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.
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伦敦大学
伦敦大学金匠学院
Foundations of Data Science: K-Means Clustering in Python
伦敦大学课程信息
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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29 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 中文（简体）, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 土耳其语（Turkish）, 英语（English）, 西班牙语（Spanish）
您将学到的内容有
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.
您将获得的技能
K-Means ClusteringMachine LearningProgramming in Python
可分享的证书
完成后获得证书
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29 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 中文（简体）, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 土耳其语（Turkish）, 英语（English）, 西班牙语（Spanish）
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伦敦大学
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
伦敦大学金匠学院
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.
教学大纲 - 您将从这门课程中学到什么
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.
完成时间为 7 小时
9 个视频 （总计 22 分钟）
9 个视频
Introduction to Data Science2分钟
What is Data?1分钟
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
完成时间为 4 小时
Week 2: Means and Deviations in Mathematics and Python
完成时间为 4 小时
11 个视频 （总计 37 分钟）, 4 个阅读材料, 11 个测验
11 个视频
2.1 – Introduction to Mathematical Concepts of Data Clustering1分钟
2.2 – Mean of One Dimensional Lists2分钟
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
4 个阅读材料
Population vs Sample, Bias10分钟
Variability, Standard Deviation and Bias10分钟
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
完成时间为 8 小时
Week 3: Moving from One to Two Dimensional Data
完成时间为 8 小时
16 个视频 （总计 53 分钟）, 10 个阅读材料, 15 个测验
16 个视频
3.1 Multidimensional Data Points and Features2分钟
3.2 Multidimensional Mean2分钟
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
10 个阅读材料
Multidimensional Data Points and Features Recap10分钟
Multidimensional Mean Recap10分钟
Multidimensional Variables Recap10分钟
Distance Metrics Recap10分钟
Normalisation Recap10分钟
Note on Matplotlib10分钟
Matplotlib Scatter Plot Documentation20分钟
Matplotlib Patches Documentation10分钟
List Comprehension Documentation20分钟
3.12 Errata10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 Information30分钟
Basic Plotting – Review Information5分钟
Storing 2D Coordinates – Review Information30分钟
Multidimensional Mean – Review Information30分钟
Adding Graphical Overlays – Review Information30分钟
Calculating Distance – Review Information30分钟
List Comprehension – Review Information30分钟
Normalisation in Python – Review Information30分钟
Outliers – Review Information30分钟
Week 3 Summative Assessment25分钟
完成时间为 4 小时
Week 4: Introducing Pandas and Using K-Means to Analyse Data
完成时间为 4 小时
8 个视频 （总计 37 分钟）, 6 个阅读材料, 8 个测验
8 个视频
4.1: Using the Pandas Library to Read csv Files5分钟
4.1a: Sorting and Filtering Data Using Pandas8分钟
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
审阅
来自FOUNDATIONS OF DATA SCIENCE: K-MEANS CLUSTERING IN PYTHON的热门评论
由 AH 提供Jun 3, 2020
I love this course as it gives me the foundations of learning the Python coding program and relevant statistical methods that used for data analysis. It's really interesting course to attend to.
由 GR 提供Sep 9, 2019
184/5000 Conferences 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.
由 SS 提供Jun 29, 2020
A well presented and interesting course. It would have been good to have some more complex examples with the thinking behind them - the exploratory bit/intelligent bit of the process.
由 BO 提供Jul 3, 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.
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