DATA1002 Informatics: Data and Computation
The Complete Exam Bible · S2 2026DATA1002 Overview
- The University of Sydney School of Computer Science
- Semester 2, 2026
- an undergraduate introductory data and computation unit
- 6 credit points
- a foundation in Python, data handling, visualisation and predictive modelling
- 50% written exam, 10% weekly coding, 2% early-feedback MCQ, 20% project report, 10% closed-book Python coding test and 8% project poster; a practice coding test carries 0%
DATA1002 Informatics: Data and Computation develops Python programming, data ingestion and cleaning, aggregation, visualisation, machine learning, communication, ethics and human-AI judgement. It is taught within The University of Sydney School of Computer Science. It is an undergraduate introductory data and computation unit.
- Two pass controls The exam requires 40% plus 50 overall; separately, attendance for at least 6 weeks across Week 4 to Week 13 is required unless Special Consideration applies, or an AF is automatic.
- Project arc Stage 1 documents data, cleaning, summaries and charts; Stage 2 presents a predictive model and evaluates its success.
- Two closed-book checks The 60-minute Python coding test and two-hour written exam verify different kinds of individual understanding.
- DATA1002 boundary Do not import DATA1902 weights: the shared teaching site contains two distinct assessment architectures.
How DATA1002 is assessed
| Component | Weight | Format |
|---|---|---|
| Written Examination · hurdle | 50% | Two-hour closed-book MCQ and short-essay exam; 40% minimum |
| Weekly Coding Tasks | 10% | Python tasks graded through automated output checks |
| Early-feedback MCQ | 2% | Week 3 check on Weeks 1-3 |
| Practice Coding Test | 0% | Unweighted preparation for the in-class coding test |
| Project Stage 1 Report | 20% | Group report on data, cleaning, summaries and charts |
| Python Coding Test | 10% | 60-minute closed-book in-class automated test |
| Project Stage 2 Poster | 8% | In-class group presentation on predictive modelling |
DATA1002 totals 100% across a 50% written exam, 10% weekly coding, 2% early MCQ, 20% project report, 10% coding test and 8% poster; the practice coding test is 0%. The written exam is the only hurdle row in the assessment table, with at least 40% on the exam and 50 overall required. Separately, attendance is required for at least 6 weeks from Week 4 to Week 13 inclusive; without approved Special Consideration, attending fewer than 6 weeks automatically results in an AF grade. DATA1902 weights are excluded.
Current DATA1002 dates
| Date | Item | Control |
|---|---|---|
| 23 August 2026 at 5:00 pm | Early-feedback MCQ | Covers Weeks 1-3 and contributes 2%. |
| 11 October 2026 at 5:00 pm | Project Stage 1 report | Group data report worth 20%. |
| Formal examination period | Written examination | No exact date is published in the outline. |
Current-offering dates captured in the course materials. Confirm changes and exact submission settings in the live LMS.
What DATA1002 covers
Read Data Questions, Lifecycle and Project Failure as the foundation, Data Visualisation and Communication as the main change in method, and Project Report, Poster and Secure Assessment as the final application of the course.
Data Questions, Lifecycle and Project Failure
data science lifecycle · decision question · project failure mode · turn a broad request into a decision question and map the data work needed to answer it02Python Values, Types and Testable Computation
Python value · data type · automated test · write a small calculation whose types, transformations and expected output can be checked independently03Conditionals, Loops and Text Processing
conditional branch · iteration · text parsing · convert a text-processing requirement into branches and loops with boundary tests04Lists, Dictionaries and Aggregation Patterns
list · dictionary · aggregation · choose data structures and aggregate records without losing the grouping key or denominator05Data Cleaning, Provenance and Quality
data provenance · missing-value convention · data-quality rule · build a cleaning pipeline whose assumptions and row-level effects can be audited06Functions, Modules, CSV and Pandas
Python function · module · DataFrame · organise ingestion and transformation into reusable functions with explicit inputs and outputs07Data Visualisation and Communication
visual encoding · comparison baseline · chart integrity · select and design a chart that supports the intended comparison and communicates its evidence boundary08Clustering, Similarity and Recommendation
clustering · distance metric · recommendation · explain how representation and distance produce clusters or recommendations and evaluate whether the grouping is useful09Predictive Models, Evaluation and Explainability
predictive model · evaluation metric · generalisation · fit and evaluate a predictive model using held-out evidence and a metric aligned to the decision10Natural Language Processing and Annotation
natural language processing · data annotation · inter-annotator agreement · design a language-labelling task and distinguish model error from an unclear category scheme11GenAI Reliability, Ethics and Fairness
generative model · algorithmic fairness · human oversight · audit an AI-assisted data decision for provenance, uncertainty, subgroup impact and accountable review12Project Report, Poster and Secure Assessment
reproducible analysis · poster narrative · exam hurdle · integrate coding, project evidence and exam reasoning while keeping DATA1002 facts separate from DATA1902It carries 6 credit points. It is positioned as a foundation in Python, data handling, visualisation and predictive modelling.
The current sequence joins computational fundamentals to a staged group data project.
It explicitly separates DATA1002 from advanced DATA1902 and uses a closed-book Python test plus a closed-book final examination to verify individual capability.
