ETC1010: Data Modelling and Computing

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# ETC1010: Data Modelling and Computing
## Lecture 10B: Exam Review
### Dr. Nicholas Tierney & Professor Di Cook
### EBS, Monash U.
### 2019-10-11

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# Announcements

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- 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
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# The Practical Exam

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- 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)
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# The Project Data Milestone: What do Submit

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- 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.)
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# The Project: More general questions

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- "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
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# The project: What should we present?

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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)
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# Exam details

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- 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).
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# Exam Details

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- 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.

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# Tidy data Concepts:

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- Variables
- Observations
- Tidy data
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# Data Wrangling Concepts:

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- Converting "messy" data to tidy data
- Code / key functions to use to convert data into "tidy" data
  - e.g, `gather`, `spread`, `separate`, etc.
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# Relational data concepts:

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- Left Join
- Why do joins of data
- Predict output of a join
- Sketching out code to summarise data from a join
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# Data visualisation concepts:

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- 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?"
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# Missing Values concepts:

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- 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
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# Linear Models Concepts:

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- Write down an equation of a model from code output
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- The formula `$y = 3x + 5$` is a function with input `$x$`, and output `$y$`, when x is `___` , the output is `___`
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# Linear Models Concepts:

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`$$\widehat{height_{in}} \sim 3.62 + 0.78 Width_{in}$$`
- **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?

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# Linear Models Concepts:

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- 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
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# Programming concepts:

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- 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
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# Networks Concepts:

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- 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)
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# How to study for the exam

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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.
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# On a more personal note

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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!
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Also a special thanks to everyone for dealing with the 8am lecture!
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