数据科学基础:Python的K-Means集群

Foundations of Data Science: K-Means Clustering in Python

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伦敦大学
Coursera
  • 完成时间大约为 29 个小时
  • 初级
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

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.

课程概况

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.

课程大纲

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.

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

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