DATA2002 Data Analytics: Learning from Data
The Complete Exam Bible · S2 2026DATA2002 Overview
- The University of Sydney
- Semester 2, 2026
- Level 2 undergraduate unit
- Mathematics and Statistics
- Six credit points
DATA2002 is the University of Sydney's intermediate unit in statistics and data science, taught by Mathematics and Statistics and worth six credit points.
- Assessed by A 60 per cent written final exam of two hours, a three part group project worth 20 per cent, an individual project report worth 10 per cent, and weekly in class feedback exercises worth 10 per cent between them.
- The organising idea Almost every mark is paid for choosing the procedure the data design permits and for writing a conclusion a reader could act on. The arithmetic in between is done by software.
- Where the marks hide The weekly feedback exercises are sat in the workshop and are worth as much as the individual project report, so workshop attendance is worth ten per cent by another name.
- Hardest habit Reporting a result in five lines every time: hypotheses, assumptions, statistic with its distribution, p-value, and a conclusion that names the variables and the population.
- What the exam rewards Fast recognition of which test a scenario allows, and reading a printed result rather than producing one. The paper is sat without a calculator, so questions ask for the expression more often than the number.
How DATA2002 is assessed
| Component | Weight | Format |
|---|---|---|
| Final Exam | 60% | Written exam in the formal examination period, two hours, generative AI prohibited. The released sample paper for this offering is closed book with no calculator, thirty equally weighted multiple choice questions worth three quarters of the paper and one extended answer question worth the remaining quarter, with an Exam Guide of key procedures printed after the last question. |
| Group Project Report (Project 1) | 10% | Group written work, due in Week 6, marked against a rubric published on the unit site. Not eligible for a simple extension because it is group work. |
| Individual Project Report (Project 2) | 10% | Individual written work, due in Week 12, drawing on the whole unit. Eligible for special consideration and for an extension of up to ten days. |
| Individual Feedback Exercise | 9% | An in class exercise sat in the weekly two hour workshop, assessing participation and understanding. Generative AI allowed. |
| Group Project Presentation (Project 1) | 5% | Group presentation, due in Week 6, marked against a rubric. Not eligible for a simple extension because it is group work. |
| Individual GitHub Contribution (Project 1) | 5% | A portfolio assessment of your own visible contribution to the group repository. Not eligible for a simple extension because it assesses collaboration inside the group work. |
| Individual Early Feedback Task | 1% | The Week 3 feedback exercise, designated the early feedback task and sat in the two hour workshop. Generative AI allowed. |
These seven components and their weights are what the University publishes on the unit of study page for the single 2026 offering, and they add to exactly 100 per cent. No component is a hurdle: the unit page states plainly that none of the tasks are hurdle tasks, so there is no double pass requirement and an overall mark of 50 is enough to pass. That is separate from a rule which is easy to confuse with a hurdle: the final exam is compulsory and must be attempted, and not attempting it results in an AF grade for the unit.
The two feedback exercise rows describe one stream. The unit runs eleven exercises across Workshops 1 to 5 and 7 to 12, each worth 1.25 per cent, and counts the best eight, capping the stream at 10 per cent, which is why the published table splits it into a 1 per cent early feedback task and a 9 per cent weekly row. Missing up to three is already priced into that rule and is not eligible for special consideration; missing more than three is what the reweighting provision exists for.
One rule links the two projects: if your Group Project Report mark is lower than your Individual Project Report mark, the individual mark replaces it, but that replacement does not apply when the Group Project Report mark is below 50 per cent. Late written work loses 5 per cent of the maximum mark for each calendar day and scores zero after ten calendar days. Weights and rules come from the unit of study page; confirm the exact submission settings on your own unit site.
Current DATA2002 dates
| Date | Item | Control |
|---|---|---|
| 6 September 2026 | Group Project Presentation due | Week 6, at 23:59; closing date the same day |
| 13 September 2026 | Group Project Report due | Week 6, at 23:59; closing date 23 September 2026 |
| 13 September 2026 | Individual GitHub Contribution due | Week 6, at 23:59; closing date the same day |
| 1 November 2026 | Individual Project Report due | Week 12, at 23:59; closing date 11 November 2026 |
Dates are as published in the unit of study page for the 2026 Semester 2 offering. Confirm exact deadlines and submission settings in the live LMS.
What DATA2002 covers
DATA2002 runs over thirteen weeks in four official modules. Weeks 1 to 3 work with categorical data, where a test statistic can be built from first principles and compared with a distribution you can name. Weeks 4 to 6 move to measurements and the normality question, answering it three ways: assume it and use a t test, avoid it with ranks, or dispense with it by resampling.
Weeks 7 to 9 generalise from two groups to many, which creates two new problems at once, the error rate across many comparisons and how to separate one factor from another. Weeks 10 to 12 change the goal from explaining the sample to predicting a new observation, which is why cross-validation exists. Week 13 is revision.
