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.
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
93,939 vistas recientes
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 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. 30 horas para completar
Sugerido: 5 Weeks of study, 5-6 hours per week
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
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
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
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.
7 horas para completar
9 videos (Total 22 minutos)
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 Dataset53s
4 ejercicios de práctica
Types of Data – Review Information15m
Supervised vs Unsupervised – Review Information15m
K-Means Clustering – Review Information30m
Week 1 Summative Assessment40m
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
2.1 – 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
4 horas para completar
Week 3: Moving from One to Two Dimensional Data
4 horas para completar
16 videos (Total 53 minutos), 10 lecturas, 15 cuestionarios
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 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 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
5 horas para completar
Week 4: Introducing Pandas and Using K-Means to Analyse Data
5 horas para completar
8 videos (Total 37 minutos), 6 lecturas, 8 cuestionarios
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: 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
Principales revisiones sobre Foundations of Data Science: K-Means Clustering in Python
184/5000\n\nConferences 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.
This course is at right level for a beginner (python and analytics) while going into details around K means clustering
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
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.
¿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.
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