This course introduces the key steps involved in the data mining pipeline, including data understanding, data preprocessing, data warehousing, data modeling, interpretation and evaluation, and real-world applications.
課程信息
data science professionals or domain experts, some experience working with data
您將學到的內容有
By the end of this course, you will be able to identify the key components of the data mining pipeline and describe how they're related.
You will be able to identify particular challenges presented by each component of the data mining pipeline.
You will be able to apply techniques to address challenges in each component of the data mining pipeline.
您將獲得的技能
- Data Pre-Processing
- Data Warehousing
- data understanding
- data mining pipeline
data science professionals or domain experts, some experience working with data
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.
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授課大綱 - 您將從這門課程中學到什麼
Data Mining Pipeline
This module provides an introduction to data mining and data mining pipeline, including the four views of data mining and the key components in the data mining pipeline.
Data Understanding
This module covers data understanding by identifying key data properties and applying techniques to characterize different datasets.
Data Preprocessing
This module explains why data preprocessing is needed and what techniques can be used to preprocess data.
Data Warehousing
This module covers the key characteristics of data warehousing and the techniques to support data warehousing.
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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