Informações sobre o curso
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100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível iniciante

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

Aprox. 26 horas para completar

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

Inglês

Legendas: Inglês
User
Os alunos fazendo este Course são
  • Data Scientists
  • Scientists
  • Data Engineers
  • Financial Analysts
  • Business Analysts

O que você vai aprender

  • 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 você terá

K-Means ClusteringMachine LearningProgramming in Python
User
Os alunos fazendo este Course são
  • Data Scientists
  • Scientists
  • Data Engineers
  • Financial Analysts
  • Business Analysts

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível iniciante

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

Aprox. 26 horas para completar

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

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
7 horas para concluir

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

9 vídeos (Total 22 mín.), 4 testes
9 videos
Introduction to Data Science2min
What is Data?1min
Types of Data1min
Machine Learning3min
Supervised vs Unsupervised Learning2min
K-Means Clustering4min
Preparing your Data1min
A Real World Dataset53s
4 exercícios práticos
Types of Data – Review Information15min
Supervised vs Unsupervised – Review Information15min
K-Means Clustering – Review Information30min
Week 1 Summative Assessment40min
Semana
2
4 horas para concluir

Week 2: Means and Deviations in Mathematics and Python

11 vídeos (Total 37 mín.), 4 leituras, 11 testes
11 videos
2.1 – Introduction to Mathematical Concepts of Data Clustering1min
2.2 – Mean of One Dimensional Lists2min
2.3 – Variance and Standard Deviation3min
2.4 Jupyter Notebooks6min
2.5 Variables4min
2.6 Lists4min
2.7 Computing the Mean3min
2.8 Better Lists: NumPy3min
2.9 Computing the Standard Deviation6min
Week 2 Conclusion31s
4 leituras
Population vs Sample, Bias10min
Variability, Standard Deviation and Bias10min
Python Style Guide10min
Numpy and Array Creation20min
10 exercícios práticos
Population vs Sample – Review Information5min
Mean of One Dimensional Lists – Review Information3min
Variance and Standard Deviation – Review Information4min
Jupyter Notebooks – Review Information20min
Variables – Review Information10min
Lists – Review Information10min
Computing the Mean – Review Information10min
Better Lists – Review Information10min
Computing the Standard Deviation – Review Information10min
Week 2 Summative Assessment40min
Semana
3
4 horas para concluir

Week 3: Moving from One to Two Dimensional Data

16 vídeos (Total 53 mín.), 10 leituras, 15 testes
16 videos
3.1 Multidimensional Data Points and Features2min
3.2 Multidimensional Mean2min
3.3 Dispersion: Multidimensional Variables3min
3.4 Distance Metrics5min
3.5 Normalisation1min
3.6 Outliers1min
3.7 Basic Plotting2min
3.7a Storing 2D Coordinates in a Single Data Structure6min
3.8 Multidimensional Mean4min
3.9 Adding Graphical Overlays5min
3.10 Calculating the Distance to the Mean3min
3.11 List Comprehension3min
3.12 Normalisation in Python5min
3.13 Outliers and Plotting Normalised Data2min
Week 3 Conclusion30s
10 leituras
Multidimensional Data Points and Features Recap10min
Multidimensional Mean Recap10min
Multidimensional Variables Recap10min
Distance Metrics Recap10min
Normalisation Recap10min
Note on Matplotlib10min
Matplotlib Scatter Plot Documentation20min
Matplotlib Patches Documentation10min
List Comprehension Documentation20min
3.12 Errata10min
15 exercícios práticos
Multidimensional Data Points and Features – Review Information3min
Multidimensional Mean – Review Information3min
Dispersion: Multidimensional Variables – Review Information5min
Distance Metrics – Review Information6min
Normalisation – Review Information3min
Outliers – Review Information4min
Basic Plotting – Review Information5min
Storing 2D Coordinates – Review Information4min
Multidimensional Mean – Review Information4min
Adding Graphical Overlays – Review Information6min
Calculating Distance – Review Information6min
List Comprehension – Review Information4min
Normalisation in Python – Review Information4min
Outliers – Review Information2min
Week 3 Summative Assessment25min
Semana
4
5 horas para concluir

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

8 vídeos (Total 37 mín.), 6 leituras, 8 testes
8 videos
4.1: Using the Pandas Library to Read csv Files5min
4.1a: Sorting and Filtering Data Using Pandas8min
4.1b: Labelling Points on a Graph4min
4.1c: Labelling all the Points on a Graph3min
4.2: Eyeballing the Data5min
4.3: Using K-Means to Interpret the Data8min
Week 4: Conclusion35s
6 leituras
Week 4 Code Resources5min
Pandas Read_CSV Function15min
More Pandas Library Documentation10min
The Pyplot Text Function10min
For Loops in Python10min
Documentation for sklearn.cluster.KMeans10min
7 exercícios práticos
Using the Pandas Library to Read csv Files – Review Information5min
Sorting and Filtering Data Using Pandas – Review Information10min
Labelling Points on a Graph – Review Information5min
Labelling all the Points on a Graph – Review Information5min
Eyeballing the Data – Review Information5min
Using K-Means to Interpret the Data – Review Information5min
Week 4 Summative Assessment40min
4.8
3 avaliaçõesChevron Right

Principais avaliações do Foundations of Data Science: K-Means Clustering in Python

por GRSep 10th 2019

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.

por AAJun 4th 2019

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

Instrutores

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Dr Matthew Yee-King

Lecturer
Computing Department, Goldsmiths, University of London
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Dr Betty Fyn-Sydney

Lecturer in Mathematics
Department of Computing, Goldsmiths, University of London
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Dr Jamie A Ward

Lecturer in Computer Science
Department of Computing, Goldsmiths, University of London
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Dr Larisa Soldatova

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

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