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Course profile

Introduction to Data Science (DATA7001)

Study period
Sem 2 2025
Location
St Lucia
Attendance mode
In Person

Course overview

Study period
Semester 2, 2025 (28/07/2025 - 22/11/2025)
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

Restrictions

Restricted to MDataSc students only.

Course staff

Course coordinator

Lecturer

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
  • Online
5%

22/08/2025 4:00 pm

Computer Code, Creative Production/ Exhibition, Paper/ Report/ Annotation, Presentation, Project, Reflection Group project
  • Identity Verified
  • Team or group-based
  • In-person
55%

Group Meeting Memorandum 1 15/08/2025 4:00 pm

Project Pitch Presentation 22/08/2025 4:00 pm

Group Meeting Memorandum 2 5/09/2025 4:00 pm

Group Meeting Memorandum 3 26/09/2025 4:00 pm

Project Trial Presentation 10/10/2025 4:00 pm

Peer Review on Trial Presentations 17/10/2025 4:00 pm

Final Presentation 24/10/2025 4:00 pm

Group Project Report 31/10/2025 4:00 pm

Reflective Essay 31/10/2025 4:00 pm

Computer Code, Notebook/ Logbook, Tutorial/ Problem Set Practical
  • Online
10%

12/09/2025 4:00 pm

Examination In-Semester Exam (invigilated)
  • Hurdle
  • Identity Verified
  • In-person
30%

22/09/2025

The In-Semester Exam is scheduled to take place from 18:00 to 20:00 on Monday, September 22, 2025 (Week 09). 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 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

  • Online
Mode
Written
Category
Essay/ Critique
Weight
5%
Due date

22/08/2025 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.

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.

The maximum extension is limited to 7 days in order to ensure timely feedback for the entire class.

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
55%
Due date

Group Meeting Memorandum 1 15/08/2025 4:00 pm

Project Pitch Presentation 22/08/2025 4:00 pm

Group Meeting Memorandum 2 5/09/2025 4:00 pm

Group Meeting Memorandum 3 26/09/2025 4:00 pm

Project Trial Presentation 10/10/2025 4:00 pm

Peer Review on Trial Presentations 17/10/2025 4:00 pm

Final Presentation 24/10/2025 4:00 pm

Group Project Report 31/10/2025 4:00 pm

Reflective Essay 31/10/2025 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 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 05 before the census date (31 Aug).

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 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.

Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14-21 days. The maximum extension is limited to 7 days in order to ensure timely feedback for the entire class.

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.

Practical

  • Online
Mode
Written
Category
Computer Code, Notebook/ Logbook, Tutorial/ Problem Set
Weight
10%
Due date

12/09/2025 4:00 pm

Learning outcomes
L03, L04

Task description

3 individual practical sheets, worth 10% in total.

These practical notebooks will involve 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.

Deferral or extension

You may be able to apply for an extension.

Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14-21 days. The maximum extension is limited to 7 days in order to ensure timely feedback for the entire class.

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

22/09/2025

The In-Semester Exam is scheduled to take place from 18:00 to 20:00 on Monday, September 22, 2025 (Week 09). 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 system.

Other conditions
Time limited.

See the conditions definitions

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 receive less than 50% of the total available marks in the In-Semester Exam, the highest grade you may achieve is 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

This is a paper-based exam.

Deferral or extension

You may be able to defer this exam.

Late submission

You will receive a mark of 0 if this assessment is submitted late.

This is an invigilated exam; therefore, late submissions cannot be accepted to ensure fairness.

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 receive less than 50% of the total available marks in the In-Semester Exam, the highest grade you may achieve is 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.

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Learning period Activity type Topic
Multiple weeks

From Week 1 To Week 13
(28 Jul - 02 Nov)

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
(04 Aug - 12 Oct)

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

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:

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: