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.

School: La Trobe Business School

Credit points: 15

Subject Co-ordinator: Daswin De Silva

Available to Study Abroad Students: No

Subject year level: Year Level 5 - Masters

Exchange Students: No

Subject particulars

Subject rules

Prerequisites: BUS5DWR and BUS5PB; or subject coordinator's approval.

Co-requisites: N/A

Incompatible subjects: N/A

Equivalent subjects: N/A

Special conditions: N/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)

City Campus, 2019, Semester 1, Night

Overview

Online enrolment: Yes

Maximum enrolment size: 120

Enrolment information: Limits of teaching space Order of application

Subject Instance Co-ordinator: Daswin 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