Course coordinator
Dr Leah Henrickson
Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Communication & Arts School
You will develop critical understanding and skills related to digital data analytics. You will use point-and-click software and programming languages to collect, analyse, and present data.
In this course, you will learn about the social complexities of quantification in communications scholarship and practice. You will also learn how to collect, process, analyse, and visualise digital data. We will start from the very beginning, assuming you have zero experience with any of this, using basic software and coding in R.
Course requirements
Assumed background
This course is not suitable for first-year students. Upon registration for this course, students are assumed to already have awareness of foundational communications and media studies theories and intermediate critical analysis skills. This course is appropriate for HASS students with no prior training in digital data analysis.
Course contact
Course staff
Lecturer
Dr Leah Henrickson
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Whilst every effort is made to place students in their preferred activity, it is not always possible for a student to be enrolled in their tutorial of choice. If you require assistance, please ensure that you email timetabling.commarts@enquire.uq.edu.au from your UQ student email with:
- Your name
- Your student ID
- The course code
- A list of three tutorial preferences (in order of preference)
- Reason for the change – e.g. timetable clash, elite athlete status, SAP
Teaching staff do not have access to change tutorials or help with timetables; all timetabling changes must be processed through the Timetabling Team.
Aims and outcomes
This course aims to provide students with critical understanding and practical skills related to contemporary digital methods for the collection, processing, analysis, visualisation, and interpretation of various forms of digital data.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Critique emerging approaches to the collection and analysis of digital data.
LO2.
Collect, process, analyse, and visualise digital data.
LO3.
Identify and solve analytical and coding issues self-sufficiently.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Participation/ Student contribution |
Tutorial Participation
|
30% |
Week 1 - Week 11
Participation will be evaluated every tutorial, without exception. Only your best six tutorials will be added together to give you a final mark out of 30. You must participate in your own scheduled tutorial unless otherwise approved by the Course Coordinator. |
| Reflection |
Failing, Flailing, Flourishing
|
25% |
29/04/2026 4:00 pm |
| Computer Code, Portfolio, Project |
Coding and Conceptualising
|
45% |
20/05/2026 4:00 pm |
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
Tutorial Participation
- Identity Verified
- In-person
- Mode
- Activity/ Performance, Oral
- Category
- Participation/ Student contribution
- Weight
- 30%
- Due date
Week 1 - Week 11
Participation will be evaluated every tutorial, without exception. Only your best six tutorials will be added together to give you a final mark out of 30. You must participate in your own scheduled tutorial unless otherwise approved by the Course Coordinator.
- Other conditions
- Secure.
- Learning outcomes
- L01, L02, L03
Task description
At UQ, a tutorial is a small group learning session run by a tutor. Tutorials are designed to help you explore and understand course material presented in lectures, sometimes through the introduction of additional academic information. Throughout the semester, you will be expected to regularly attend - and actively participate in - your scheduled tutorials. Through tutorial participation, you will enhance your understanding of digital analytics, and demonstrate your understanding of course material through ongoing conversations with your peers and the teaching team.
Each tutorial is worth 5 points:
- 2 points for completing the assigned activities
- 3 points for making a meaningful contribution to the tutorial
The activities required for completion each week will be clearly delineated in the weekly tutorial instructions, posted to Learn. Partial marks will not be given (i.e. you either get the points for completion of the activities or you don't; you either get the points for a meaningful contribution or you don't).
A 'meaningful contribution' may include, but is not limited to: asking a thoughtful and relevant question; sharing a thoughtful and relevant answer or comment; providing peer feedback; or helping a peer complete the assigned activities if they are struggling. Through your meaningful contribution, you are valuably contributing to the co-creation of knowledge in the class. The presiding tutor will assess whether or not your contribution is meaningful and worthy of points. If your tutor does not observe your meaningful contribution, it will not be counted.
