- International Fees
International fees are typically three times the amount of domestic fees. Exact cost will be calculated upon completion of registration.
Continuing on from COMP 1630 this course is aimed at students who already understand relational database, data modeling and the importance of improving Data Quality (DQ). Survey topics include the business impact of DQ, data profiling techniques, DQ capability/maturity models, Data Governance, DQ improvement roadmaps and approaches to gaining executive support for the DQ improvement program. In class labs, homework assignments and a project focus on how to use Data Quality techniques and tools. Successful participants learn how to apply a data quality framework to data migration projects and are better prepared to move on to the specific Business Intelligence Analytic tools covered in: COMP 4679 and COMP 4681.
- 60% in COMP 1630
Below is one offering of COMP 3839 for the Winter 2024 term.
Fri Jan 12 - Fri Feb 16 (6 weeks)
- 6 weeks
- CRN 84217
- Domestic fees $424.15International fees are typically three times the amount of domestic fees.
Class meeting times
|Jan 12 - Feb 16||Fri||18:00 - 21:00||Downtown DTC Rm. 389|
Course outline TBD — see Learning Outcomes in the interim.
- Departmental approval needed
- International fees are typically three times the amount of domestic fees. Exact cost will be calculated upon completion of registration.
Please email firstname.lastname@example.org for Departmental approval. Include your Student number (A0#) and COMP__ and preferred CRN __ and Program Declaration____. Course is 18 hours on campus. Late registration is not permitted.
Upon successful completion of this course, the student will be able to:
- Define the business value and goals of Data Quality (DQ) improvement.
- Measure the DQ of data sets with data profiling to establish baselines for improvement.
- Use simple statistical measures for analyzing DQ at columnar, record and table levels.
- Assess business process capabilities through the DQ of their data sets.
- Discuss DQ root causes and prioritize mitigation for discovered DQ issues.
- Document data flows using simple process diagrams and SIPOCs (Suppliers, Inputs,Process, Outputs, and Customers).
- Integrate data quality improvement initiatives into development projects such as data warehouses, data migration and upgrades.
- Outline how to connect data stewardship to long term data governance.
Effective as of Fall 2015
Data Quality Improvement (COMP 3839) is offered as a part of the following programs:
School of Computing and Academic Studies
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Programs and courses are subject to change without notice.