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Data Mining for Software Engineering

General

Prefix

SE

Course Number

412

Course Level

Undergraduate

College/School

College of Science and Engineering

Description

Mining interesting information from large data sets. Statistical analysis and machine learning, data mining concepts and techniques, data representation and their similarity/dissimilarity measures, data pre-processing, frequent pattern mining, supervised and unsupervised modeling.

Prerequisites

Credits

Min

3

Max

3

Repeatable

No

Goals and Diversity

MN Goal Course

No

Cultural Diversity

No

Learning Outcomes

Outcome

Identify data mining concepts and technologies.

Outcome

Identify the different types of data, their statistical description, and similarity/dissimilarity measures.

Outcome

Apply basic data pre-processing techniques.

Outcome

Derive interesting patterns using frequent pattern mining techniques.

Outcome

Apply and predict future instances using supervised learning techniques (classification).

Outcome

Apply cluster analysis techniques to group similar data (unsupervised learning).

Outcome

Use a variety of data mining tools.

Course Outline

Course Outline

Concepts and techniques of data mining. 10% Statistical models for data types. 10% Similarity/dissimilarity measures. 10% Data pre-processing. 10% Frequent pattern mining algorithms. 15% Supervised learning and prediction algorithms. 15% Unsupervised learning algorithms. 15% Overview of data mining tools. 15%

Dependencies

Programs

SE412 is a completion requirement for: