Data Mining

 

 


 

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E.g., 2016-12-08
E.g., 2016-12-08
E.g., 2016-12-08
Dec 12th 2016

Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.

Average: 5.5 (2 votes)
Dec 12th 2016

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.

Average: 6.5 (2 votes)
Dec 5th 2016

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.

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Dec 5th 2016

This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.

Average: 8.5 (2 votes)
Dec 5th 2016

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery.

Average: 8 (1 vote)
Dec 5th 2016

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.

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Nov 28th 2016

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis.

Average: 10 (2 votes)
Oct 3rd 2016

This 5 week MOOC will introduce data mining concepts through practical experience with the free Weka tool.

Average: 6.8 (6 votes)
Oct 3rd 2016

This course follows on from Data Mining with Weka and provides a deeper account of data mining tools and techniques. Again the emphasis is on principles and practical data mining using Weka, rather than mathematical theory or advanced details of particular algorithms.

Average: 9.5 (2 votes)
Apr 13th 2016

El contenido de este curso consiste en una introducción al mundo del Big Data orientada a conocer cómo extraer valores escondidos de los datos manejados en cualquier negocio. Se explicarán técnicas de minería de datos aplicadas en el Big Data. También se considerarán aspectos de seguridad y privacidad.

Average: 8 (1 vote)
Jul 1st 2015

Learn how and when to use key methods for educational data mining and learning analytics on large-scale educational data.

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Apr 6th 2015

Explore applications of linear algebra in the field of data mining by learning fundamentals of search engines, clustering movies into genres and of computer graphics by posterizing an image.

Average: 10 (1 vote)
Feb 23rd 2015

Learn to use linear algebra in computer graphics by making images disappear in an animation or creating a mosaic or fractal and in data mining to measure similarities between movies, songs, or friends.

Average: 9 (4 votes)
Apr 21st 2014

Learn both theory and application for basic methods that have been invented either for developing new concepts – principal components or clusters, or for finding interesting correlations – regression and classification. This is preceded by a thorough analysis of 1D and 2D data.

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