- International Fees
International fees are typically three times the amount of domestic fees. Exact cost will be calculated upon completion of registration.
Course details
There is a wealth of information stored in each organization's operational data store, with an increasing demand to mine that data and use it for competitive advantage. Data Mining/Business Intelligence enables organizations to move from merely good to great by supporting better business decision making. Course participants will gain an understanding of the principles of data mining and the various techniques and algorithms through classroom instructions and by conducting a research paper in data mining / business intelligence technology. Through participation in a group project, students are exposed to hands-on experience of designing and using a data mining tool to consolidate their learning experience. Some of the Data Mining predictive and inference techniques learned in the course can apply to Big Data and Data Science. The course is divided into three main components: (1) Understanding of knowledge discovery, business intelligence, data mining concepts, techniques, selected algorithms and associated applications, as well as overview of the underlying large data storage architecture such as data warehouse and data mart; (2) Participation in a group project in design and building a model using a business intelligence data mining tool; (3) Conduct an individual research paper on a data mining algorithm and/or tool for a business application.
Prerequisite(s)
- Acceptance into the Bachelor of Science in Applied Computer Science (BScACS) program
Credits
3.0
- Not offered this term
- This course is not offered this term. Please check back next term or subscribe to receive notifications of future course offerings and other opportunities to learn more about this course and related programs.
Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Discuss business intelligence / data mining applications and related issues.
- Prepare and preprocess data for data mining using techniques such as aggregation, sampling, dimensionality reduction, subset selection, discretization, binarization and variable transformation.
- Identify the use of appropriate measures of similarity and dissimilarity between data objects.
- Describe knowledge discovery, business intelligence and the various data mining concepts and algorithms, such as classification, association, clustering and anomaly detection.
- Apply appropriate model/non-model base and algorithm using a data mining tool.
- Explain key architecture components and issues of large data stores.
- Design and implement a data mining group project from end-to-end.
- Conduct an individual research paper on a data mining algorithm and/or tool for a business application.
Effective as of Fall 2018
Related Programs
Selected Topics in Computer Systems - Data Mining (COMP 7611) is offered as a part of the following programs:
School of Computing and Academic Studies
- Applied Computer Science (Database Option)
Bachelor of Science Part-time
- Applied Computer Science (Human Computer Interface Option)
Bachelor of Science Part-time
- Applied Computer Science (Network Security Administration Option)
Bachelor of Science Part-time
- Applied Computer Science (Wireless and Mobile Applications Development Option)
Bachelor of Science Part-time
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