index.Rmd

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output: 
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   theme: flatly
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---
```{r, include=FALSE}
htmltools::tagList(rmarkdown::html_dependency_font_awesome())
```
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<div class=xlfont> 
<mark>The course website for STA130 Winter 2019 can be found on [Quercus](https://q.utoronto.ca) </mark>

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## <span class="glyphicon glyphicon-chevron-right" aria-hidden="true"></span> Midterm Test Information

Midterm test information is available [here](Fall2018/term_test_Fall2018.html).


## <span class="glyphicon glyphicon-chevron-right" aria-hidden="true"></span> TA Office Hours


TA office hours will be held in SS623B (Sidney Smith lower level).  


**Schedule for TA office hours after December 6**

- Thursday, December 13, 11am-1pm
- Friday, December 14, 11am-1pm
- Monday, December, 17, 2-5pm
- Tuesday, December 18, 2-5pm

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Weekly office hours will be held on:

- Wednesday: 14:00 - 17:00
- Thursday: 11:00 - 13:00

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## <span class="glyphicon glyphicon-chevron-right" aria-hidden="true"></span> Instructors

[Professor Nathan Taback](http://utstat.toronto.edu/~nathan/) and Professor Nathalie Moon.

## <span class="glyphicon glyphicon-chevron-right" aria-hidden="true"></span> Syllabus

Important information about the course can found on the [Fall 2018 syllabus](Fall2018/STA130syllabus2018S-Fall2018.html).

## <span class="glyphicon glyphicon-chevron-right" aria-hidden="true"></span> Course Calendar

Important course dates can be found in the [course calender](Fall2018/STA130-Fall2018-Calendar.html).

## <span class="glyphicon glyphicon-chevron-right" aria-hidden="true"></span> Computing

The course will use [R](https://www.r-project.org) for computing. R is freely available [here](http://cran.utstat.utoronto.ca). We recommend using [R Studio](https://www.rstudio.com) which can be [downloaded](http://cran.utstat.utoronto.ca) for free.  We will use [R Markdown](http://rmarkdown.rstudio.com) as an authoring framework for creating reproducible data science documents.

### <span class="glyphicon glyphicon-chevron-right" aria-hidden="true"></span> Getting Started with R

- If you have never programmed before then [Hands-On Programming with R, by Garrett Grolemund](https://d1b10bmlvqabco.cloudfront.net/attach/ighbo26t3ua52t/igp9099yy4v10/igz7vp4w5su9/OReilly_HandsOn_Programming_with_R_2014.pdf) is a great place to start.
- [R for Data Science, by Hadley Wickham and Garrett Grolemund](http://r4ds.had.co.nz) is a wonderful resource. 
- A list of more resources is available on the course website [here](R_resources.html).

### RStudio Cloud or RStudio Desktop

1. Students in the course will be able to do all computing on R Studio Cloud <https://rstudio.cloud>

2. Another alternative is to install RStudio on your own computer.  You will need to download [R](http://cran.utstat.utoronto.ca) then [RStudio](https://www.rstudio.com/products/rstudio/download/#download). 



<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons Licence" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />This website is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

R Markdown source for this website <a href="https://github.com/ntaback/UofT_STA130">
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</a>