BIT 3200 Business Intelligence and Data Warehousing (4 CU)
(a) Course Description: This course introduces the student on how companies can build data
warehouses and utilize business intelligence for decision-making hence saving money and
increasing profit. Several, initiatives ranging from supply chain integration to compliance
with organizational/government-mandated reporting requirements depend on well
designed data warehouse architecture. Therefore the course introduces you to the main
components of a data warehouse for business intelligence applications. This will include
how a data warehouse fits into the overall strategy of a complex enterprise, how to develop
data models, data mats useful for business intelligence, and how to combine data from
disparate sources into a single database that comprises the core of your data warehouse.
The course will also explore how to define and specify useful management reports from
warehouse data.
(b)
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Aims: The aims of the course are
Give students the understanding on the role and operation of data warehouses
To equip students with skills of developing data warehouses
To equip students with skills of maintaining existing data warehouses
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To equip students with skills of manipulating data warehouses to generate information for
business decision making
(c) Learning outcomes: On completion of this course, the students will be able to:
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Explain how data warehouse combined with good business intelligence can increase a
company’s bottom line.
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Describe the components of a data warehouse.
Describe different forms of business intelligence that can be gleaned from a data
warehouse and how that intelligence can be applied toward business decision-making.
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Develop dimensional models from which key data for critical decision-making can be
extracted.
Sketch out the process for extracting data from disparate databases and data sources, and
then transforming the data for effective integration into a data warehouse.
Load extracted and transformed data into the data warehouse.
(d) Teaching and learning pattern: Teaching will be in form of class lectures, tutorials and
lab demonstrations as well as class presentations.
(e) Indicative content:
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Data warehouse concepts: partitioning, granularity, record of source, and metadata
Building viable decision support environments.
Architect development,
Data migration and integration,
Use of operational data stores, and transactional systems.
(f) Assessment method: Assessment will be in form of tests and practical assignments
(40%) and final written examination (60%) (g)
Reference books:
(i) Data warehousing fundamentals by Paulraj Ponniah, 2001, ISBN: 978-0-471-41254.