Unit Synopsis
This unit focuses on the foundational concepts of data science. Digital data is growing at a very fast rate with data being the underlying driver of the knowledge economy. This unit will prepare you with foundational knowledge and practical skills about data collection, representation, storage, retrieval, management, analysis, and visualisation through the exploration of data-related challenges. You will also learn the impact of big data and business analytics on business performance to cater for the development of useful information and knowledge in an attempt to achieve data-driven decision making.
Details
| Level | Undergraduate |
|---|---|
| Unit Level | 2 |
| Credit Points | 6 |
| Student Contribution Band | SCA Band 2 |
| Fraction of Full-Time Student Load | 0.125 |
| Pre-requisites or Co-requisites |
Prerequisite: COIT11226 Systems Analysis Co-requisite: COIT11237 Database Design & Implementation 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). |
| Class Timetable | View Unit Timetable |
| Residential School | No Residential School |
Unit Availabilities from Term 2 - 2027
Term 2 - 2027 Profile
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).
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
To see assessment details from an earlier availability, please search via a previous term.
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%).
Past Exams
All University policies are available on the Policy web site, however you may wish to directly view the following policies below.
This list is not an exhaustive list of all University policies. The full list of policies are available on the Policy web site.
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: Teaching team feedback
The lecture on Machine Learning System Design lacks practical components.
Update the lecture topic “An Overview of Machine Learning System Design” to include practical components to complement theory.
Added more learning resources in Week 11 (Machine Learning System Design), including a new reference book and relevant web-based resources, to give students more practical examples and strengthen their understanding of machine learning system design concepts.
Source: Teaching team feedback
The unit currently lacks a hands-on example of bias in data analysis, which is important to reinforce professional data science practices.
Integrate a hands-on example of bias in data analysis into one of the tutorials.
A hands-on example of bias in data analysis was incorporated into a tutorial, supported by additional case studies, web resources, and videos to strengthen students' understanding of bias in data science practice.
Source: Student Feedback
Students indicated that the expectations and marking criteria for Assessment 3 were unclear, particularly regarding the requirement to submit a recorded presentation and also present live.
Provide clear instructions and marking criteria for Assessment 3, explaining the purpose of the recorded and live presentation components and how each will be assessed.
In Progress
To see Learning Outcomes from an earlier availability, please search via a previous term.