Statement of Course Outcomes

Course Number: CS 513

Course Name: Knowledge Discovery and Data Mining

Course Coordinator: Wendy Hui Wang

Graduate or Undergraduate Equivalent: None

Catalog Description: The course introduces fundamental and practical tools, techniques, and algorithms for Knowledge Discovery and Data Mining (KD&DM). It provides a balanced approach between methods and practices. On the methodological side, it covers the key techniques for transforming data into business intelligence including: Data Preprocessing, Data Quality, K-Nearest Neighborhood Algorithm, Machine Learning (ML) and Decision Trees (DT: C4.5, and CART), Artificial Neural Networks (ANN), Clustering, and Algorithm Evaluation Techniques. On the practical side, a number of case studies from the real world applications are analyzed and discussed to illustrate the practical significance of the various techniques and reinforce learning. A current list of KD&DM software products and algorithms are also introduced.

Prerequisite: Knowledge of probability.

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Course Outcomes: The graduates of this course will be prepared to:
(Each course outcome is followed in parentheses by the Program Outcome to which it relates.)

1.        [Information Systems] Define, describe, and clearly state the objectives of Knowledge Discovery and Data Mining [core:requirements]

2.        [Information Systems] Identify relevant data and corresponding databases and data warehouses [core:requirements]

3.        [Computer Science] Describe how to access relevant data [core:environments]

4.        [Computer Science] Preprocess the data (clean, integrate, transform) [core:problem-solving]

5.        [Computer Science] Specify the proper algorithm(s) and data mining technique(s) [core:problem-solving]

6.        [Computer Science] Identify and or develop software to execute the specified algorithm(s)/data mining technique(s) [core:problem-solving]

7.        [Computer Science] Mine and discover models, patterns, dependencies that will enable predictions, make intelligent business and operation decisions, learn and extract nuggets of knowledge [core:problem-solving]

8.        [Information Systems] Present and document results [core:problem-solving]

9.        [Information Systems] Input the extracted knowledge to the next iterative steps [core:problem-solving]