bus5wb data warehousing and big data
DATA WAREHOUSING AND BIG DATA
BUS5WB
2019
Credit points: 15
Subject outline
Modern organisations accumulate increasing volumes of data in both structured and unstructured form. A significant task in business analytics is to determine suitable information/data management solutions, architectures and platforms to suit business problems and context. In this subject you will focus on the knowledge and skills necessary to conceptualise, design and develop information/data management solutions. You will cover key topics in structured information management: relational modelling, SQL, dimensional modelling, data warehouse design and development, ETL, cubes, multidimensional and tabular queries, data mining on cubes. You will examine Big Data technology. Microsoft Azure platform will be used to understand the Apache Hadoop stack, MapReduce and some IoT programming. You will execute analytics algorithms on information management platforms to derive insights from unstructured data, analyse and integrate with output from the data warehouse. Upon successful completion of the subject, you will possess the expertise necessary to build data warehouses and address real world Big Data problems.
SchoolLa Trobe Business School
Credit points15
Subject Co-ordinatorDaswin De Silva
Available to Study Abroad StudentsNo
Subject year levelYear Level 5 - Masters
Exchange StudentsNo
Subject particulars
Subject rules
Prerequisites BUS5DWR and BUS5PB; or subject coordinator's approval.
Co-requisitesN/A
Incompatible subjectsN/A
Equivalent subjectsN/A
Special conditionsN/A
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | Delivering Business Intelligence with Microsoft SQL Server | Recommended | Larson (2016) | McGraw Hill |
| Readings | The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling | Recommended | Kimball and Ross (2013) | Wiley |
Graduate capabilities & intended learning outcomes
01. Build a data warehouse and demonstrate data integration capabilities from diverse data sources.
- Activities:
- Lectures and workshops
- Related graduate capabilities and elements:
- Inquiry and Analytical Skills(Creative Problem-solving,Inquiry/Research)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
02. Develop a query strategy for online analytical processing (OLAP) and execute multidimensional and tabular queries that generate reports and lead to data mining activities.
- Activities:
- Lectures, workshops and assignments
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing)
- Inquiry and Analytical Skills(Creative Problem-solving,Inquiry/Research)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
03. Understand the need for large-scale processing infrastructure in business analytics and execute analytics algorithms on such an instance to derive insights from unstructured data.
- Activities:
- Lecture and workshops
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing)
- Inquiry and Analytical Skills(Creative Problem-solving,Inquiry/Research)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
Subject options
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City Campus, 2019, Semester 1, Night
Overview
Online enrolmentYes
Maximum enrolment size120
Enrolment information Limits of teaching space Order of application
Subject Instance Co-ordinatorDaswin De Silva
Class requirements
Lecture/WorkshopWeek: 10 - 22
One 3.0 hours lecture/workshop per week on weekdays at night from week 10 to week 22 and delivered via face-to-face.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| Dimensional design for an enterprise level business problem | Individual. Word equivalency: 2300 | 40 | 01 |
| Cube, OLAP and report development | Individual. Word equivalency: 2100 | 30 | 01, 02 |
| Report on big data analytics architectures and/or applications | Individual. Word equivalency: 2100 | 30 | 02, 03 |