课程信息
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100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

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初级

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

完成时间大约为23 小时

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

英语(English)

字幕:英语(English)

您将学到的内容有

  • 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.

您将获得的技能

K-Means ClusteringMachine LearningProgramming in Python

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

初级

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

完成时间大约为23 小时

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

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 7 小时

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 个视频 (总计 22 分钟), 4 个测验
9 个视频
Types of Data1分钟
Machine Learning3分钟
Supervised vs Unsupervised Learning2分钟
K-Means Clustering4分钟
Preparing your Data1分钟
A Real World Dataset53
4 个练习
Types of Data – Review Information15分钟
Supervised vs Unsupervised – Review Information15分钟
K-Means Clustering – Review Information30分钟
Week 1 Summative Assessment40分钟
2
完成时间为 4 小时

Week 2: Means and Deviations in Mathematics and Python

...
11 个视频 (总计 37 分钟), 2 个阅读材料, 11 个测验
11 个视频
2.3 – Variance and Standard Deviation3分钟
2.4 Jupyter Notebooks6分钟
2.5 Variables4分钟
2.6 Lists4分钟
2.7 Computing the Mean3分钟
2.8 Better Lists: NumPy3分钟
2.9 Computing the Standard Deviation6分钟
Week 2 Conclusion31
2 个阅读材料
Python Style Guide10分钟
Numpy and Array Creation20分钟
10 个练习
Population vs Sample – Review Information5分钟
Mean of One Dimensional Lists – Review Information3分钟
Variance and Standard Deviation – Review Information4分钟
Jupyter Notebooks – Review Information20分钟
Variables – Review Information10分钟
Lists – Review Information10分钟
Computing the Mean – Review Information10分钟
Better Lists – Review Information10分钟
Computing the Standard Deviation – Review Information10分钟
Week 2 Summative Assessment40分钟
3
完成时间为 3 小时

Week 3: Moving from One to Two Dimensional Data

...
16 个视频 (总计 53 分钟), 3 个阅读材料, 15 个测验
16 个视频
3.3 Dispersion: Multidimensional Variables3分钟
3.4 Distance Metrics5分钟
3.5 Normalisation1分钟
3.6 Outliers1分钟
3.7 Basic Plotting2分钟
3.7a Storing 2D Coordinates in a Single Data Structure6分钟
3.8 Multidimensional Mean4分钟
3.9 Adding Graphical Overlays5分钟
3.10 Calculating the Distance to the Mean3分钟
3.11 List Comprehension3分钟
3.12 Normalisation in Python5分钟
3.13 Outliers and Plotting Normalised Data2分钟
Week 3 Conclusion30
3 个阅读材料
Matplotlib Scatter Plot Documentation20分钟
Matplotlib Patches Documentation10分钟
List Comprehension Documentation20分钟
15 个练习
Multidimensional Data Points and Features – Review Information3分钟
Multidimensional Mean – Review Information3分钟
Dispersion: Multidimensional Variables – Review Information5分钟
Distance Metrics – Review Information6分钟
Normalisation – Review Information3分钟
Outliers – Review Information4分钟
Basic Plotting – Review Information5分钟
Storing 2D Coordinates – Review Information4分钟
Multidimensional Mean – Review Information4分钟
Adding Graphical Overlays – Review Information6分钟
Calculating Distance – Review Information6分钟
List Comprehension – Review Information4分钟
Normalisation in Python – Review Information4分钟
Outliers – Review Information2分钟
Week 3 Summative Assessment25分钟
4
完成时间为 5 小时

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

...
8 个视频 (总计 37 分钟), 6 个阅读材料, 8 个测验
8 个视频
4.1b: Labelling Points on a Graph4分钟
4.1c: Labelling all the Points on a Graph3分钟
4.2: Eyeballing the Data5分钟
4.3: Using K-Means to Interpret the Data8分钟
Week 4: Conclusion35
6 个阅读材料
Week 4 Code Resources5分钟
Pandas Read_CSV Function15分钟
More Pandas Library Documentation10分钟
The Pyplot Text Function10分钟
For Loops in Python10分钟
Documentation for sklearn.cluster.KMeans10分钟
7 个练习
Using the Pandas Library to Read csv Files – Review Information5分钟
Sorting and Filtering Data Using Pandas – Review Information10分钟
Labelling Points on a Graph – Review Information5分钟
Labelling all the Points on a Graph – Review Information5分钟
Eyeballing the Data – Review Information5分钟
Using K-Means to Interpret the Data – Review Information5分钟
Week 4 Summative Assessment40分钟
5.0
1 个审阅Chevron Right

来自Foundations of Data Science: K-Means Clustering in Python的热门评论

创建者 AAJun 4th 2019

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

讲师

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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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