A weekly two hour workshop runs underneath the whole unit and is where the assessed feedback exercises are sat.
Working with data in R and reproducible reports
The analysis loop from import to plot, the four variable types that route every later method, cleaning decisions that must be recorded, and the version control the unit assesses in its own right. (Week 1)02Study design: surveys, experiments and observation
Sampling and the three named bias families, the randomised controlled double blind design and what randomisation buys, confounding and its two conditions, and the reversal of an association when a third variable is conditioned on. (Week 1)03Hypothesis testing and chi-squared goodness of fit
The six reporting slots the unit marks against, where the chi-squared statistic comes from, degrees of freedom and the price of estimating a parameter, the expected count condition and pooling, and goodness of fit to a named count distribution. (Weeks 1 to 2)04Diagnostic accuracy and measures of risk
Nine measures from one two by two table and the margin each conditions on, the base rate effect on predictive value, relative risk and the odds ratio, and the study design that decides which of the two you may compute. (Week 2)05Contingency tables: homogeneity, independence, exact tests
Two tests that share every line of arithmetic and differ in what they may conclude, where the row times column rule comes from, and the exact and simulated methods for tables too small for the approximation. (Week 3)06Testing means with the t-test family
Where the t distribution comes from, the general estimator over standard error shape, one sample, two independent samples pooled and unpooled, and the paired design that changes the answer on identical numbers. (Week 4)07Confidence intervals, power and sample size
Critical values under the unit's lower tail notation, the interval as the set of hypotheses a test would not reject, the two error rates, power as a curve rather than a number, and sizing a study with the round up rule. (Week 4)08Rank-based tests when normality fails
Checking normality honestly, the sign test as a binomial test on directions, the signed-rank test and the information it recovers, the rank-sum test for independent samples, and the statistic software prints instead of the one you computed. (Week 5)09Permutation tests and the bootstrap
Building a null distribution by rearranging labels, exchangeability as the single assumption, resampling with replacement for an interval, the percentile method, and the two readings of a confidence interval the unit rules out. (Week 6)10Multiple testing and error rate control
The outcome table for many hypotheses, three error rates side by side, how fast the family wise rate grows, the Bonferroni threshold and what conservative means, and the step up procedure that controls the false discovery rate instead. (Week 7)11One-way ANOVA and post hoc comparisons
The sums of squares decomposition, the F ratio and its two degrees of freedom, contrasts and the union bound behind the Bonferroni multiplier, three multiplier methods and what each permits, and what to do when the assumptions fail. (Weeks 7 to 8)12Two-way and two-factor analysis of variance
Blocking as the removal of a nuisance source and what it buys, the two way table, replication and the fourth row it creates, interaction and how to read an interaction plot, and the rank based test that ranks within blocks. (Weeks 8 to 9)13Linear regression and model interpretation
Least squares, the four assumptions and the plot that checks each, inference on a coefficient, r-squared and why it cannot select a model, confidence against prediction intervals, and the conditional reading of a multiple regression coefficient. (Week 10)14Model selection and assessing performance
Why in-sample fit always improves, information criteria and the direction of the comparison, stepwise and exhaustive search and why the two need not agree, and k-fold cross-validation with the whole procedure inside the loop. (Week 10)15Classification: logistic regression, trees, neighbours
Log-odds, odds and probability and where the neutral value sits on each, the confusion matrix and the accuracy figure that flatters a useless model, the complexity constant that runs backwards, and the number of neighbours that cannot be chosen in sample. (Weeks 11 to 12)16Dimension reduction and clustering
Supervised against unsupervised, components as directions of maximal variance, loadings against scores, how many components to keep when two rules disagree, reading a dendrogram by height, and what an unsupervised result can claim. (Week 12)It takes the hypothesis test you met in first year and turns it into a decision procedure: given data that arrived in a particular way, which analysis is defensible, what does it assume, and what may the conclusion say? The unit is organised into four official modules that widen the question in stages.
Module 1 works with categorical data, where a statistic can be built from first principles and compared with a distribution you can name, and it covers goodness of fit, diagnostic accuracy, measures of risk, and tests of homogeneity and independence with their exact small sample alternatives.
Module 2 changes the response from a count to a measurement, which forces the normality question, and answers it three ways: assume it and use the t test family, avoid it by working with ranks, or dispense with it by permuting and resampling.
Module 3 goes from two groups to many, which immediately creates two new problems, the error rate across many comparisons and how to separate the effect of one factor from another, and it answers them with multiple testing corrections and the analysis of variance.
Module 4 changes the goal altogether: instead of explaining the sample you predict a new observation, which is why in sample measures stop being sufficient and cross-validation appears, and it closes on classification and on methods that have no response variable at all.
