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. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.
About this Course
Learner Career Outcomes
Learner Career Outcomes
University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
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TOP REVIEWS FROM DATA VISUALIZATION
A fairly interesting course with a good instructor. The course gave me a chance to play with my visualization tools in order to expand my usage rather than being in a rush to complete my tasks.
Very useful course. It enlightens my ways to data visualization. I knew some concepts, but in a disorganized way and not knowing how. This course fills these gaps. It is tremendously helpful.
Good course, very well structured and with interesting assignments. Some (especially first) lessons are more of a general culture but most are very helpful and allow to learn a lot of things.
I enjoyed a great deal of this course. However, I felt that the course could be improved by adding in a few programming implementations of visualization. Thank you for teaching this subject!
About the Data Mining Specialization
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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