While you will receive a mark for tutorial participation each week, only your best six tutorials will be added together to give you a final mark out of 30. We encourage you to participate in as many tutorials as you are able.
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.
Submission guidelines
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Please contact the Course Coordinator if you have any questiones.
Failing, Flailing, Flourishing
- Hurdle
- Mode
- Written
- Category
- Reflection
- Weight
- 25%
- Due date
29/04/2026 4:00 pm
- Learning outcomes
- L01, L03
Task description
In this course, you are asked to complete a variety of tasks in the weekly tutorials, and undertake substantial group work. In this assessment, you will identify a single instance when something has not gone right for you in this course. Maybe you got an ‘error’ message. Maybe something was formatted incorrectly. Maybe you just didn’t understand something, or didn't know what to do next. Whatever instance you choose, write a reflection on this experience in 750 words (+/- 10%), excluding references.
Your reflection must include:
- An introduction to what went wrong: Briefly explain the context of the incident.
- A description of the issue you faced: Describe a single instance of what went wrong. If you understand the reason for the issue now, explain why it happened.
- Actions taken to resolve the issue: Describe how you attempted to solve the problem. If you couldn’t resolve it, explain what you did instead and why.
- What you learned: Explain what this experience taught you about digital analytics. How does this experience connect with concepts and content we have covered in the course? This section is weighted the most strongly, so it should be the primary focus of your submission.
- Conclusion: Briefly summarise the main points of your reflection.
You are encouraged to use the first-person ‘I’ pronoun in this assessment.
If images help you more clearly articulate your ideas, you are welcome (but not required) to include them. If images are included, you must explicitly introduce, explain, and respond to them in the body of your text, and they must include figure labels (e.g. 'Figure 1') and explanatory captions (neither included in the word count).
Your submission must reference at least two different academic sources, with at least one of these sources being from the course's required reading list, in order to achieve a passing grade; failure to achieve this hurdle will result in an overridden mark of 49 if your work does not fail naturally. These references must be meaningfully integrated through in-text citations, and must adhere to APA style.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance. 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.
Hurdle requirements
Your submission must reference at least two different academic sources, with at least one of these sources being from the course's required reading list, in order to achieve a passing grade; failure to achieve this hurdle will result in an overridden mark of 49 if your work does not fail naturally. These references must be meaningfully integrated through in-text citations, and must adhere to APA style.Submission guidelines
Submit via Turnitin.
TurnItIn Receipts:
This assignment will be submitted electronically via Blackboard and using TurnItIn. Before submitting any assignments for this course, you must ensure you have completed UQ's compulsory online Academic Integrity Tutorial.
When you successfully submit your assessment to TurnItIn you will see text confirming your submission is complete, before being redirected to your Assignment inbox. On this page you can:
- View the name of the submitted file
- View date and time of the upload
- Resubmit your paper (if necessary)
- Download your submitted paper
- Download digital receipt.
If you cannot see your submission in your Assignment inbox you should regard your submission as unsuccessful. Students are responsible for retaining evidence of submission by the due date for all assessment items, in the required form (e.g. screenshot, email, photo, and an unaltered copy of submitted work).
If the submission was not successful:
- Note the error message (preferably take a screenshot).
- Go to your assignment page and see if it is possible to submit again.
- If you cannot submit again email your course coordinator immediately with the assignment attached.
Please visit this webpage for further advice on how to submit your TurnItIn assignment.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
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.
Coding and Conceptualising
- Identity Verified
- Team or group-based
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Portfolio, Project
- Weight
- 45%
- Due date
20/05/2026 4:00 pm
- Other conditions
- Peer assessment factor, Secure.
- Learning outcomes
- L01, L02, L03
Task description
For this assessment, you’ll work in a group of 3-4 students from your tutorial to create a dataset and analyse it in multiple ways. You will select your own group. Groups will be finalised in Week 6. You’ll have some tutorial time to work with your group, but most work will be completed outside of regularly-scheduled class time.
