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CST 6307 - Data Mining
The study of algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance. Covers knowledge discovery that deals with extracting useful knowledge from raw data using processes including: data selection, cleaning, coding, statistical analysis, pattern recognition, machine learning techniques, reporting and visualizing generated structures. Important related technologies such as Data Warehousing and On-line Analytical Processing (OLAP) are also discussed. Processes are illustrated by examples of practical applications. Prerequisite: Admission to the Certificate program in Data Analytics 3.000 Credit hours 3.000 Lecture hours Levels: Graduate Schedule Types: Lecture Computer Science Department

CST 6307 - Data Mining

The study of algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance. Covers knowledge discovery that deals with extracting useful knowledge from raw data using processes including: data selection, cleaning, coding, statistical analysis, pattern recognition, machine learning techniques, reporting and visualizing generated structures. Important related technologies such as Data Warehousing and On-line Analytical Processing (OLAP) are also discussed. Processes are illustrated by examples of practical applications.

Prerequisite: Admission to the Certificate program in Data Analytics

3.000 Credit hours
3.000 Lecture hours

Levels: Graduate
Schedule Types: Lecture

Computer Science Department

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Catalog Entries		Fall 2021 May 18, 2024

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