Entrance Skills

CURRICULUM COMMITTEE APPROVAL: S06

CUYAMACA COLLEGE

OFFICIAL COURSE OUTLINE

BUSINESS 242 – DATA MINING

2 hours lecture, 3 hours laboratory, 3 units

Catalog Description

This class provides an introduction to the fundamental concepts of data mining. The class will explore motivation for and

applications of data mining and survey current techniques and models used in data mining. Data mining development cycle

and potential pitfalls of machine learning will also be covered.

Recommended Preparation

CIS 140 or equivalent

Entrance Skills

Without the following skills, competencies and/or knowledge, students entering this course will be highly unlikely to succeed:

1) Basic knowledge of logical and physical database design characteristics.

2) Understand basic principles of data integrity.

3) Relationships between tables and columns (primary and foreign keys, one-to-one, one-to-many, and many-to-many).

4) Understand how data is stored and organized in database tables (rows and columns).

5) Ability to create database forms and reports.

6) Understanding of basic database query techniques using both simple queries and complex query techniques.

Course Content

1) Data mining principles:

a. Identify business objectives.

b. Determine resources, constraints, assumptions for a data analysis project.

c. Discuss new applications of data mining such as text mining and web mining.

2) Data mining goals:

a. Create project objectives from business goals.

b. Define success criteria and implement a project plan to produce the desired results.

3) Collect, describe, explore and verify data quality:

a. List datasets acquired and the methods used to acquire them.

b. Describe the data which has been acquired including the format of the data.

c. Explore the key and target attributes of a prediction task.

d. Produce data quality verification reports to identify if problems exist.

4) Describe the dataset that will be used for the modeling or the major analysis of work in a data mining project:

a. Decide on the data to be used for analysis.

b. Raise the quality of the data to the level required by the selected analysis techniques.

c. Produce derived attributes, entire new records or transformed values for existing attributes.

d. Merge two or more tables together that have different information about the same objects.

5) Use data modeling techniques using decision trees or neural network generation with back propagation:

a. Document modeling techniques and assumptions on the data.

b. Generate test designs or procedures to validate the quality of the model.

c. Build an actual model and verify parameters which can be adjusted along with the rational for the choice of parameter

settings.

6) Evaluate results of a data model:

a. Assess the data model to verify how results meet the business objectives and modify the model to improve the

results as necessary.

b. Review results to identify other findings which are not necessarily related to the original business objective.

7) Develop a data mining strategy for deployment:

a. Create a maintenance policy to avoid incorrect usage of data mining results.

b. Produce a final data mining report including both the successes and failures in the project.

Course Objectives (Expected Student Learning Outcomes)

Students will be able to:

1) Develop a list of goals and objectives for a business, select the data to be mined and pre-process the data, specify the

patterns to be mined, define the constraints on the desired patterns and then post-process the extracted patterns.

2) Describe a dataset required for modeling and analysis and list methods used to acquire the dataset.

3) Use a dataset to identify interesting data subsets which may require further analysis.

4) Apply various modeling techniques and assess model results against business goals.

5) Use data modeling techniques with decision trees or neural network generation including assumptions on the data to

generate test designs or procedures which can validate the quality of the model.

6) Build a data model and verify parameters which can be adjusted including rationale for the choice of parameter settings.

7) Assess model results against business goals, identifying the success of the model along with a plan for deployment. BUS 242 Page 2 of 2

Method of Evaluation (Measuring Student Learning Outcomes with Representative Assignments)

A grading system will be established by the instructor and implemented uniformly. Grades will be based on demonstrated

proficiency in subject matter determined by multiple measurements for evaluation, one of which must be essay exams, skills