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随风渐入醉 2018-02-28 07:58 0 票支持; 0 票反对
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开始时间: 06/21/2022 持续时间: Approximately 8 months to complete Suggested pace of 4 hours/week
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所在平台: Coursera专项课程 课程类别: 计算机科学 大学或机构: CourseraNew |
课程主页: https://www.coursera.org/specializations/data-mining
课程评论: 1 个评论
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
数据挖掘专长：数据挖掘专长教授针对符合清晰定义的架构的结构化数据和以自然语言文本形式存在的非结构化数据的数据挖掘技术。具体的课程主题包括模式发现，群集，文本检索，文本挖掘和分析以及数据可视化。 Capstone项目的任务是使用Yelp的餐厅评论数据集解决现实世界中的数据挖掘难题。 本专业的课程2-5构成了在线在线数据科学计算机科学硕士学位课程的授课内容。您可以在开始专业化之前或之后申请学位课程。Course: 1
Course Link: https://www.coursera.org/learn/datavisualization?specialization=data-mining
Title:Data Visualization
Description:Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.
Course: 2
Course Link: https://www.coursera.org/learn/text-retrieval?specialization=data-mining
Title:Text Retrieval and Search Engines
Description:Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people’s opinions and preferences, in addition to many other kinds of knowledge that we encode in text.
Course: 3
Course Link: https://www.coursera.org/learn/text-mining?specialization=data-mining
Title:Text Mining and Analytics
Description:This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.
Course: 4
Course Link: https://www.coursera.org/learn/data-patterns?specialization=data-mining
Title:Pattern Discovery in Data Mining
Description:Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for data-driven phrase mining and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.
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随风渐入醉 2018-02-28 07:58 0 票支持; 0 票反对
全是英？ |
伊利诺伊大学香槟分校的数据挖掘专项课程系列（Data Mining Specialization），这个系列包含5门子课程和1个毕业项目课程，涵盖数据可视化，信息检索，文本挖掘与分析，模式发现和聚类分析等，感兴趣的同学可以关注：Data Mining Specialization-Analyze Text, Discover Patterns, Visualize Data. Solve real-world data mining challenges.
