BUSN11028 - The ABC's (Analysis, Bias and Connections) of Big Data

General Information

Unit Synopsis

In this unit, you will engage with the ABC (analysis, bias and connections) of big data. You will focus on how data mining and analysis can drive decision-making processes in both business and personal contexts. You will learn about the role of big data in strategic decision-making and the application of statistical techniques and software to analyse and interpret data. Throughout the unit, simulated activities will provide practical experience in data interpretation. Additionally, you will explore the critical issues of ethics and privacy in the age of big data, ensuring that your data-driven decisions are effective, ethically sound, and respectful of privacy concerns.

Details

Level Undergraduate
Unit Level 1
Credit Points 6
Student Contribution Band 10
Fraction of Full-Time Student Load 0.125
Pre-requisites or Co-requisites There are no pre-requisites for the unit.

Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).

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Residential School No Residential School

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Higher Education Unit Availabilities from Term 2 - 2024

There are no Higher Education availabilities for this unit on or after Term 2 - 2024
Assessment Overview

Recommended Student Time Commitment

Each 6-credit Undergraduate 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. Critical Review 40%
2. Practical Assessment 40%
3. Portfolio 20%

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 Policy for more details of interim results and final grades

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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.

Unit learning Outcomes

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

  1. Analyse the ethical considerations and privacy concerns associated with using big data in various contexts
  2. Apply statistical techniques and use appropriate software to analyse and interpret big data within a personal and business context
  3. Justify using big data to enhance decision-making processes for personal and business objectives
  4. Evaluate the use of big data insights into practical, ethical, and impactful decisions for business and personal objectives.

Alignment of Assessment Tasks to Learning Outcomes
Assessment Tasks Learning Outcomes
1 2 3 4
1 - Critical Review • • •
2 - Practical Assessment • • •
3 - Portfolio • •
Alignment of Graduate Attributes to Learning Outcomes
• Introductory Level
• Intermediate Level
• Graduate Level
Graduate Attributes Learning Outcomes
1 2 3 4
1 - Communication • •
2 - Problem Solving •
3 - Critical Thinking • •
4 - Information Literacy •
5 - Team Work •
6 - Information Technology Competence •
7 - Cross Cultural Competence •
8 - Ethical practice •
9 - Social Innovation •
10 - Aboriginal and Torres Strait Islander Cultures •
Alignment of Assessment Tasks to Graduate Attributes
• Introductory Level
• Intermediate Level
• Graduate Level
Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7 8 9 10