COIT20253 - Business Intelligence using Big Data

General Information

Unit Synopsis

Big data is a popular term used to describe the exponential growth and availability of structured and unstructured data. In this unit, you will explore big data within the context of business intelligence. In this unit, you will learn concepts of business intelligence, alignment of big data to business intelligence and how big data technologies can be used in building organisational business intelligence. You will learn how big data is changing businesses and how organisations can take advantage of big data in decision making. You will learn how organisations are integrating non-traditional unstructured data with the traditional structured enterprise data to do the business intelligence analysis. In order to understand these, you will learn big data analytical tools and technologies to help solve authentic business problems and make effective business decisions.

Details

Level Postgraduate
Unit Level Not Applicable
Credit Points 6
Student Contribution Band 2
Fraction of Full-Time Student Load 0.125
Pre-requisites or Co-requisites

Prerequisites: COIT20250 e-Business Systems, COIT20245 Introduction to Programming and COIT20247 Database Design and Development.

Anti-Requisites: If you have completed unit COIT20236 then you cannot take this unit.

Class Timetable View Unit Timetable
Residential School No Residential School

Unit Availabilities from Term 1 - 2019

Term 1 - 2019 Profile
Brisbane
Melbourne
Online
Rockhampton
Sydney
Term 2 - 2019 Profile
Brisbane
Melbourne
Online
Sydney

Attendance Requirements

All on-campus students are expected to attend scheduled classes – in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).

Assessment Overview

Recommended Student Time Commitment

Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.

Assessment Tasks

Assessment Task Weighting
1. Written Assessment 35%
2. Presentation 25%
3. Project (applied) 40%

This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%).

Consult the University’s Grades and Results Procedures for more details of interim results and final grades

Past Exams

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Previous Feedback

Term 1 - 2018 : The overall satisfaction for students in the last offering of this course was 4.4 (on a 5 point Likert scale), based on a 46.15% response rate.

Feedback, Recommendations and Responses

Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.

Source: Through Moodle site
Feedback
Interesting unit, very practical, excellent, topics are good and materials provided are nice
Recommendation
Continue providing current research study materials
Action Taken
Webinar Series on Big Data is provided through Zoom. Current research papers are provided as Unit Resource Online(URO) on Moodle.
Source: Through Moodle site
Feedback
Slides should be more informative
Recommendation
Continue enhancing lecture slides and assessments
Action Taken
There has been a continuous enhancement in the lecture slides. Lecture slides have been rewritten to address this. Detail specifications on assessments have been provided. Zoom sessions on Assessment clarifications have been conducted. An online Leganto reading list has been activated and current Library Unit Resources Online (UROs) matched to unit code have been added.
Source: Have Your Say Moodle Evaluation.
Feedback
Lectures are clear and knowledge passed is useful and contextualised for real case scenarios. Help and support is provided.
Recommendation
Keep providing the support and delivering useful and contextualised knowledge to our students' and continue to help them in their journey to the completion of the course. Continue with the good practices of teaching.
Action Taken
Nil.
Source: Have Your Say Moodle Evaluation.
Feedback
More practical use of Hadoop Application.
Recommendation
More examples of Hadoop applications will be provided. The unit contents are enhanced on a regular basis to fit version changes of Hadoop.
Action Taken
Nil.
Unit learning Outcomes

On successful completion of this unit, you will be able to:

  1. Apply concepts and principles of big data to evaluate and explain how large volume of structured and unstructured data are managed in an organisation
  2. Analyse critically and reflect on how organisations are including non-traditional valuable data with the traditional enterprise data to do the business intelligence analysis
  3. Critically analyse and evaluate different big data technologies used for decision making in an organisation
  4. Develop big data strategy for data-centric organisations to meet client requirements
  5. Apply big data architecture, tools, and technologies for decision making and problem solving in the organisational context.

Australian Computer Society (ACS) recognises the Skills Framework for the Information Age (SFIA). SFIA is in use in over 100 countries and provides a widely used and consistent definition of ICT skills. SFIA is increasingly being used when developing job descriptions and role profiles.

ACS members can use the tool MySFIA to build a skills profile at https://www.acs.org.au/professionalrecognition/mysfia-b2c.html

This unit contributes to the following workplace skills as defined by SFIA. The SFIA code is included:

  • Research(RSCH)
  • Data Management (DATM)
  • Emerging Technology Monitoring (EMRG)
  • Data Analysis (DTAN)
  • Application Support (ASUP) 
  • Analytics (INAN)

Alignment of Assessment Tasks to Learning Outcomes
Assessment Tasks Learning Outcomes
1 2 3 4 5
1 - Written Assessment
2 - Presentation
3 - Project (applied)
Alignment of Graduate Attributes to Learning Outcomes
Advanced Level
Professional Level
Graduate Attributes Learning Outcomes
1 2 3 4 5
1 - Knowledge
2 - Communication
3 - Cognitive, technical and creative skills
4 - Research
5 - Self-management
6 - Ethical and Professional Responsibility
Alignment of Assessment Tasks to Graduate Attributes
Advanced Level
Professional Level
Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7
1 - Written Assessment
2 - Presentation
3 - Project (applied)