Your group will present all of this assessment's components in a single portfolio. This assessment demonstrates your understanding of key concepts from the course.
Your portfolio will include six parts:
(1) A research question: Choose a specific topic and write one clear research question that will guide your entire portfolio. Your question should be as specific as possible.
(2) Scraped data: Using the scraping methods covered in class (manual Excel scraping and/or automated scraping in R), create a dataset that can help you answer your research question. For example, if your question relates to increasing property prices in Brisbane, you may wish to scrape data about housing prices on RealEstate.com. As another example, if your question relates to K-pop fan cultures, you may wish to scrape data from Blackpink's YouTube channel. Your dataset must include at least 30 cases and three variables. You must also provide a short explanation (no more than 150 words) of your sampling strategy and why it suits your research question. You must submit your full dataset in a .csv file format. If you used R to scrape your data, you must also submit your full R code, including comments (#) that show your understanding of each coding step.
(3) Descriptive statistics: Choose one variable from your dataset and report its:
- range
- mean
- median
- mode
- standard deviation
You may use Excel or R. Either way, you must specify which functions you used. You must include a short explanation (no more than 150 words) of how these statistics help answer your research question.
(4) Two visualisations made using R: Using R, create two visualisations that help answer your research question. Include the code for each, with comments (#) that show your understanding of each coding step.
- Visualisation 1 must be a bar chart made using the barplot() function. Your bar chart must include: data; a title; axis, variable, and range labels; reasonable scale(s); and a data source.
- Visualisation 2 must be in a form of your choosing. Choose an appropriate type of visualisation for your data and research question. This visualisation should demonstrate R proficiency and good practice for data visualisations as we have reviewed in class. To make this visualisation, you will need to self-direct your learning using some of the many publicly available R resources. You cannot use the barplot() function a second time, but you can make another bar chart using a different function or package if you feel this is appropriate.
Each visualisation must include a short explanation (no more than 150 words) of how it helps answer your research question.
(5) A one-page (A4 size) proposal for a creative data story/presentation. On a single A4 page, describe a creative way to present your data. You must:
- Identify the intended audience of your creative data story/presentation, being as specific as possible.
- Explain how your dataset would be used, and how your story/presentation would help others understand your dataset and research question.
- Show your understanding of course concepts and content through your decisions.
- Include in-text reference to at least one required course reading using APA style.
You may use just text, or mix of text and images. There is no minimum or maximum word count for this part of the assessment, but everything must fit onto one A4 page. Your idea must be feasible, but you do not need to execute it; you can be as aspirational as you wish.
(6) References list: Include at least one required course reading (cited in your creative data story/presentation proposal) and any other sources, unoriginal assets, and/or AI tools you have used.
A template is provided on Learn to help you compile your portfolio for submission. Only one member of your group needs to submit on behalf of the entire group.
Peer Evaluation (Buddycheck): Peer Evaluation is completed for this assessment item using Buddycheck. Buddycheck is a peer evaluation tool, that calculates student contributions to a group project and enables peer feedback. Information about Buddycheck can be accessed here: https://elearning.uq.edu.au/student-guides-ultra/buddycheck-ultra-student .
Using student ratings and feedback, Buddycheck calculates a Peer Assessment Factor (PAF) which is then applied to the group assessment mark. This PAF is a percentage which is multiplied by the group mark to calculate your individual final PAF-adjusted assessment mark. For example, if a group received 25/30 for an assessment and then one member of the group received a 0.9 (90%) for the PAF that student would receive 25 x 0.90 = 22.5 as their final PAF-adjusted assessment mark.
This Peer Evaluation is summative and will be used to calculate students' final marks for this assessment. If you do not complete the Peer Evaluation, your perception of your contribution to the assessment will be absent from the overall calculation of your contribution to the group work.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance. 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.
Submission guidelines
Submit the portfolio via Blackboard. Only one member of the group should submit; Blackboard will automatically connect the submission to all members of the group.
