This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field.
课程信息
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100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 3 门）
中级
data science professionals or domain experts, some experience working with data, successful completion of Data Mining Pipeline
完成时间大约为23 小时
英语（English）
字幕：英语（English）
您将学到的内容有
Identify the core functionalities of data modeling in the data mining pipeline
Apply techniques that can be used to accomplish the core functionalities of data modeling and explain how they work.
Evaluate data modeling techniques, determine which is most suitable for a particular task, and identify potential improvements.
您将获得的技能
- outlier analysis
- clustering
- classification
- model evaluation
- frequent pattern analysis
可灵活调整截止日期
根据您的日程表重置截止日期。
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。
第 2 门课程（共 3 门）
中级
data science professionals or domain experts, some experience working with data, successful completion of Data Mining Pipeline
完成时间大约为23 小时
英语（English）
字幕：英语（English）
提供方
科罗拉多大学波德分校
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
立即开始攻读硕士学位
此 课程 隶属于 科罗拉多大学波德分校 提供的 100% 在线 Master of Science in Data Science。
如果您被录取参加全部课程，您的课程将计入您的学位学习进程。
授课大纲 - 您将从这门课程中学到什么
完成时间为 6 小时
Frequent Pattern Analysis
This module starts with an overview of data mining methods, then focuses on frequent pattern analysis, including the Apriori algorithm and FP-growth algorithm for frequent itemset mining, as well as association rules and correlation analysis.
完成时间为 6 小时
2 个视频 （总计 94 分钟）, 2 个阅读材料, 1 个测验
完成时间为 6 小时
Classification
This module introduces supervised learning, classification, prediction, and covers several core classification methods including decision tree induction, Bayesian classification, support vector machines, neural networks, and ensemble methods. It also discusses classification model evaluation and comparison.
完成时间为 6 小时
2 个视频 （总计 106 分钟）
完成时间为 6 小时
Clustering
This module introduces unsupervised learning, clustering, and covers several core clustering methods including partitioning, hierarchical, grid-based, density-based, and probabilistic clustering. Advanced topics for high-dimensional clustering, bi-clustering, graph clustering, and constraint-based clustering are also discussed.
完成时间为 6 小时
2 个视频 （总计 105 分钟）, 1 个阅读材料, 1 个测验
完成时间为 5 小时
Outlier Analysis
This module discusses three different types of outliers (global, contextual, and collective) and how different methods may be used to identify and analyze such outliers. It also covers some advanced methods for mining complex data, as well as the research frontiers of the data mining field.
完成时间为 5 小时
2 个视频 （总计 108 分钟）
关于 Data Mining Foundations and Practice 专项课程
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project.
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