Course coordinator
Dr Ruihong Qiu
Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
This course introduces the fundamental process of data science and provides the necessary computational and statistical foundations for further courses in the Master of Data Science. Design thinking methodology will be utilised to approach complex data science problems as a design problem. The data science process will be practiced through case studies in a number of data-intensive domains.
DATA7001 Introduction to Data Science is structured around five comprehensive modules: Problem Solving with Data, Getting the Data I Need, Is My Data Fit for Use?, Making the Data Confess, and Storytelling with Data. These modules have been meticulously selected to cover crucial aspects of data science, guiding students from initial problem definition through to insightful data storytelling.
Assessment in the course consists of four key components designed to develop both technical and collaborative skills. First, the group project requires students to collaborate effectively within groups of three to five members. Each group will identify an engaging data science problem, manage regular meetings, present their findings, and submit detailed project reports. Throughout this process, ample examples, clear instructions, timely reminders, and direct assistance will be provided to ensure students feel confident and well-supported.
The second assessment involves a case study exercise, where students critically evaluate two exemplary group project reports from past semesters. This reflection encourages deeper understanding of high-quality data science practices. Comprehensive Python programming training is provided to equip students with practical skills, evaluated individually through the third assessment item --- Practical tasks involving straightforward coding assignments.
Lastly, an in-semester exam, scheduled several weeks before the semester's conclusion, allows students to consolidate and demonstrate their understanding of core concepts and methodologies covered throughout the course.
Course requirements
Assumed background
Completion of Queensland Year 12 or equivalent Mathematics B and aﾠ3-year undergraduate degree with a major in computer science, IT, statistics, mathematics, engineering, or a program with substantial quantitative competencies sufficient to allow timely completion of the master of data science.ﾠ
Prerequisites
You'll need to complete the following courses before enrolling in this one:
Program entry requirements
Incompatible
You can't enrol in this course if you've already completed the following:
DATA2001 or COMP2011
Restrictions
Restricted to MDataSc students only.
Course contact
Course staff
Lecturer
Dr Ruihong Qiu
Dr Sharon Lee
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
A detailed timetable is available on the Blackboard system.
Aims and outcomes
This course aims to introduceﾠthe fundamental process of data science and provideﾠthe necessary computational and statistical foundations for further courses in the master of data science. Design thinking methodology will be utilised to approach complex data science problems as a design problem. The data science process will be executed in a practical setting using data sceince tools and methods with the help of a number of case studies in data-intensive domains.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Apply design thinking methodology to data science problems
LO2.
Design effective data science processes from problem formulation to persuasive story telling with data
LO3.
Develop data-centric approaches to complex business and scientific problems
LO4.
Reason with the fitness of basic computational and analytical models in data science scenarios
LO5.
Work in teams with diverse backgrounds towards authentic data science solutions
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Essay/ Critique | Case Study | 10% |
13/03/2026 4:00 pm |
| Computer Code, Creative Production/ Exhibition, Paper/ Report/ Annotation, Presentation, Project, Reflection |
Group project
|
50% |
Project Pitch Presentation 20/03/2026 4:00 pm Group Meeting Memorandum 1 27/03/2026 4:00 pm Group Meeting Memorandum 2 17/04/2026 4:00 pm Project Trial Presentation 8/05/2026 4:00 pm Peer Review on Trial Presentations 15/05/2026 4:00 pm Final Presentation 22/05/2026 4:00 pm Group Project Report 29/05/2026 4:00 pm |
| Computer Code, Notebook/ Logbook, Tutorial/ Problem Set | Assignment | 10% |
2/04/2026 4:00 pm |
| Examination |
In-Semester Exam (invigilated)
|
30% |
29/04/2026
The In-Semester Exam is scheduled to take place during the lecture time on Wednesday, 29 April, 2026 (Week 9). This time frame includes both preparation and the collection of exam papers. The duration of the exam itself is 90 minutes. Further details regarding the venue and additional arrangements will be communicated in due course via the Blackboard Ultra system. |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Case Study
- Mode
- Written
- Category
- Essay/ Critique
- Weight
- 10%
- Due date
13/03/2026 4:00 pm
- Learning outcomes
- L01, L02
Task description
The Case Study involves writing a report from a "design thinking" perspective based on a given data science story/case that will be provided.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
The assessment item Case Study is to be submitted online via Blackboard Ultra.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Extensions are limited to 7 days as feedback will be provided within 14 days.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Group project
- Identity Verified
- Team or group-based
- In-person
- Mode
- Activity/ Performance, Written
- Category
- Computer Code, Creative Production/ Exhibition, Paper/ Report/ Annotation, Presentation, Project, Reflection
- Weight
- 50%
- Due date
Project Pitch Presentation 20/03/2026 4:00 pm
Group Meeting Memorandum 1 27/03/2026 4:00 pm
Group Meeting Memorandum 2 17/04/2026 4:00 pm
Project Trial Presentation 8/05/2026 4:00 pm
Peer Review on Trial Presentations 15/05/2026 4:00 pm
Final Presentation 22/05/2026 4:00 pm
Group Project Report 29/05/2026 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
Group project with multiple milestones and deliverables as detailed on the course Blackboard Ultra site. The Course Coordinator reserves the right to vary group marks for each group member in the event of varied contributions to the team effort.
