-
Notifications
You must be signed in to change notification settings - Fork 14
/
Copy pathcourse_content.Rmd
344 lines (176 loc) · 15.4 KB
/
course_content.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
---
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) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Introduction to R script](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week1/introduction_to_R.R) <i class="fa fa-code" aria-hidden="true"></i>
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week1/lect1_sta130_nt.Rmd) <i class="fa fa-github" aria-hidden="true"></i>
[Class slides - Prof. Gibbs](week1/introtoggplot.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week1/introtogglot.Rmd) <i class="fa fa-github" aria-hidden="true"></i>
[Happiness Datasets](https://github.com/ntaback/UofT_STA130/tree/master/week1) <i class="fa fa-table" aria-hidden="true"></i>
<i class="fa fa-book" aria-hidden="true"></i> 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) <i class="fa fa-html5" aria-hidden="true"></i>
[Example solutions to practice problems](week1/Week1PracticeProblems-solutions.html) <i class="fa fa-html5" aria-hidden="true"></i>
<br>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) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Class slides](week2/lect2_sta130_nt-ver1.html) <i class="fa fa-html5" aria-hidden="true"></i>
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week2/lect2_sta130_nt-ver1.Rmd)
<i class="fa fa-github" aria-hidden="true"></i></a>
[Annotated slides - 10:00 class](week2/STA130_Class 2_NT_10.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Annotated slides - 14:00 class](week2/STA130_Class 2_2pm.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Trump's Tweets](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week2/trumptweets.csv) <i class="fa fa-table" aria-hidden="true"></i>
#### 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) <i class="fa fa-external-link" aria-hidden="true"></i>
[The Economic Guide To Picking A College Major - FiveThirtyEight](https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/) <i class="fa fa-external-link" aria-hidden="true"></i>
[dplyr cheat sheet #1](data-transformation.pdf), [dplyr cheat sheet #2](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf)
<i class="fa fa-book" aria-hidden="true"></i> Modern Data Science with R: 4.1, 4.2, 4.3, 4.4, 5.1
## January 19 Tutorial
[Practice problems](week2/Week2PracticeProblems-student.html) <i class="fa fa-html5" aria-hidden="true"></i>
[Example solutions to practice problems](week2/Week2PracticeProblems-NT.html) <i class="fa fa-html5" aria-hidden="true"></i>
# Week 3
## January 22 Class
### Slides and References
[Class slides](week3/STA130_class3_nt.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Class slides](week3/lect4_sta130_nt.html) <i class="fa fa-html5" aria-hidden="true"></i>
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week3/lect4_sta130_nt.Rmd) <i class="fa fa-github" aria-hidden="true"></i></a>
[Annotated slides - 10:00 class](week3/STA130_class3_10.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Annotated slides - 14:00 class](week3/STA130_class3_nt_2pm.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
<i class="fa fa-book" aria-hidden="true"></i>
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) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
<i class="fa fa-book" aria-hidden="true"></i>
2. [R for Data Science. G. Grolemund and H. Wickham. Chapter 5](http://r4ds.had.co.nz/transform.html) <i class="fa fa-external-link" aria-hidden="true"></i>
## January 26 Tutorial
[Practice Problems](week3/Week3PracticeProblems-student.html) <i class="fa fa-html5" aria-hidden="true"></i>
[Example solutions to practice problems](week3/Week3PracticeProblems-NT.html) <i class="fa fa-html5" aria-hidden="true"></i>
# Week 4
## January 29 Class
### Slides and References
[Announcements](week4/STA130announcement_January29.pdf)
[Class slides](week4/Week4_Testing1.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Class slides](week4/Week4_Testing1.html) <i class="fa fa-html5" aria-hidden="true"></i>
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week4/Week4_Testing1.Rmd) <i class="fa fa-github" aria-hidden="true"></i></a>
[Annotated slides - 10:00 class](week4/STA130_Week4_Testing1_am.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Annotated slides - 14:00 class](week4/STA130_Week4_Testing1_pm.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
<i class="fa fa-book" aria-hidden="true"></i> [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) <i class="fa fa-external-link" aria-hidden="true"></i>
## February 2 Tutorial
[Practice Problems](week4/Week4PracticeProblems-student.html) <i class="fa fa-html5" aria-hidden="true"></i>
[Example solutions to practice problems](week4/Week4PracticeProblems-solutions.html) <i class="fa fa-html5" aria-hidden="true"></i>
<br>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) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Class slides](week5/Week5_Testing2.html) <i class="fa fa-html5" aria-hidden="true"></i>
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week5/Week5_Testing2.Rmd) <i class="fa fa-github" aria-hidden="true"></i></a>
[Annotated slides - 10:00 class](week5/Week5_Testing2_am.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Annotated slides - 14:00 class](week5/Week5_Testing2_pm.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
