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John C. Hart, Jiawei Han, and ChengXiang Zhai

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.

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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.

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What's inside

Six courses

Data Visualization

Learn general concepts of data mining, basic methodologies, and applications. Dive into pattern discovery, learning in-depth concepts, methods, and applications. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery.

Text Retrieval and Search Engines

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, and social media. Text data are unique in that they are usually generated directly by humans and are thus especially valuable for discovering knowledge about people’s opinions and preferences. This course will cover search engine technologies, which play an important role in any data mining applications involving text data.

Text Mining and Analytics

This course covers techniques for mining and analyzing text data to discover patterns, extract knowledge, and support decision making. Statistical approaches that can be applied to text data in any natural language with no or minimum human effort are emphasized.

Pattern Discovery in Data Mining

Learn data mining basics, methodologies, and applications. Dive into pattern discovery, including concepts, methods, and applications. Explore data-driven phrase mining and applications of pattern discovery. Gain skills in scalable pattern discovery methods, pattern evaluation measures, and mining diverse patterns, sequential patterns, and sub-graph patterns.

Cluster Analysis in Data Mining

Discover cluster analysis basics, methodologies, algorithms, and applications. Includes partitioning (k-means), hierarchical (BIRCH), and density-based (DBSCAN/OPTICS) methods. Learn clustering validation and evaluation techniques. Explore cluster analysis applications.

Data Mining Project

This six-week Project course of the Data Mining Specialization will allow you to apply the learned algorithms and techniques for data mining from the previous courses in the Specialization to solve interesting real-world data mining challenges.

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