Assessment in DATA1002 is distributed as follows: 50% written exam, 10% weekly coding, 2% early-feedback MCQ, 20% project report, 10% closed-book Python coding test and 8% project poster; a practice coding test carries 0%
The operational assessment conditions matter here.
The final is a two-hour closed-book examination with multiple-choice and short-essay questions during the formal examination period.
The Python coding test is a separate 60-minute closed-book in-class task with no cheat sheet.
What makes DATA1002 demanding is concrete: Turning messy data into a defensible decision: students must write correct Python, document transformations, choose meaningful summaries and charts, evaluate predictive models, and distinguish evidence from confident-looking AI or algorithmic output.
The written examination is the only hurdle row in the assessment table: students need at least 40% in that examination and at least 50 overall, with failure potentially capping the final mark at 45. A separate attendance pass rule requires at least 6 weeks from Week 4 to Week 13 inclusive;
without approved Special Consideration, fewer than 6 weeks automatically results in an AF grade.
For enrolment planning, The current outline lists no prerequisites or assumed knowledge and prohibits INFO1903 and DATA1902.
Read Data Questions, Lifecycle and Project Failure as the foundation, Data Visualisation and Communication as the main change in method, and Project Report, Poster and Secure Assessment as the final application of the course.
Coverage note: DATA1902 shares teaching pages but has a different assessment split;
this build uses only the DATA1002 rows.
Diagnose a data-cleaning bug before fitting a model
- 1Inspect types, ranges and missing-value conventions before changing the data.
- 1Replace the documented age sentinel -1 with a missing value rather than a real age.
- 1Remove currency symbols and separators, then convert price to a numeric type with invalid conversions exposed.
- 1Record row counts and summary statistics before and after each transformation.
- 1Write automated checks that ages are plausible and nonmissing prices are numeric.
- 1Fit only after deciding how missing ages will be excluded, imputed or represented and documenting that decision.
Key terms
- Data cleaning
- The documented process of detecting and correcting invalid, inconsistent, missing or unsuitable data values.
- Predictive model
- A fitted rule that uses input features to estimate an unknown outcome for new observations.
- Algorithmic fairness
- The evaluation of how a computational system distributes errors, benefits and burdens across relevant groups.
- Data science lifecycle
- An iterative sequence for framing a question, obtaining data, cleaning, analysing, modelling, communicating and reviewing.
- Python function
- A named reusable block of code that accepts inputs, performs operations and may return a result.
- Aggregation
- The combination of multiple observations into grouped summaries such as counts, totals, means or rates.
- Pandas DataFrame
- A labelled two-dimensional tabular data structure whose columns can hold different data types.
- Data visualisation
- A graphical encoding of data designed to reveal structure and support accurate comparison or communication.
- Clustering
- An unsupervised method that groups observations according to a defined measure of similarity or distance.
- Natural language processing
- Computational methods for representing, analysing or generating human language data.
- Model evaluation
- The structured assessment of predictive performance using suitable data, metrics and comparison baselines.
- Data provenance
- Recorded information about where data came from and how they were collected, transformed and used.
- Exception handling
- Program logic that anticipates and responds to runtime errors without silently corrupting the result.
DATA1002 FAQ
Which current DATA1002 dates are captured?
Early-feedback MCQ: 23 August 2026 at 5:00 pm; Project Stage 1 report: 11 October 2026 at 5:00 pm; Written examination: Formal examination period. Confirm any change and the exact submission setting in the live LMS.
How is DATA1002 assessed?
50% written exam, 10% weekly coding, 2% early-feedback MCQ, 20% project report, 10% closed-book Python coding test and 8% project poster; a practice coding test carries 0%
What is the DATA1002 exam or final-task format?
The final is a two-hour closed-book examination with multiple-choice and short-essay questions during the formal examination period. The Python coding test is a separate 60-minute closed-book in-class task with no cheat sheet.
Does DATA1002 have a hurdle or component-level pass rule?
The written examination is the only hurdle row in the assessment table. School policy requires at least 40% in that examination and an overall final mark of at least 50; failure may cap the final mark at 45. A separate attendance pass rule requires at least 6 weeks from Week 4 to Week 13 inclusive; without approved Special Consideration, fewer than 6 weeks automatically results in an AF grade.
What prerequisites or restrictions apply to DATA1002?
The current outline lists no prerequisites or assumed knowledge and prohibits INFO1903 and DATA1902.
Where do students usually lose marks in DATA1002?
Turning messy data into a defensible decision: students must write correct Python, document transformations, choose meaningful summaries and charts, evaluate predictive models, and distinguish evidence from confident-looking AI or algorithmic output.
Which offering does this DATA1002 guide cover?
It is aligned to Semester 2, 2026; confirm your enrolled class and timetable in the current institutional system.
Is this DATA1002 resource an official university guide?
No. It is an independent DATA1002 study resource; current institutional instructions remain authoritative for assessment operation.
How to study for the exam
Retrieve the course map, practise the recurring method—frame the data question, inspect provenance and structure, write testable Python transformations, choose a visual or model suited to the decision, and evaluate the result for error, fairness and transfer—on changed scenarios, and verify every operational assessment detail in the live institutional system.
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