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.
提供方
想要獲得學位學分？了解詳情
您可以獲得計入 Master of Science in Data Science 的 科罗拉多大学波德分校 的學分。
課程信息
8,829 次近期查看
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可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 2 門課程（共 3 門）
中級
data science professionals or domain experts, some experience working with data, successful completion of Data Mining Pipeline
完成時間大約為23 小時
英語（English）
字幕：英語（English）
您將學到的內容有
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.
您將獲得的技能
- outlier analysis
- clustering
- classification
- model evaluation
- frequent pattern analysis
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 2 門課程（共 3 門）
中級
data science professionals or domain experts, some experience working with data, successful completion of Data Mining Pipeline
完成時間大約為23 小時
英語（English）
字幕：英語（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.
開始攻讀學位
該課程屬於 科罗拉多大学波德分校 提供的 1 個在線學位計劃。當您通過大學註冊學分課程而非學位課程並想在線完成時，它會計入 CU-Boulder 學位的學分課時。您只需通過大學申請即可。
授課大綱 - 您將從這門課程中學到什麼
完成時間為 6 小時
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.
完成時間為 6 小時
2 個視頻 （總計 94 分鐘）, 2 個閱讀材料, 1 個測驗
完成時間為 6 小時
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.
完成時間為 6 小時
2 個視頻 （總計 106 分鐘）
完成時間為 6 小時
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.
完成時間為 6 小時
2 個視頻 （總計 105 分鐘）, 1 個閱讀材料, 1 個測驗
完成時間為 5 小時
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.
完成時間為 5 小時
2 個視頻 （總計 108 分鐘）
關於 Data Mining Foundations and Practice 專項課程
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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