Submit peer feedback via Buddycheck.
Blackboard Assignments
This assignment will be submitted electronically via Blackboard Assignments. Before submitting any assignments for this course, you must ensure you have completed UQ's compulsory online Academic Integrity Tutorial.
Important submission information
- You must click on the Submit button to submit your work for assessment. Academic staff will not see the files if you have merely saved them as a submission in progress.
- Files that are submitted CANNOT be retrieved for editing and re-submission. Once submitted, they are delivered to the Course Coordinator.
- It is your responsibility to check the Submission History and confirm your assignment was successfully submitted.
- To allow for the many possible technical problems with computers and the internet, students are advised to commence assignment uploads at least 3 hours before they are due.
- If you don’t receive a submission ID, you should regard your submission as unsuccessful.
- It is your responsibility to check the assignment preview and confirm that the assignment has been successfully submitted.
- Students should take a screenshot showing the successful submission to confirm that they have followed the correct process.
Students are responsible for retaining evidence of submission by the due date for all assessment items, in the required form (e.g. screenshot, email, photo, and an unaltered copy of submitted work).
If the submission was not successful:
- Note the error message (preferably take a screenshot).
- Go to your assignment page and see if it is possible to submit again.
- If you cannot submit again email your course coordinator immediately with the assignment attached.
Please visit this webpage for further advice on how to submit your Blackboard assignment.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
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.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 24 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 25 - 44 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 45 - 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
- Marking rubrics for each assessment are provided on the course's Blackboard site.
- Where fractional marks occur in the calculation of the final grade, a mark of x.5% or greater will be rounded up to (x+1)%. A percentage mark of less than x.5% will be rounded down to x%.
- Where no assessable work is received, a Grade of X will apply.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
- Further information regarding the assessment, including marking criteria and/or marking rubrics are available in the ‘Assessment’ folder in Blackboard for this course.
- Marks Cannot Be Changed After Being Released: Marks are not open to negotiation with course staff. If you wish to discuss the feedback you have received, you should make an appointment to speak with the Course Coordinator.
- Assessment Re-mark: If you are considering an Assessment Re-mark, please follow the link to important information you should consider before submitting a request.
- Integrity Pledge: Assignments for this course will be submitted electronically via Blackboard and using Turnitin. Before submitting any assignments for this course, you must ensure you have completed UQ's compulsory online Academic Integrity Modules.ﾠIn uploading an assignment via Turnitin you are certifying that it is your original work, that it has not been copied in whole or part from another person or source except where this is properly acknowledged, and that it has not in whole or part been previously submitted for assessment in any other course at this or any other university.
- Withholding marks prior to finalisation of grades: Per UQ Assessment Procedures – Release of Assessment Item Marks and Grades: The final assessment item and the marks for the assessment item are to be released only after the final grade for the course has been released.
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.
Additional learning resources information
Additional learning resources and information may be shared on the course's Blackboard site.
For tutorials, students will need to bring their own computers (not tablets) with Internet, Excel, and RStudio access for full participation. Information about Library laptop rentals will be provided in class and on the course's Blackboard site.