Running underneath the whole unit is R, assessed directly through a reproducible report and a version controlled repository, and a weekly two hour workshop where the assessed feedback exercises are sat.
The final examination is worth 60 per cent, runs for two hours in the formal examination period, and the released sample paper for this offering describes it as closed book, sat without a calculator, with thirty multiple choice questions carrying three quarters of the paper and a single extended answer question carrying the rest.
Route a scenario to a test before computing anything
- +2Read the sampling sentence in each study before anything else. In the first, one group of 240 patients was collected and then classified two ways, so only the grand total was fixed. In the second, three sample sizes of 80 were chosen in advance, so the row totals are design constants.
- +1First study: a test of independence. The null hypothesis is that discharge within four hours and triage category are unrelated in this population, so the joint probabilities factor into their marginals.
- +1Second study: a test of homogeneity. The null hypothesis is that the proportion discharged within four hours is the same in all three triage populations.
- +1Degrees of freedom are the same for both, because the tables have the same shape: three rows and two columns give (3 minus 1) times (2 minus 1), which is 2. Only the wording of the hypothesis and the conclusion differ.
- +1An expected count of 4.1 is below five, so the chi-squared approximation is not reliable and the ordinary test should not be reported. With a three by two table, either pool two triage categories if that still answers the question, or compute a simulated p-value with the margins held fixed.
Key terms
- Test statistic
- A quantity computed from the sample whose distribution is known when the null hypothesis is true. Comparing the observed value with that distribution is what produces a p-value, so a test statistic without a stated null distribution is not yet a test.
- P-value
- The probability, computed assuming the null hypothesis is true, of a test statistic at least as extreme as the one observed. It is not the probability that the null hypothesis is true, and a large one is a failure to find evidence rather than evidence of no effect.
- Expected count
- The count a cell of a table would contain on average if the null model held, computed as the row total times the column total divided by the grand total. The chi-squared approximation requires every expected count to be at least five, and the condition is checked on the expected counts rather than the observed ones.
- Homogeneity
- The hypothesis that several populations, each sampled separately with a size the investigator chose, share the same distribution of an outcome. Its arithmetic is identical to a test of independence and its conclusion is about groups rather than about two variables.
- Odds ratio
- The odds of an outcome under exposure divided by the odds without it, equal to the cross product of a two by two table. It is the same number whichever margin the table is conditioned on, which is why it can be estimated from a retrospective study when relative risk cannot.
- Power
- The probability of rejecting the null hypothesis when a specified alternative is true. It is a function of the true effect rather than a single number, and it rises with sample size and effect size and falls when the significance level is made smaller.
- Exchangeability
- The assumption that permuting the observations leaves their joint distribution unchanged. It is the single condition a permutation test needs, and it is weaker than the normality assumptions it replaces, though data with a time trend or a clustered structure do not satisfy it.
- Family wise error rate
- The probability of making at least one false positive across a family of tests. It grows quickly with the number of tests and is the quantity the Bonferroni correction bounds.
- False discovery rate
- The expected proportion of the results declared significant that are actually false. Controlling it is more permissive than controlling the family wise rate and is the usual choice when a screen produces a shortlist for follow up.
- Interaction
- The part of a cell mean in a two factor design that the two margins do not predict. When it is present the effect of one factor depends on the level of the other, so each main effect becomes an average over conditions in which the factor behaves differently.
- Cross-validation
- Splitting the observations into folds, holding each out in turn, fitting on the rest and predicting the held out fold. It estimates how a procedure will perform on data it has not seen, which no measure computed on the fitting data can do.
- Loading
- The weight a variable receives in a principal component, as distinct from a score, which is the value of that component for one observation. Loadings say what a component means; scores say where an observation sits along it.
DATA2002 FAQ
What does this unit actually cover, week by week?
Four modules over twelve teaching weeks, with a revision week at the end. Weeks 1 to 3 are categorical data: the hypothesis testing framework, chi-squared goodness of fit, measures of diagnostic accuracy and risk, and tests of homogeneity and independence with exact alternatives for small tables.
Weeks 4 to 6 move to measurements: the t test family, confidence intervals, power and sample size, rank based tests for when normality fails, and permutation and bootstrap methods. Weeks 7 to 9 handle several groups and several factors: multiple testing corrections, one way analysis of variance with post hoc comparisons, and two way and two factor designs including interaction.
Weeks 10 to 12 turn to prediction: linear regression, model selection and cross-validation, classification with logistic regression, trees and nearest neighbours, and finally dimension reduction and clustering. R runs underneath all of it and is assessed in its own right.
How is the mark made up, and is anything a hurdle?
The final exam is 60 per cent. The rest is a three part group project worth 20 per cent between a presentation, a report and an individual assessment of your contribution to the shared repository, an individual project report worth 10 per cent, and weekly feedback exercises worth 10 per cent between the Week 3 early feedback task and the remaining weeks.