<i class="fa fa-book" aria-hidden="true"></i> [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) <i class="fa fa-external-link" aria-hidden="true"></i>
## February 9 Tutorial
[Practice Problems](week5/Week5PracticeProblems-student.html) <i class="fa fa-html5" aria-hidden="true"></i>
[Example solutions to practice problems](week5/Week5PracticeProblems-solutions.html) <i class="fa fa-html5" aria-hidden="true"></i>
# Week 6
## February 12 Class
### Slides and References
[Class slides](week6/Week6_CIs.pdf) (Watch for the typo on slide 46!) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Class slides](week6/Week6_CIs.html) (Watch for the typo on slide 46!) <i class="fa fa-html5" aria-hidden="true"></i>
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week6/Week6_CIs.Rmd) <i class="fa fa-github" aria-hidden="true"></i></a>
[Announcement about Mental Health Project](week6/Mental_Health_Project_Recruitment.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Annotated slides - 10:00 class](week6/Week6_CIs_am_annotated.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Annotated slides - 14:00 class](week6/Week6_CIs_pm_annotated.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
<i class="fa fa-book" aria-hidden="true"></i> Modern Data Science with R: 7.1, 7.2, 7.3
## February 16 Tutorial
[Practice Problems](week6/Week6PracticeProblems-student.html) <i class="fa fa-html5" aria-hidden="true"></i>
[Example solutions to practice problems](week6/Week6PracticeProblems-solutions.html) <i class="fa fa-html5" aria-hidden="true"></i>
# 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) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Class slides](week7/sta130_test_review.html) <i class="fa fa-html5" aria-hidden="true"></i>
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week7/sta130_test_review.Rmd) <i class="fa fa-github" aria-hidden="true"></i></a>
[Annotated slides - 10:00 class](week7/test_review_annotated_am.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Annotated slides - 14:00 class](week7/test_review_annotated_pm.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Example solutions to test review](week7/sta130_test_review_solutions.html) <i class="fa fa-html5" aria-hidden="true"></i>
# Week 8
## March 8 Class
### Slides and References
[Class slides](week8/Class8_ClassificationTrees1.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Class slides](week8/Class8_sta130_nt_ver1.html) <i class="fa fa-html5" aria-hidden="true"></i>
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week8/Class8_sta130_nt_ver1.Rmd) <i class="fa fa-github" aria-hidden="true"></i></a>
[Annotated slides - 10:00 class](week8/Week8_ClassificationTrees_10.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Annotated slides - 14:00 class](week8/Class8_ClassificationTrees1_2.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
<i class="fa fa-book" aria-hidden="true"></i> 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) <i class="fa fa-html5" aria-hidden="true"></i>
# Week 9
## March 12 Class
### Slides and References
[Class slides](week9/sta130_lecture9.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Class slides](week9/sta130_lecture9.html) <i class="fa fa-html5" aria-hidden="true"></i>
[Annotated slides - 10:00 class](week9/sta130_lecture9_10.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Annotated slides - 14:00 class](week9/sta130_lecture9_2.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week9/sta130_lecture9.Rmd) <i class="fa fa-github" aria-hidden="true"></i></a>
<i class="fa fa-book" aria-hidden="true"></i> 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
<iframe width="560" height="315" src="https://www.youtube.com/embed/zWKm22p201U" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
## March 16 Tutorial
[Practice Problems](week9/Week9PracticeProblems-student.html)
[Example solutions to practice problems](week9/Week9PracticeProblems-solutions.html) <i class="fa fa-html5" aria-hidden="true"></i>
# Week 10
## March 19 Class
### Slides and References
[Class slides](week10/Week10_Regression2.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Class slides](week10/Week10_Regression2.html) <i class="fa fa-html5" aria-hidden="true"></i>
[Annotated slides - 10:00 class](week10/Week10_Regression2_am_annotated.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Annotated slides - 14:00 class](week10/Week10_Regression2_pm_annotated.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[R Markdown source of slides](https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week10/Week10_Regression2.Rmd) <i class="fa fa-github" aria-hidden="true"></i></a>
<i class="fa fa-book" aria-hidden="true"></i>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) <i class="fa fa-html5" aria-hidden="true"></i>
# Week 11
## March 26 Class
[Annotated slides - 10:00 class](week11/sta130_lecture11_beam_10.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Annotated slides - 14:00 class](week11/sta130_lecture11_beam_2.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Class slides](week11/sta130_lecture11_beam.pdf) <i class="fa fa-file-pdf-o" aria-hidden="true"></i>
[Class slides](week11/sta130_lecture11.html) <i class="fa fa-html5" aria-hidden="true"></i>
<i class="fa fa-book" aria-hidden="true"></i> 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) <i class="fa fa-html5" aria-hidden="true"></i>
# 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) <i class="fa fa-github fa-2x" aria-hidden="true"></i>