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 |
|---|---|---|
Week 1 (23 Feb - 01 Mar) |
Lecture |
Week 1 Lecture: What Do We Mean By 'Digital Analytics'? Learning outcomes: L01 |
Tutorial |
Week 1 Tutorial: Identifying Digital/Online Data Learning outcomes: L01, L02, L03 |
|
Week 2 (02 Mar - 08 Mar) |
Lecture |
Week 2 Lecture: The Building Blocks Learning outcomes: L01 |
Tutorial |
Week 2 Tutorial: Computational Thinking Learning outcomes: L01, L02, L03 |
|
Week 3 (09 Mar - 15 Mar) |
Lecture |
Week 3 Lecture: Consciously Collecting Data Learning outcomes: L01, L02 |
Tutorial |
Week 3 Tutorial: Manual Web Scraping Learning outcomes: L01, L02, L03 |
|
Week 4 (16 Mar - 22 Mar) |
Lecture |
Week 4 Lecture: Analysing Data (Part 1) Learning outcomes: L01, L02 |
Tutorial |
Week 4 Tutorial: Analysing Data in Excel Learning outcomes: L01, L02, L03 |
|
Week 5 (23 Mar - 29 Mar) |
Lecture |
Week 5 Lecture: Presenting Data (Part 1) Learning outcomes: L01, L02 |
Tutorial |
Week 5 Tutorial: Visualising Data in Excel Learning outcomes: L01, L02, L03 |
|
Week 6 (30 Mar - 05 Apr) |
Lecture |
Week 6 Lecture: Hello, World! Learning outcomes: L01, L02 |
Tutorial |
Week 6 Tutorial: Introduction to R Learning outcomes: L01, L02, L03 |
|
Mid-sem break (06 Apr - 12 Apr) |
No student involvement (Breaks, information) |
Mid-Semester Break |
Week 7 (13 Apr - 19 Apr) |
Lecture |
Week 7 Lecture: Automated and Unconscious Data Collection Learning outcomes: L01, L02 |
Tutorial |
Week 7 Tutorial: Automated Web Scraping Learning outcomes: L01, L02, L03 |
|
Week 8 (20 Apr - 26 Apr) |
Lecture |
Week 8 Lecture: Analysing Data (Part 2) Learning outcomes: L01, L02 |
Tutorial |
Week 8 Tutorial: Analysing Data in R Learning outcomes: L01, L02, L03 |
|
Week 9 (27 Apr - 03 May) |
Lecture |
Week 9 Lecture: Presenting Data (Part 2) Learning outcomes: L01, L02 |
Tutorial |
Week 9 Tutorial: Visualising Data in R Learning outcomes: L01, L02, L03 |
|
Week 10 (04 May - 10 May) |
Lecture |
Week 10 Lecture: Guest Lecture Learning outcomes: L01 |
Tutorial |
Week 10 Tutorial: Data Storytelling Learning outcomes: L01, L02, L03 |
|
Week 11 (11 May - 17 May) |
No student involvement (Breaks, information) |
Week 11: NO LECTURE THIS WEEK No lectures this week |
Tutorial |
Week 11 Tutorial: Assessment Clinic Learning outcomes: L01, L02, L03 |
|
Week 12 (18 May - 24 May) |
No student involvement (Breaks, information) |
Week 12: NO LECTURE OR TUTORIALS |
Week 13 (25 May - 31 May) |
No student involvement (Breaks, information) |
Week 13: NO LECTURE OR TUTORIALS |
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.
Course guidelines
Assessment Questions
The teaching staff in this course do not respond to questions about assessments over email. To have questions about assessments addressed:
- Speak to the Course Coordinator or tutors before or after the in-person lecture.
- Speak to your tutor during the regularly-scheduled tutorials.
- Attend the Course Coordinator's weekly drop-in hours (information provided in the 'Course Staff' section on Learn).
- Post to the course's Discussion Board on Learn.
Communication Expectations
While you are a student at UQ, all communication must be conducted according to the UQ Student Code of Conduct. The UQ Library has a helpful Communicate and collaborate online module.
- Email is the primary way for you to send messages to, and receive information from, the School, and our staff. In this course, however, emails about assessments will not be accepted; please see above.
- If emailing, you must use your UQ email address (not a private address) to communicate with staff.
- You should add a clear subject line, including course code, and a 2-3 word statement.
- You can send email at any time, however please do not expect responses outside normal working hours (Monday to Friday from ~9am to ~5pm).
- Emails that constitute bullying, harassment, or discrimination against staff contravene the Student Code of Conduct. Emails like this will be reported to the University, and the matter will be pursued as misconduct.