This is a progressive group assessment with a series of tasks due between Week 4 and Week 13.
Feedback on the pitch presentation videos will be provided during the lecture in Week 5 before the census date (31 March).
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Assignments are to be submitted online via Blackboard Ultra unless otherwise specified for a particular assessment item.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Extensions for groupwork are typically not available as this impacts on all members of the team.
Students with valid extension requests either receive team mark or will be required to undertake alternative assessment.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assignment
- Mode
- Written
- Category
- Computer Code, Notebook/ Logbook, Tutorial/ Problem Set
- Weight
- 10%
- Due date
2/04/2026 4:00 pm
- Learning outcomes
- L03, L04
Task description
Three individual coding notebooks, worth 10% in total.
These notebooks will involve coding problem-solving and application of data science techniques on artificial and real data.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
This is to be submitted online via Blackboard Ultra.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Extensions are limited to 7 days as feedback will be provided within 14 days.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
In-Semester Exam (invigilated)
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 30%
- Due date
29/04/2026
The In-Semester Exam is scheduled to take place during the lecture time on Wednesday, 29 April, 2026 (Week 9). This time frame includes both preparation and the collection of exam papers. The duration of the exam itself is 90 minutes. Further details regarding the venue and additional arrangements will be communicated in due course via the Blackboard Ultra system.
- Other conditions
- Time limited, Secure.
- Learning outcomes
- L01, L02, L03, L04
Task description
The In-semester exam will be based on the content covered in lectures, applied classes, and practicals prior to the exam date. The exam will be invigilated and will be completed in person.
Students are not permitted to use a computer, tablet, or mobile phone during this assessment task.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
If you achieve less than 50% of the total available marks in the In-Semester Exam, your grade will be capped at a 3.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Open book examination - any written or printed material is permitted; material may be annotated |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 20 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
If you achieve less than 50% of the total available marks in the In-Semester Exam, your grade will be capped at a 3.
Note that your final percentage will be rounded to the nearest whole number (e.g. 84.5% is a 7), and the final marks are calculated as a simple accumulation of all marks obtained in this semester.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Having Troubles?
If you are having difficulties with any aspect of the course material, you should seek help and speak to the course teaching staff.
If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help; for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).
Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Library resources are available on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Lecture |
Lectures Lectures will cover the foundation concepts of the data science process. These sessions are also used for revisions, mid semester exam, guest lectures on specialist topics and industry, and student discussions and presentations on group project. More details will be provided on course web site Learning outcomes: L01, L02, L03, L04, L05 |
Applied Class |
Applied Class These sessions will be used for coding boot camp, the introduction of Collaborating on code with Github, discussion questions, and group projects technical Q&A. Note that these are not every week - see the course website for details Learning outcomes: L01, L02, L03, L04 |
|
Multiple weeks From Week 2 To Week 10 |
Practical |
Practicals The practical sessions will provide hands-on learning activities in the specialist data science lab including practical methods for each of the course modules, case studies, and group project work. Note that these are not every week - see the course website for details Learning outcomes: L03, L04, L05 |
Additional learning activity information
Feedback on the pitch presentation videos will be provided during the lecture in Week 5 before the census date (31 March).
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
- AI for Assessment Guide
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: