE: K-MEANS CLUSTERING IN PYTHON

Preguntas Frecuentes

Comienza o impulsa tu carrera profesional

Cursos y certificaciones populares

Colecciones y artículos populares

Obtén un título de grado o un certificado en línea

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Foundations of Data Science: K-Means Clustering in Python

Foundations of Data Science: K-Means Clustering in Python

Acerca de este Curso

Qué aprenderás

Habilidades que obtendrás

Instructores

Dr Matthew Yee-King

Dr Betty Fyn-Sydney

Dr Jamie A Ward

Dr Larisa Soldatova

Ofrecido por

Universidad de Londres

Goldsmiths, University of London

Programa - Qué aprenderás en este curso

Week 1: Foundations of Data Science: K-Means Clustering in Python

Week 2: Means and Deviations in Mathematics and Python

Week 3: Moving from One to Two Dimensional Data

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

Week 5: A Data Clustering Project

Reseñas

Principales reseñas sobre FOUNDATIONS OF DATA SCIENCE: K-MEANS CLUSTERING IN PYTHON

Preguntas Frecuentes

Coursera Footer

Comienza o impulsa tu carrera profesional

Cursos y certificaciones populares

Colecciones y artículos populares

Obtén un título de grado o un certificado en línea

Coursera

Comunidad

Más