--- title: "ETC1010: Data Modelling and Computing" subtitle: "Lecture 10B: Exam Review" author: "Dr. Nicholas Tierney & Professor Di Cook" institute: "EBS, Monash U." date: "`r Sys.Date()`" output: xaringan::moon_reader: lib_dir: libs css: ["shinobi", "ninjutsu", "slides.css"] seal: true self_contained: false nature: ratio: "16:9" highlightStyle: github highlightLines: true countIncrementalSlides: false --- ```{r setup, include=FALSE} library(emo) library(tidyverse) library(knitr) library(lubridate) knitr::opts_chunk$set( fig.width = 8, fig.height = 4.5, fig.retina = 3, fig.align = "center", out.width = "90%", message = FALSE, warning = FALSE, cache = TRUE, autodep = TRUE, hiline = TRUE ) knitr::opts_hooks$set(fig.callout = function(options) { if (options$fig.callout) { options$echo <- FALSE options$out.height <- "99%" options$fig.width <- 16 options$fig.height <- 8 } options }) options( htmltools.dir.version = FALSE, width = 90, max.print = 9999, knitr.table.format = "html" ) as_table <- function(...) knitr::kable(..., format = "html", digits = 3) ``` class: bg-main1 # Announcements .huge[ - Some of the lecture slides don't load on a web browser - Current alternative for viewing: Download the HTML or the RMD for the moment and view locally. - I'm in the process of sorting it out ] --- class: bg-main1 # The Practical Exam .huge[ - Time: 1 Hour - Task: Complete a data analysis and answer questions (like the assignments, but short - similar to the in class exercises) - Conditions: - Open book - Internet access - No contacting / messaging / communicating friends during exam (penalty will be the same as cheating in exam) ] --- class: bg-main1 # The Project Data Milestone: What do Submit .huge[ - An Rmd file + - The rendered HTML file - The data - Zipped / compressed together - In the document: - Write about the data - what is it, what are the variables, what sort of questions are you asking, what did you need to do clean the data, what cleaning is remaining? - (About 1-2 paragraphs of text.) ] --- class: bg-main1 # The Project: More general questions .huge[ - "How many questions should we ask?" - About 3 - roughly one per person in your group - "How will the project be marked?" - You will all mark the projects as you watch them - You will need to present for 5 minutes max - use this to help frame how long your analysis should be. - 5 minutes is short - but it is actually a pretty serious challenge! - But also consider that the project will be marked by me as well - you won't need to cover everything ] --- class: bg-main1 # The project: What should we present? .huge[ Many options! - You can submit an Rmarkdown document + HTML file - You present with a [flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/) (demo) - You could even present as a slide show (like the lecture slides) - How to share - Ideally you can use netlify drag and drop (demo) ] --- class: bg-main1 # Exam details .huge[ - Worth 50% of your final grade - NOT Open Book - Calculator: Yes - Allowed to take in "Only HP 10bII+ or Casio FX82 (any suffix) calculator permitted" - One A4 page of notes, printed on both sides. This will be collected at the end of the examination. - **Hurdle requirement**: You must get 40% on the exam to pass the course (also true of the final project). ] --- class: bg-main1 # Exam Details .huge[ - I'll now talk about the questions in the exam, and some of the concepts you need to be familiar with - These concepts will help guide what you focus on in the lectures - Disclaimer: This list is not exhaustive - these are to help give you a sense of what I'm thinking about for each of the questions in the exam. ] --- class: bg-main1 # Tidy data Concepts: .huge[ - Variables - Observations - Tidy data ] --- class: bg-main1 # Data Wrangling Concepts: .huge[ - Converting "messy" data to tidy data - Code / key functions to use to convert data into "tidy" data - e.g, `gather`, `spread`, `separate`, etc. ] --- class: bg-main1 # Relational data concepts: .huge[ - Left Join - Why do joins of data - Predict output of a join - Sketching out code to summarise data from a join ] --- class: bg-main1 # Data visualisation concepts: .huge[ - How the grammar of graphics produces a plot - identify plots produced by which code - Understand the focus of a given graphic on the data - questions like: - "What and how does this graphic make us focus on what feature of the data?" - "What do you learn from a graphic?" ] --- class: bg-main1 # Missing Values concepts: .huge[ - Principles of tidy missing data - Interpreting graphics of missing data - Predict output of function on data with missing values - Imputation - what is it, which methods are good / bad / better ] --- class: bg-main1 # Linear Models Concepts: .huge[ - Write down an equation of a model from code output ] -- .huge[ - The formula $y = 3x + 5$ is a function with input $x$, and output $y$, when x is `___` , the output is `___` ] --- class: bg-main1 # Linear Models Concepts: .huge[ $$\widehat{height_{in}} \sim 3.62 + 0.78 Width_{in}$$ - **slope**: For each additional inch the painting is wider, the height is expected to be higher, on average, by 0.78 inches. - **Intercept**: Paintings that are 0 inches wide are expected to be 3.62 inches high, on average. - If a paining is 5 Inches wide, what is it's estimated height? ] --- class: bg-main1 # Linear Models Concepts: .huge[ - How to make predictions from a fitted model - Understand what makes predictions good and bad - Measurements of model fit: - R2 - what it is - what values mean good/bad fit? - **A good idea to bring in an equation of R2** - Residuals - What do we expect to see - Centering variables - Think about how you can improve fit of models to your data ] --- class: bg-main1 # Programming concepts: .huge[ - Why write functions - How to write a function in R - How to take existing code and turn it into a function - Identify potential mistakes in provided code - Understand what `map` does ] --- class: bg-main1 # Networks Concepts: .huge[ - From an association / correlation matrix, which are most or least related? - Understand how you can convert a numeric matrix into a binary association matrix - Understand how a correlation matrix (or other association) of data relates to a provided network (let's do an example) ] --- class: bg-main1 # How to study for the exam .vlarge[ These concepts guide what to focus on While the readings aren't assessed, **they provide great information that will certainly help improve your understanding** Remember that these are to help you focus, I can't give you the exam, but I can help tell you what is important. Disclaimer: This list is not exhaustive - these are to help give you a sense of what I'm thinking about for each of the questions in the exam. ] --- class: bg-main1 # On a more personal note .huge[ I've had a great time this semester I've been **seriously impressed** with you all - you are all so bright! Thanks for being engaged and curious, I'm really excited to see your projects! ] -- .huge[ Also a special thanks to everyone for dealing with the 8am lecture! ]