There are 40 skills to demonstrate across the year and they are all listed in this table. Once you’ve demonstrated a skill, it’s yours forever, and you can attempt a skill as many times as you like with no penalty for trying again (it’s your "best attempt", and not the most recent one, that indicates if you have demonstrated a skill).

Some skills are automatically graded: these are the multiple-choice style questions. These skills will be marked, and your demonstration of the skill will be updated, every time you attempt the skill.

Other skills require manual grading by somebody on the teaching team: these are the short-answer write-up questions. These skills are indicated by a *. We will mark these skills after every 5-week teaching block, so if you submit by the last Friday of each block, it will be marked in the subsequent weeks. You can submit a response to these questions any time you want as the semester goes on, or even submit multiple responses — at the end of each block we will grade the most recently submitted response.

What about the exam? The exam will not include any programming skills, because programming is typically a process trial and error, which cannot be captured in an exam. It will also not include any reflecting skills. Questions in the exam will cover 20 of the skills from the middle 5 columns of the table below.

Summarising Visualising Analysing Inferring Communicating Programming
(not in the exam)		 Reflecting
(not in the exam)
Block 1 S1I can identify whether a variable is continuous, categorical, or ordinal.
S2I can compute appropriate measure(s) of central tendency for a given variable.
S3I can compute appropriate measure(s) of dispersion for a given variable.
 V1I can appropriately plot the distribution of a single variable.
V2I can customise the appearance of ggplots.
 P1I can identify the cause of common error messages in R.
P2I can manage libraries/packages in R.
P3I can modify the arguments of a given R function to arrive at the desired output.
Block 2 S4I can transform a variable into z-scores.
 A3*
(marked after Block2, Block3, Block4)I can explain what a sampling distribution is.
A1I can calculate the standard error of the mean for a given sample.
A2I can compute a 95% confidence interval for the population mean.
 I3I can identify how different factors affect the bias and precision of an estimate
I4I can identify the difference between a Type I error and a Type II error.
I2*
(marked after Block2, Block3, Block4)I can explain how null hypothesis significance testing (NHST) works.
I1I can appropriately interpret a 95% confidence interval.
 P4I can extract specific subsets of a given dataset.
 R1*
(marked after Block2, Block3, Block4)I can write approximately 200 words reflecting on what I experienced and what I learned in DAPR in Semester 1.
Block 3 S5I can compute measures that summarise how two variables are related
 V4I can appropriately plot how two variables are related
V3I can appropriately describe patterns of data shown in a plot.
 A6I can implement a given statistical test.
A4I can check the assumptions underlying basic statistical tests.
A5I can identify the appropriate type of basic statistical test for a given question/design.
 I6I can tell the difference between statistical significance and practical significance.
I5I can identify whether I can or cannot reject the null hypothesis.
I7I can define appropriate null and alternative hypotheses for a given research question.
 C1*
(marked after Block3, Block4)I can describe the steps taken in a given analysis.
C2*
(marked after Block3, Block4)I can describe in writing the results of a given basic statistical test.
 P5I can wrangle/modify data as required for the given analysis task.
P6I can identify what data wrangling/modifications are required for a given analysis task.
Block 4 V5I can visually estimate the parameters of a line from a plot.
 A10I can represent binary predictors using treatment coding.
A7I can use a fitted simple linear model to predict an outcome value for a given predictor value
A8I can appropriately interpret the parameters of a simple linear model.
A9*
(marked after Block4)I can explain how both a t-test and a correlation can be expressed as a linear model.
 I8I can identify the null hypothesis being tested for the parameters of a linear model.
 C3*
(marked after Block4)I can describe in writing the results of a given simple linear model.
 R2*
(marked after Block4)I can write approximately 200 words reflecting on what I experienced and what I learned in DAPR in Semester 2.
R3*
(marked after Block4)I can rate my confidence across the set of DAPR1 skills.