COIT12209 - Data Science

General Information

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 1 - 2024

Term 1 - 2024 Profile
Brisbane
Melbourne
Online
Rockhampton
Sydney
Term 3 - 2024 Profile
Online
Term 1 - 2025 Profile
Brisbane
Melbourne
Online
Rockhampton
Sydney
Term 2 - 2025 Profile
Brisbane
Cairns
Melbourne
Online
Rockhampton
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 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. Practical Assessment 40%
2. Written Assessment 40%
3. Presentation 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

Past Exams

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Previous Feedback

Term 1 - 2024 : The overall satisfaction for students in the last offering of this course was 87.50% (`Agree` and `Strongly Agree` responses), based on a 32% 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: Unit Evaluation
Feedback
Practice materials on the R language were insufficient.
Recommendation
Provide additional materials on R langauge.
Action Taken
Tutorials included practice materials on R language.
Source: Unit Evaluation
Feedback
Big data lecture materials (i.e., Hadoop) were not updated.
Recommendation
Review the lecture slides on big data, and revamp the lecture materials.
Action Taken
New materials on big data were introduced.
Source: Self-evaluation
Feedback
Supplementary materials need to be updated.
Recommendation
Update the weekly supplementary materials.
Action Taken
In Progress
Source: Staff feedback (teaching team)
Feedback
As students are introduced to Python in an earlier semester and given its extensive and versatile data science libraries, running this unit using Python would be highly beneficial for students.
Recommendation
Convert tutorials from R to Python.
Action Taken
In Progress
Source: Staff feedback (teaching team)
Feedback
Hadoop is not appropriate for an introductory level data science unit.
Recommendation
Replace Hadoop with a more work-ready topic (e.g., ethical and professional data science practices).
Action Taken
In Progress
Unit learning Outcomes

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

  1. Discuss and demonstrate data science foundational concepts
  2. Investigate and evaluate applications for data storage, management, retrieval, and analysis and visualisation
  3. Apply knowledge to process data for data driven decision making
  4. Analyse and generate solutions to solve data-related challenges
  5. Demonstrate the knowledge required in using data science skills to solve business problems.

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:

Data Management (DATM)

Business Analysis (BUAN)

Data Analysis (DTAN)

IT Operation (ITOP)

Alignment of Assessment Tasks to Learning Outcomes
Assessment Tasks Learning Outcomes
1 2 3 4 5
1 - Practical Assessment • •
2 - Written Assessment • • • •
3 - Presentation • • •
Alignment of Graduate Attributes to Learning Outcomes
• Introductory Level
• Intermediate Level
• Graduate Level
Graduate Attributes Learning Outcomes
1 2 3 4 5
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 • •
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
1 - Practical Assessment • • • •
2 - Written Assessment • • • • • • •
3 - Presentation • • •