DATA WAREHOUSING AND BIG DATA

BUS5WB

2020

Credit points: 15

Subject outline

This subject focuses on the knowledge and skills necessary to conceptualise, design, develop, implement and use such technology platforms. Students will learn to design and develop an actual data warehouse on cloud computing infrastructure. They will use this warehouse to build cubes for OLAP, write SQL and MDX queries, apply machine learning algorithms, generate reports and dashboards to communicate actionable insights. Students will learn how to configure and use a cloud computing environment (based on Microsoft Azure) to develop a technology platform that encompasses computing, networking, security and data management components. They will execute analytics techniques and machine learning algorithms on a live Hadoop instance to derive insights from unstructured data. Upon completion of the subject, students will demonstrate practical knowledge and skills necessary to construct and evaluates modern technology platform that addresses Big Data complexities

SchoolLa Trobe Business School

Credit points15

Subject Co-ordinatorDaswin De Silva

Available to Study Abroad/Exchange StudentsNo

Subject year levelYear Level 5 - Masters

Available as ElectiveNo

Learning ActivitiesLectures, workshops and assignments.

Capstone subjectNo

Subject particulars

Subject rules

PrerequisitesBUS5PB AND BUS5DWR

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Quota Management StrategyMerit based quota management

Quota-conditions or rulesBy the order of application to subject coordinator.

Special conditionsN/A

Minimum credit point requirementN/A

Assumed knowledgeN/A

Readings

Delivering Business Intelligence with Microsoft SQL Server

Resource TypeBook

Resource RequirementRecommended

AuthorLarson

Year2016

Edition/VolumeN/A

PublisherMcGraw Hill

ISBN978 007 175 9380

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling

Resource TypeBook

Resource RequirementRecommended

AuthorKimball and Ross

Year2013

Edition/VolumeN/A

PublisherWiley

ISBN978 111 853 0801

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Career Ready

Career-focusedNo

Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

COMMUNICATION - Communicating and Influencing
DISCIPLINE KNOWLEDGE AND SKILLS
INQUIRY AND ANALYSIS - Creativity and Innovation
INQUIRY AND ANALYSIS - Research and Evidence-Based Inquiry

Intended Learning Outcomes

01. Design and develop a data warehouse, and use this warehouse to build cubes for OLAP, write SQL and MDX queries, apply machine learning algorithms, as well as generate reports and dashboards to communicate actionable insights
02. Understand the need for computing, networking, security, data management and large-scale data processing infrastructure, and know how to configure and use a cloud computing environment to address such needs.
03. Demonstrate practical knowledge and skills necessary to construct, compare and evaluate a modern technology platform that addresses Big Data complexities.

Subject options

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Start date between: and    Key dates

City Campus, 2020, Semester 1, Night

Overview

Online enrolmentYes

Maximum enrolment size120

Subject Instance Co-ordinatorDaswin De Silva

Class requirements

Lecture/Workshop Week: 10 - 22
One 3.00 h lecture/workshop per week on weekdays at night from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*

Technology architecture design and critical evaluation Individual. Word equivalency: 21 00

N/AAssignmentIndividualNo30 SILO1, SILO2

Construct, use and synthesis data warehouse Individual. Word equivalency: 2300

N/AAssignmentIndividualNo40 SILO1, SILO2

Adapt, use and evaluate a cloud computing environment for machine learning and big data complexities Individual. Word equivalency: 2100

N/AAssignmentIndividualNo30 SILO1, SILO3