---
title: "STA130 Course Content"
date: 'Winter 2018'
output:
html_document:
toc: true
toc_depth: 2
toc_float: true
theme: flatly
---
This page contains course material such as class slides, practice problems, and tutorial assignments.
# Week 0
## January 5 Tutorial
There are no tutorials on January 5. Instead of attending tutorial we suggest that you spend some time getting acquainted with the basics of [R](https://www.r-project.org). We will be using R throughout the course.
The first classes are on January 8. Before you come to class do the following:
0. Read through the [course syllabus](STA130syllabus2018S.html)
1. Read the [R resources section](R_resources.html) of the course webpage. Make sure to login to http://rstudio.chass.utoronto.ca/ (see [R resources section](R_resources.html) for more details).
2. Sign up for the [Piazza discussion forum](https://piazza.com/utoronto.ca/winter2018/sta130h1/home).
3. Get introduced to R. Two ways to get you started are:
(i) Complete [Datacamp's](https://www.datacamp.com) free online [Introduction to R](https://www.datacamp.com/courses/free-introduction-to-r)
(ii) Read chapters 1, 2, and 3 of [Hands-On Programming with R, by Garrett Grolemund](https://d1b10bmlvqabco.cloudfront.net/attach/ighbo26t3ua52t/igp9099yy4v10/igz7vp4w5su9/OReilly_HandsOn_Programming_with_R_2014.pdf).
You can do both (i) and (ii), but a lot of the same content is covered. If you decide to only complete the readings then make sure to type the commands into the console window in RStudio.
# Week 1
## January 8 Class
[Class slides - Prof. Taback](week1/STA130H1_Class1_NT.pdf)
[Introduction to R script](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week1/introduction_to_R.R)
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week1/lect1_sta130_nt.Rmd)
[Class slides - Prof. Gibbs](week1/introtoggplot.pdf)
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week1/introtogglot.Rmd)
[Happiness Datasets](https://github.com/ntaback/UofT_STA130/tree/master/week1)
Modern Data Science with R: Section 2.1 and chapter 3 up to and including section 3.2.2.
## January 12 Tutorial
[Practice problems](week1/Week1PracticeProblems-student.html)
[Example solutions to practice problems](week1/Week1PracticeProblems-solutions.html)
Note: in question 1, the textbook asks for scatterplots of each person's height against their father's height. The x- and y-axes in the plots in the solutions should be switched.
# Week 2
## January 15 Class
### Slides and References
[Class slides](week2/STA130_Class 2_NT.pdf)
[Class slides](week2/lect2_sta130_nt-ver1.html)
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week2/lect2_sta130_nt-ver1.Rmd)
[Annotated slides - 10:00 class](week2/STA130_Class 2_NT_10.pdf)
[Annotated slides - 14:00 class](week2/STA130_Class 2_2pm.pdf)
[Trump's Tweets](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week2/trumptweets.csv)
#### Articles of Interest
[For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights - NYT](https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html)
[The Economic Guide To Picking A College Major - FiveThirtyEight](https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/)
[dplyr cheat sheet #1](data-transformation.pdf), [dplyr cheat sheet #2](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf)
Modern Data Science with R: 4.1, 4.2, 4.3, 4.4, 5.1
## January 19 Tutorial
[Practice problems](week2/Week2PracticeProblems-student.html)
[Example solutions to practice problems](week2/Week2PracticeProblems-NT.html)
# Week 3
## January 22 Class
### Slides and References
[Class slides](week3/STA130_class3_nt.pdf)
[Class slides](week3/lect4_sta130_nt.html)
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week3/lect4_sta130_nt.Rmd)
[Annotated slides - 10:00 class](week3/STA130_class3_10.pdf)
[Annotated slides - 14:00 class](week3/STA130_class3_nt_2pm.pdf)
1. [Hands on Programming With R. G. Grolemund - Chapters 1-5](https://d1b10bmlvqabco.cloudfront.net/attach/ighbo26t3ua52t/igp9099yy4v10/igz7vp4w5su9/OReilly_HandsOn_Programming_with_R_2014.pdf)
2. [R for Data Science. G. Grolemund and H. Wickham. Chapter 5](http://r4ds.had.co.nz/transform.html)
## January 26 Tutorial
[Practice Problems](week3/Week3PracticeProblems-student.html)
[Example solutions to practice problems](week3/Week3PracticeProblems-NT.html)
# Week 4
## January 29 Class
### Slides and References
[Announcements](week4/STA130announcement_January29.pdf)
[Class slides](week4/Week4_Testing1.pdf)
[Class slides](week4/Week4_Testing1.html)
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week4/Week4_Testing1.Rmd)
[Annotated slides - 10:00 class](week4/STA130_Week4_Testing1_am.pdf)
[Annotated slides - 14:00 class](week4/STA130_Week4_Testing1_pm.pdf)
[Introductory Statistics with
Randomization and Simulation - Sections 2.3.1, 2.3.2, 2.3.7 and 2.4 ](https://www.openintro.org/stat/textbook.php?stat_book=isrs)
## February 2 Tutorial
[Practice Problems](week4/Week4PracticeProblems-student.html)
[Example solutions to practice problems](week4/Week4PracticeProblems-solutions.html)
Typo in solution to Question 2 corrected on March 1. It used to say the test statistic is 0.38 in one spot, but the test statistic is 0.17, as used elsewhere else in the solution.
# Week 5
## February 5 Class
### Slides and References
**Note:** A new version of the unannotated slides was posted February 8 (both html and pdf). This version corrects a few typos noted in class plus a typo on pages 58 and 59 (in the mathematical note that you're not responsible for).
[Class slides](week5/Week5_Testing2.pdf)
[Class slides](week5/Week5_Testing2.html)
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week5/Week5_Testing2.Rmd)
[Annotated slides - 10:00 class](week5/Week5_Testing2_am.pdf)
[Annotated slides - 14:00 class](week5/Week5_Testing2_pm.pdf)
[Introductory Statistics with
Randomization and Simulation - Sections 2.1, 2.2, 2.3 (excluding 2.3.4)](https://www.openintro.org/stat/textbook.php?stat_book=isrs)
## February 9 Tutorial
[Practice Problems](week5/Week5PracticeProblems-student.html)
[Example solutions to practice problems](week5/Week5PracticeProblems-solutions.html)
# Week 6
## February 12 Class
### Slides and References
[Class slides](week6/Week6_CIs.pdf) (Watch for the typo on slide 46!)
[Class slides](week6/Week6_CIs.html) (Watch for the typo on slide 46!)
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week6/Week6_CIs.Rmd)
[Announcement about Mental Health Project](week6/Mental_Health_Project_Recruitment.pdf)
[Annotated slides - 10:00 class](week6/Week6_CIs_am_annotated.pdf)
[Annotated slides - 14:00 class](week6/Week6_CIs_pm_annotated.pdf)
Modern Data Science with R: 7.1, 7.2, 7.3
## February 16 Tutorial
[Practice Problems](week6/Week6PracticeProblems-student.html)
[Example solutions to practice problems](week6/Week6PracticeProblems-solutions.html)
# Week 7
## February 26 Class
### Slides
[Announcement: Statistical Sciences Career Panel, Saturday March 3](week7/SSU_Career_Panel_2018.png)
[Class slides](week7/test_review.pdf)
[Class slides](week7/sta130_test_review.html)
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week7/sta130_test_review.Rmd)
[Annotated slides - 10:00 class](week7/test_review_annotated_am.pdf)
[Annotated slides - 14:00 class](week7/test_review_annotated_pm.pdf)
[Example solutions to test review](week7/sta130_test_review_solutions.html)
# Week 8
## March 8 Class
### Slides and References
[Class slides](week8/Class8_ClassificationTrees1.pdf)
[Class slides](week8/Class8_sta130_nt_ver1.html)
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week8/Class8_sta130_nt_ver1.Rmd)
[Annotated slides - 10:00 class](week8/Week8_ClassificationTrees_10.pdf)
[Annotated slides - 14:00 class](week8/Class8_ClassificationTrees1_2.pdf)
Modern Data Science with R: 8.1, 8.2, 8.4
## March 9 Tutorial
[Practice Problems](week8/Week8PracticeProblems-student1.html)
[Example solutions to practice problems](week8/Week8PracticeProblems-solutions1.html)
# Week 9
## March 12 Class
### Slides and References
[Class slides](week9/sta130_lecture9.pdf)
[Class slides](week9/sta130_lecture9.html)
[Annotated slides - 10:00 class](week9/sta130_lecture9_10.pdf)
[Annotated slides - 14:00 class](week9/sta130_lecture9_2.pdf)
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week9/sta130_lecture9.Rmd)
Modern Data Science with R: page 189, page 465 - 468, page 470.
Geotab Data Scientist Brenda Nguyen's [presentation](https://www.youtube.com/embed/zWKm22p201U) on Hazardous Driving Data
## March 16 Tutorial
[Practice Problems](week9/Week9PracticeProblems-student.html)
[Example solutions to practice problems](week9/Week9PracticeProblems-solutions.html)
# Week 10
## March 19 Class
### Slides and References
[Class slides](week10/Week10_Regression2.pdf)
[Class slides](week10/Week10_Regression2.html)
[Annotated slides - 10:00 class](week10/Week10_Regression2_am_annotated.pdf)
[Annotated slides - 14:00 class](week10/Week10_Regression2_pm_annotated.pdf)
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week10/Week10_Regression2.Rmd)
Section 7.6 of *Modern Data Science with R*
Section 1.4.1 of [*Introductory Statistics with Randomization and Simulation* from OpenIntro](https://www.openintro.org/stat/textbook.php?stat_book=isrs)
## March 23 Tutorial
[Practice Problems](week10/Week10PracticeProblems-student.html)
[Example solutions to practice problems](week10/Week10PracticeProblems-solutions.html)
# Week 11
## March 26 Class
[Annotated slides - 10:00 class](week11/sta130_lecture11_beam_10.pdf)
[Annotated slides - 14:00 class](week11/sta130_lecture11_beam_2.pdf)
[Class slides](week11/sta130_lecture11_beam.pdf)
[Class slides](week11/sta130_lecture11.html)
Modern Data Science with R: Chapter 6.
### Slides and References
## April 30 - No Tutorial ( University Closed on Good Friday)
[Practice Problems](week11/Week11PracticeProblems-student.html)
[Example solutions to practice problems](week11/Week11PracticeProblems-solutions.html)
# Week 12
## April 2 STA130 Poster Fair
[Project Information](project_info.html)
# Tutorial Content
Documents created by TAs for tutorials can be found [here](https://drive.google.com/open?id=1ip3MLIWDOe-pKs2Qs4FBzhbSoNcbhRiY).
[R Markdown source](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/course_content.Rmd)