No task is a hurdle: the unit page states that none of the tasks are hurdle tasks, so an overall mark of 50 passes and there is no separate exam threshold. One rule is easy to mistake for a hurdle and is not one, which is that the final exam is compulsory and must be attempted, and not attempting it results in an AF grade.
What is the exam like, and what can I take in?
The unit page gives the weight and the conditions: 60 per cent, two hours, formal examination period, generative AI prohibited. The sample paper released for this offering describes the format, and it is worth planning around: ten minutes of reading time before two hours of writing, closed book with no material permitted, no calculators, a twenty eight page booklet, and two sections.
Thirty equally weighted multiple choice questions carry seventy five per cent of the paper and one extended answer question with several parts carries the rest, with working required. An Exam Guide summarising key procedures with their contexts and assumptions is printed after the last question. Read your own cover sheet on the day, because a sample is strong evidence rather than a promise.
How much R do I need to memorise?
Very little, and the unit says so directly: you are not expected to memorise the code, you are expected to understand what a piece of code is doing and how to interpret its result in context. In a closed book paper sat without a calculator, that translates into one specific skill, which is reading a printed result and saying which number answers the question.
The projects are the opposite: there you write and run the code, and the reproducibility of the report and your contribution to the shared repository are both assessed.
How do the weekly feedback exercises actually count?
There are eleven of them, run in Workshops 1 to 5 and 7 to 12. Each is worth 1.25 per cent and the best eight are counted, which caps the stream at 10 per cent and is why the published table shows it as a 1 per cent row for the Week 3 early feedback task and a 9 per cent row for the rest. Missing up to three is already priced into the best eight rule and is not eligible for special consideration.
Missing more than three, for example through prolonged illness, is what the mark adjustment provision is for.
How do the two projects fit together?
Project 1 has three assessed components in Week 6: a group presentation, a group report and an individual assessment of your own contribution to the group's repository.
Project 2 is an individual report in Week 12. A progress mark adjustment runs between them: if your Group Project Report mark is lower than your Individual Project Report mark, the individual mark replaces it, but not when the Group Project Report mark is below 50 per cent.
So a weak group report is worth rescuing to the pass line first and only then worth improving through Project 2. The group components and the repository assessment are not eligible for simple extensions, because they are group work.
A question describes a study without naming a test. How do I choose?
Answer four questions in order. What is the response: a count, a measurement or a label? How many groups are being compared: one, two, or more than two? Are the observations paired, meaning each one is matched to exactly one in the other group? And what is the goal: to test a hypothesis, to estimate something with uncertainty, or to predict a new case?
Those four answers narrow the field to one or two candidates before you write anything. One extra sentence matters for tables of counts: if the sizes of the groups were chosen in advance the test is one of homogeneity, and if a single sample was classified two ways it is one of independence.
Is this a heavily mathematical unit?
It is quantitative rather than proof heavy. Formulas are derived rather than dropped in, so you see where the chi-squared statistic comes from and why the analysis of variance ratio is a ratio, but the assessment rewards choosing and interpreting procedures rather than reproducing derivations.
The assumed knowledge is a first or second year unit in statistics or data science with a substantial coding component, and the unit expects you to be comfortable using R before you start.
How to study for the exam
Build the routing table first and keep adding to it. One page with the four questions down the side, response type, number of groups, paired or not, and goal, and the tests down the other, will do more for your mark than rereading the formulas, because most multiple choice items in this unit are recognition questions rather than calculations.
Second, practise writing the five reporting lines for a method you only half remember: hypotheses, assumptions, statistic and its distribution, p-value, conclusion in context. Several of those lines carry marks on their own, so a structurally complete answer with a missing statistic scores most of the way while a blank scores nothing.
Third, keep a single sheet of directions, because this unit is full of statements that are easy to reverse: the prediction interval is the wider one, lower information criterion values are better, a larger complexity constant gives a smaller tree, the log-odds scale is neutral at zero and the odds ratio scale at one.
Fourth, do the workshop exercises when they are set rather than before the exam, since they are assessed weekly and only the best eight count, so an exercise skipped in Week 4 cannot be recovered in Week 10. Fifth, treat every printed result you meet as a comprehension exercise: cover the interpretation, decide which number answers the question, then check.
Since the paper is sat without a calculator, spend your final revision writing expressions and degrees of freedom rather than finishing arithmetic, and make sure that every practice answer you write ends with a sentence naming the variables and the population rather than stopping at the decision.
Your AI Statistics tutor for DATA2002
Stuck on a hard DATA2002 question? Sia is AskSia’s AI Statistics tutor — ask any DATA2002 Data Analytics: Learning from Data question and get a clear, step-by-step explanation grounded in how the course is actually taught and assessed. Read this whole study guide free, then take your hardest questions to Sia.
