Course coordinator
Dr Xin Xia
Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
This course will utilize a scenario-based methodology to approach simple and complex data science problems in various data-intensive sectors and domains.
COMP2011 introduces students to the complete data science lifecycle through a scenario-based approach grounded in real-world problems from diverse data-intensive sectors. Students will develop competencies in formulating data-driven questions, acquiring and preprocessing data, applying analytical methods, and interpreting results to support data-informed decisions. The course emphasizes both technical and critical thinking skills and covers structured, unstructured, and temporal data.
This course will be composed of four modules:
• Module 1 (Weeks 1–2): Data Science Process
The course begins by introducing the full data science process — from problem formulation and data collection to modelling, analysis, and storytelling. Students will explore how this lifecycle applies across domains through scenario-based examples.
• Module 2 (Weeks 3–6): Structured Data and Hindsight
This module focuses on handling and analysing structured data. Students will learn how to clean, transform, visualise, and extract retrospective insights using techniques such as exploratory data analysis.
• Module 3 (Weeks 7–9): Unstructured Data and Insight
Students will shift their focus to unstructured data, particularly text. The module explores data collection, problem formulation, data preparation, data analysis techniques for natural language processing to extract meaningful insights.
• Module 4 (Weeks 10–12): Time Series and Foresight
The final module equips students with tools to work with temporal data. Emphasis is placed on identifying trends, seasonality, and forecasting future outcomes through time series analysis methods.
Students will complete a formative multiple-choice and short-answer quiz in Blackboard during Week 4. This task is not assessed but provides automated feedback to help students understand their progress prior to the Census Date (31 August).
Course requirements
Assumed background
Basic knowledge of Python and SQL
Prerequisites
You'll need to complete the following courses before enrolling in this one:
(CSSE1001 or ENGG1001) and INFS1200
Incompatible
You can't enrol in this course if you've already completed the following:
DATA2001 or DATA7001
Course contact
Course coordinator
Dr Zijian Wang
Course staff
Lecturer
Dr Xin Xia
Professor Shazia Sadiq
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
To develop the students’ knowledge of the data scienceﾠlifecycle and capability to ask the right questions to the data and toﾠselect the appropriate methods of analysis.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Describe the core techniques, tools and processes that are used in data science
LO2.
Design effective data science processes from problem formulation to persuasive storytelling with data
LO3.
Critically apply data science techniques to analyse, model and visualise different types of data
LO4.
Compare and discuss technical and ethical aspects of data science projects, and evaluate and reflect on the results
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code | A1-Hindsight | 20% |
8/09/2025 5:00 pm |
| Computer Code | A2-Insight | 25% |
7/10/2025 5:00 pm |
| Computer Code | A3-Foresight | 25% |
31/10/2025 6:00 pm |
| Examination |
Final Exam
|
30% |
End of Semester Exam Period 8/11/2025 - 22/11/2025 |
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
A1-Hindsight
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code
- Weight
- 20%
- Due date
8/09/2025 5:00 pm
- Learning outcomes
- L01, L02, L03, L04
Task description
This assessment evaluates students' understanding of core data science concepts covered in the course, including data wrangling, exploratory data analysis, and modeling techniques. You will be required to complete a practical assignment involving Python programming and data analysis using real-world datasets. 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. To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Online submission. Students must submit a single Jupyter Notebook (.pynb file).
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.
A2-Insight
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code
- Weight
- 25%
- Due date
7/10/2025 5:00 pm
- Learning outcomes
- L02, L03, L04
Task description
In this assignment, students will gain hands-on experience with unstructured text data. Students will perform data exploration, preprocessing, model training, evaluation, and analysis. The tasks are centered around two machine learning objectives: sentiment analysis and rating prediction.
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. To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Online Submission. Students should only submit the completed Jupyter notebook in .ipynb format, including written answers in markdown and results from executed code cells.
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.
A3-Foresight
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code
- Weight
- 25%
- Due date
31/10/2025 6:00 pm
- Learning outcomes
- L02, L03, L04
Task description
This assignment focuses on time series analysis and forecasting. Students will complete tasks involving data preparation, decomposition, model building and scenario-based forecasting. Your notebook should include code, visualisations, and brief written explanations.
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. To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Online Submission. Students must complete this task in Python using a Jupyter notebook.
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.
Final Exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 30%
- Due date
End of Semester Exam Period
8/11/2025 - 22/11/2025
- Other conditions
- Secure.
- Learning outcomes
- L01, L02, L03, L04
Task description
This assessment evaluates your understanding of key data science concepts taught throughout the course. You will be required to answer all questions within a 90-minute working time, with an additional 10-minute reading time. The exam is closed book, with no calculators or electronic devices permitted.
Hurdle requirements
Students must achieve at least 40% on this final exam in order to pass the course. This exam is a hurdle requirement, meaning: Regardless of your performance on other assessments, failing to meet this threshold will result in a fail grade for the course.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | No calculators permitted |
| Open/closed book | Closed book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
In-Person.
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 Marks | 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
Final marks for the course will be rounded to the nearest whole number before determining the final grade. For example, 84.5 is grade 7 and 84.4 is grade 6.
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
Find the required and recommended resources for this course 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: Fundamentals of Data Science These weekly lectorials combine lecture content with in-class interactive activities in a fitting classroom setting. Students are introduced to core data science topics such as the data science lifecycle, data types, exploratory analysis, and introductory machine learning, and apply these concepts through guided problem-solving, discussions, and short coding tasks. Lectorials help reinforce concepts that support the practicals and assessments. Learning outcomes: L01, L02, L03, L04 |
Multiple weeks From Week 3 To Week 13 |
Practical |
Practicals: Python for Data Science This learning activity consists of a series of weekly practicals designed to build foundational skills in data science through hands-on exercises using Python and Jupyter Notebooks. Students will explore topics such as using Jupyter Notebooks, analyzing structured data with pandas, processing unstructured text data, and working with time series and measurement data. Each practical includes guided coding tasks, data analysis problems, and reflection questions that reinforce key concepts taught in the lectures. Learning outcomes: L02, L03, L04 |
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
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: