DPHU1008 Working with Data
The Complete Study & Assessment Guide · T3 2026DPHU1008 Overview
- Term 3, 2026
- UNSW College delivery
- Level 1 Diploma
- 6 units of credit
- No final exam
Working with Data is a Term 3, 2026 course listed by UNSW Sydney and delivered by UNSW College, where it runs as DPHU1008 and MEDA1308 in the Diploma of Media and Communication. It is a Level 1 (Diploma) course worth 6 units of credit, with no presumed knowledge, and the outline recommends taking Media, Society and Politics first.
- Assessed by Journal, viva, handwritten portfolio, redesign defence
- Every answer needs A concept, a rendering and its social impact
- Hardest step Turning description into situated critique
- How to prepare Collect your own renderings from Week 1
How DPHU1008 is assessed
| Component | Weight | Format |
|---|---|---|
| Assessment 1A: Critical Journal · hurdle | 10% | 500 words (plus or minus 10%) via Turnitin, Tuesday to 3pm Friday of Week 4; AI category 2 on Moodle; completion required |
| Assessment 1B: Viva Voce · hurdle | 20% | 7-minute supervised questioning on your journal in the Week 5 workshop (Moodle: Weeks 5 and 6); no AI; completion required |
| Assessment 2: Portfolio Part One · hurdle | 30% | In-tutorial invigilated handwritten answer, minimum 400 words in two hours, Week 9 workshop; no AI; completion required |
| Assessment 3: Portfolio Part Two, Data Redesign Showcase and Spoken Defence · hurdle | 40% | Original data redesign and 7-minute spoken defence in the Week 12 workshop; no AI; completion required |
| Hurdle: submit all assessments · hurdle | 0% | Failure to complete all assessments as required results in an Unsatisfactory Fail (UF) grade |
Weights follow the Term 3, 2026 course outline. The UNSW Handbook for 2026 groups the critical journal and viva voce as one 30% component and names the 30% portfolio task Portfolio Part One: Draft and Final Submission; the 40% data redesign is the same in both. The outline and the Moodle summary mark every task as a completion requirement, while the Handbook lists no hurdle. To pass you need an overall mark of at least 50%, an attempt at every task and a genuine attempt at each. On attendance, the outline recommends aiming for 100% and requires at least 80% of classes.
What DPHU1008 covers
The course moves from what counts as data and why no data is raw, through Indigenous data sovereignty, situated analysis, classification and affect, to data forms, storytelling, datafication and surveillance. The last three chapters turn those concepts into the critical journal, the viva voce, the handwritten lens evaluation and the data redesign with its spoken defence.
Conceptualising and Contextualising Data
Data as sensory stimuli, the data pyramid, data as partial, data rendering, representation, data and powerThe opening lecture asks what counts as data before any analysis begins. The workshop works with a broad definition, data as sensory stimuli, and shows how noticing, perception and context decide what is recorded.
- Data as sensory stimuli: anything noticed and recorded by someone
- The data pyramid: records, then information, then knowledge
- Partial data: every set is an abstraction with gaps
- Data rendering: any representation or interpretation of data
- Representation: what a rendering emphasises and leaves out
- Data and power: who collects, owns and interprets
Raw Data, Cooked Data and the God Trick
Why raw data is an oxymoron, cooked data, framing, Haraway's God trick and the view from nowhereThe second lecture takes its title from a deliberately awkward phrase: raw data is an oxymoron. Data appear raw, as if simply found, yet by the time anyone uses them they have been selected, categorised, cleaned and stored.
- Raw data as an oxymoron: no data arrive untouched
- Cooked data: collection, categories and cleaning leave fingerprints
- Framing: the same numbers can tell opposite stories
- The God trick: a view from nowhere that hides its position
- Pluralism: several situated views instead of one total view
- Fingerprints left by cleaning, merging and rounding data before publication
Indigenous Data Sovereignty and 5D Data
Snipp's three features of decolonised data, deficit data, Davis on the UN declaration and self-identificationA course learning outcome asks you to situate data in relation to settler colonialism and to engage with First Nations approaches to data and justice. The readings come from the collection Indigenous Data Sovereignty: Toward an Agenda. C.
- Data sovereignty: managing information by a nation's own laws and customs
- Who is counted: communities decide membership
- What is collected: content reflects community priorities
- Who has access: communities control dissemination
- 5D data: difference, disparity, disadvantage, dysfunction, deprivation
- Self-identification and group acceptance in UN practice
Situated Analysis of a Data Rendering
Situated knowledge, standpoint, context rings, a six-move analysis sequence and Assessment 2 Stage 1Lecture 3 moves from what data are to how to read them in context. A situated analysis explains a rendering by the position it was made from: who produced it, for whom, with which data, and within which history.
- Situated knowledge: all knowledge is produced from a position
- Situated data: collectors also work from a time, place and purpose
- Standpoint: whose view is privileged and who is missing
- Context rings: makers, data source, audience, history
- A six-move sequence from description to evaluation
- Stage 1 of Assessment 2 as group situated analysis
Classification Systems, Binaries and Social Constructs
What gets counted counts, Bowker and Star, gender and race binaries, paradox of exposure, Smith on historyLecture 4 looks at data infrastructures: the materials, systems and classifications that hold data together. Its key concepts are classification systems, binaries or binary oppositions, and social constructs.
- What gets counted counts: counting directs policy and resources
- Classification is naturalised until contested or broken
- Binaries are social constructs with histories
- Inadequate categories versus an inadequate system
- Paradox of exposure and privilege hazard
- Smith: order, fragmentation and history as power
Affect, Embodiment and Affective Labour
Affect theory, Tomkins on affect and emotion, the circumplex model, affective renderings, Jones on microworkLecture 5 asks what data do to bodies. The syllabus frames the week around how media influence emotions, bodily responses and behaviour, and how affect shapes political ideologies and social movements.
- Affect: being moved before naming the feeling
- Affect and emotion: bodily response versus scripted name
- Circumplex model: pleasantness and energy
- Affective data renderings designed to move viewers
- Politics of affect: whom a feeling serves
- Affective labour and hidden microwork
Data Forms: Structured and Unstructured Data
Structured versus unstructured data, structuring as work, data form, creative data rendering, data typesLecture 6 surveys the forms data take and the approaches used to render them, from charts and infographics to narratives. The key concepts are the data set, structured and unstructured data, the data form and the creative data rendering.
- Structured data: consistent rows and columns
- Unstructured data: meaning carried in text, image and sound
- Structuring as a set of choices that software can hide
- Data form: the medium as part of the argument
- Creative data rendering: unexpected forms that make scale felt
- Nominal, ordinal and interval data guide design
Data Storytelling: Four Pillars and Feminist Principles
Symbol, colour, caption and editorial; Think, Feel, Do; narrativising data; six feminist visualisation principlesLecture 8 is about representing, rendering and narrativising data.
- Narrativising data: a story with conflict, audience and purpose
- Symbol: resonant and relevant to the audience
- Colour: cultural meanings, contrast and accessibility
- Caption: anchoring one reading of a polysemic image
- Editorial layout: arranging elements for the platform
- Think, Feel, Do as the test of a story
Datafication, Algorithms and Dataveillance
Datafication, big data, what algorithms can do, dataveillance, predictive policing loops and data futuresThe final lectures follow data into society. Lecture 9 covers big data, datafication and data algorithms: how everyday activity becomes recorded traces, how value is extracted from them, and how they shape work and governance.
- Datafication: everyday activity turned into recorded traces
- Big data and the value of prediction across many people
- What algorithms can and cannot know
- Dataveillance: monitoring through data trails
- Feedback loops in predictive policing
- Critical questions for big data claims
The Critical Journal and Viva Voce
Assessment 1A critical journal, 500 words, 10%, and Assessment 1B viva voce, 7 minutes, 20%Assessment 1 has two linked parts. The critical journal, worth 10% and marked out of 20, is one 500-word entry, plus or minus 10%, that identifies, defines and applies a course concept to a data rendering you found yourself.
- Choosing a rendering not used in lectures, workshops or readings
- Question 1: definition with citation, then your own words
- Questions 2a and 2b: describe, then analyse through the concept
- Question 3: impact on society with course content
- Harvard in-text citations and reference list
- Viva voce: seven minutes, one slide, no notes
Lens Evaluation in the Handwritten Portfolio
Assessment 2, Portfolio Part One, 30%: Stages 1 to 3, the assigned lens, notes rules and the four criteriaAssessment 2 is a staged task worth 30%, with only Stage 3 marked. In the Week 6 tutorial your group describes and situates an assigned rendering; in Week 8 it receives a set of analytical lenses and identifies strengths and aspects to critique.
- Stage 1: group description and situated analysis
- Stage 2: group evaluation with analytical lenses
- Stage 3: individual handwritten answer, two hours
- Question 1 and Question 2 requirements
- Allowed notes: key words only, printed readings annotated
- Four marking criteria at 5 marks each
Data Redesign and the Spoken Defence
Assessment 3, Portfolio Part Two, 40%: original redesign, planning sheet, seven-minute defence, feedback criterionAssessment 3 is worth 40% and marked out of 20. You create one original data rendering from your Assessment 2 data set and your Part One answers, show it in the Week 12 workshop and defend it aloud for seven minutes.
- Turning Part One critiques into design moves
- The outline's pillars: data, context, narrative, visuals
- The planning sheet in six steps
- Materials and editorial form
- Pacing a seven-minute defence
- Criteria: narrative, visuals, defence, response to feedback
The course summary describes a dual aim: to produce data objects and to analyse them critically, practising extraction, analysis and visualisation while learning to evaluate data used by media, governments and civil society.
The lectures move from conceptualising data and the claim that raw data is an oxymoron, through situated analysis, data infrastructures and classification, and affect and embodiment, to data forms, storytelling, datafication and surveillance, and data futures.
Readings include D'Ignazio and Klein's Data Feminism, Linda Tuhiwai Smith's Decolonizing Methodologies, chapters on Indigenous data sovereignty, and Feigenbaum and Alamalhodaei's Data Storytelling Workbook. Teaching combines a weekly two-hour lecture with a two-and-a-half-hour workshop.
There is no final exam.
Assessment builds through a written critical journal and a viva voce, a handwritten lens evaluation in the Week 9 workshop, and an original data redesign defended aloud in Week 12, and every task must be completed to pass.
Worked example · free
Analysing a rendering through data as partial
- +1Define the concept in your own words: every data set is an abstraction that records some features of the world and leaves others out.
- +1Describe the rendering: a colour-coded map presenting app users' trip times as the city's commuting experience.
- +1Analyse through the concept: the data come only from people who own smartphones and use the app, so walkers, older residents and people on overnight shifts are under-recorded.
- +1Impact on society: if the map guides transport funding, suburbs whose residents are missing from the data may lose services, deepening an existing inequality.
Key terms
- Data rendering
- A representation or interpretation of data, from a chart to a photograph, advertisement or interview.
- Cooked data
- Data understood as the result of choices about collection, categories and cleaning.
- God trick
- Haraway's name for presenting a view from nowhere while hiding the position it comes from.
- Data sovereignty
- The principle that information is governed by the laws and customs of the nation it concerns.
- Situated analysis
- An explanation of a rendering through its makers, data, audience and history.
- Paradox of exposure
- The double bind in which those who gain most from being counted face the most danger from it.
- Affect
- A bodily response that arrives before it is recognised and named as an emotion.
- Four Pillars
- The workbook's design levers for data stories: symbol, colour, caption and editorial layout.
- Datafication
- The conversion of everyday activity into recorded traces that can be analysed and predicted.
- Dataveillance
- Monitoring people through the trails their data leave rather than by direct observation.
DPHU1008 FAQ
How is Working with Data assessed in Term 3, 2026?
The outline lists four weighted tasks: a 10% critical journal, a 20% viva voce, a 30% handwritten portfolio answer in the Week 9 workshop and a 40% data redesign with a spoken defence in Week 12. A separate 0% hurdle requires every assessment to be completed.
Can I use AI tools in this course?
Only in the critical journal, which the Moodle summary places in Category 2, where AI may assist. The viva, the handwritten portfolio and the redesign defence are Category 1, where AI cannot be used. The outline's own journal page also says AI cannot be used, so confirm with your tutor first.
What makes a strong critical journal entry?
A precise definition of one course concept with an in-text reference, an explanation in your own words, a rendering you found yourself, an analysis that shows new insight from the concept, and a clear link to impacts on society, all referenced in Harvard style.
What notes can I take into the Week 9 portfolio task?
One handwritten page, one side, of key words and phrases, plus printed required readings with your own handwritten annotations. Notes containing full sentences, or photocopied and printed notes, are not permitted in the room.
What happens if I skip one assessment but pass overall?
You would still fail. The pass rules require an overall mark of at least 50%, an attempt at every assessment and a genuine attempt at each; missing any of these leads to an Unsatisfactory Fail grade.
Which readings matter most for the assessments?
Data Feminism's chapter on what gets counted, the Data Storytelling Workbook's visual storytelling section, Smith's Decolonizing Methodologies and the Indigenous data sovereignty chapters supply most of the concepts the rubrics expect you to cite.
Questions students ask in each DPHU1008 chapter
- Why does the course call a photograph or an interview data?
- Because the course defines data broadly as anything noticed and recorded, a photograph or interview records selected information about the world. Treating it as a rendering lets you ask who chose what to record and what effect that choice has on viewers. Chapter 01 · Conceptualising and Contextualising Data
- Why is raw data called an oxymoron?
- Data become usable only after people decide what to record, which categories to use and how to clean the results. The phrase points out that the idea of untouched data contradicts itself, since every data set has already been shaped. Chapter 02 · Raw Data, Cooked Data and the God Trick
- Who wrote 'What does data sovereignty imply' if Moodle lists Megan Davis?
- The chapter itself names C. Matthew Snipp as its author, while Davis wrote a different chapter in the same collection on the UN declaration. Reference each chapter under its own author in your Harvard list. Chapter 03 · Indigenous Data Sovereignty and 5D Data
- What is the difference between describing and situating a rendering?
- Describing states what the rendering is, shows and says. Situating explains where it came from: its makers, purpose, data source, audience and history. The rubric rewards both, but description alone sits in the lower bands. Chapter 04 · Situated Analysis of a Data Rendering
- Does Data Feminism say we should stop classifying people?
- No. The chapter states that working infrastructures need classification. Its critique asks who designed a system, what values it encodes and who bears the costs of its gaps, and it treats counting as useful when consent and safety are protected. Chapter 05 · Classification Systems, Binaries and Social Constructs
- What is the difference between affect and emotion?
- In the reading, an affect is an immediate bodily response, such as blushing, that arrives before you recognise it. An emotion is that response after a learned script has named it, such as shame or pride. Chapter 06 · Affect, Embodiment and Affective Labour
- Why does it matter whether data are structured?
- Structure decides what can be counted and compared. Turning unstructured stories into rows makes patterns visible but strips away tone and context, so a critical analysis reports both what structuring reveals and what it discards. Chapter 07 · Data Forms: Structured and Unstructured Data
- Are the four pillars in the outline the same as the workbook's?
- No. The outline's Assessment 3 brief lists data, context, narrative and visuals and credits Tufte, while the workbook lists symbol, colour, caption and editorial. Use the outline's list to structure the task and the workbook's pillars inside the visuals step, citing each by source. Chapter 08 · Data Storytelling: Four Pillars and Feminist Principles
- Why can a predictive policing model be accurate and unfair at once?
- Its training data record where police patrolled, not where all crime occurred. The model predicts future patrols well while sending officers back to the same neighbourhoods, so its accuracy reflects past policing rather than public safety. Chapter 09 · Datafication, Algorithms and Dataveillance
- Can I use an example from the lectures in my critical journal?
- No. The outline states that you must not use data rendering examples given in the lectures, workshops or readings. Find your own rendering early, ideally one where your chosen concept reveals something a casual viewer would miss. Chapter 10 · The Critical Journal and Viva Voce
- Which lens will I be assigned in Week 9?
- Each tutorial group is assigned one lens from those explored in the Week 8 tutorial. The materials name Feminist Data Principles in a sample but do not publish the full list, so prepare notes on every major course concept. Chapter 11 · Lens Evaluation in the Handwritten Portfolio
- Do I need a new data set for the redesign?
- No. The redesign must be based on your Assessment 2 data set and your Portfolio Part One answers. The aim is to answer the weaknesses you identified there with your own design choices. Chapter 12 · Data Redesign and the Spoken Defence
How to prepare for the assessments
Treat the course as one skill practised four ways: name a concept, apply it to a specific rendering and connect the result to people's lives. From Week 1, keep a file of renderings you find yourself, because the journal must use one that the course has not shown you. Each week, write a definition of the new key concepts in your own words beside a short reading reference, and test it on one rendering.
Before the viva, rehearse your journal aloud. Before Week 9, practise the two-hour handwritten answer twice with a random lens and a single page of key words. For Assessment 3, start your prototype early and bring a specific question to the Week 11 workshop, since acting on feedback is itself assessed.
When a concept will not settle, ask Sia to explain it a different way or to quiz you on a new rendering; it supports your understanding and never replaces your own assessed work.
