This will be a reading-intensive, seminar-style course consisting of in-class discussions and activities, and students are expected to participate actively in class. Check for weekly updates as the schedule will be adjusted according to progress. Note that in the schedule below book readings are annotated as in the following examples: M(1)~McElreath 2020 chapter 1, H&H(3)~Hobbs and Hooten 2015 chapter 3, I(2.1-2.4)~Irizarry chapter sections 2.1 through 2.4, and L(4)~Lavine chapter 4. Book chapters and other assigned readings should be completed by the date on which they appear in the schedule . Class periods will be focused on the assigned readings for that day.
| Meeting | Statistics | R | ||||
|---|---|---|---|---|---|---|
| Readings | Videos | Questions | Readings | Videos | Exercises | |
| 1: May 25 | M(1) The Golem of Prague (18 pages); Supplementary: H&H(3.1-3.2) Principles of probability (8 pages) |
The Golem of Prague (Lecture 1; ch 1; 1:00 hours)
Slides Lecture 1 |
Questions | I(1) Getting started with R and RStudio (8 pages) | History of R (16:07 mins) R data types (1) (9:26 mins) R data types (2) (9:45 mins) |
Exercise
Solutions |
| 2: May 26 | M(2) Small worlds and large worlds (21 pages); Supplementary: H&H(3.3-3.4.2) Principles of probability (12 pages) |
The Garden of Forking Data (Lecture 2; Ch 2 & 3; 1:05 hours)
Slides Lecture 2 |
Questions | I(2.1-2.6) R basics (17 pages) |
R data types (3) (11:51 mins) Subsetting (1) (7:01 mins) Subsetting (2) (10:18 mins) |
Exercise
Solutions |
| 3: May 27 | M(3) Sampling the imaginary (23 pages); Supplementary: H&H(3.4.3-3.4.5) Principles of probability (21 pages) |
Geocentric Models (Lecture 3; ch 4; 1:02 hours)
Slides Lecture 3 |
Questions | I(2.7-2.16) R basics (14 pages) |
Vectorization (3:46 mins) Reading and Writing (1) (12:55 mins) Reading and Writing (2) (9:30 mins) |
Exercise
Solutions |
| 4: June 1 | M(4) Geocentric Models (55 pages); Supplementary: H&H (4) Likelihood (8 pages) |
Wiggly orbits (Lecture 4; ch 5; 1:01 hours)
Slides Lecture 4 |
Questions | I(3.1-3.6) Programming basics (7 pages) |
Control Structures (1) (7:10 mins) Control Structures (2) (8:11 mins) Writing first function (10:29 mins) |
Exercise
Solutions |
| 5: June 2 | M(5) The many variables & the spurious waffles (39 pages); Supplementary: H&H (5 - 5.3) Simple Bayesian Models (11 pages) |
The many variables & the spurious waffles (Lecture 5; 1:01 hours)
Slides Lecture 5 |
Questions | I(4.1-4.6) The tidyverse (7 pages) |
Functions (1) (9:17 mins) Functions (2) (7:13 mins) |
Exercise
Solutions |
| 6: June 3 | M(6) The haunted DAG and the causal terror (31 pages); Supplementary: H&H (5.4) Simple Bayesian Models (15 pages) |
The haunted DAG and the causal terror (Lecture 6; 1:01 hours)
Slides Lecture 6 |
Bayesian Linear Regression |
I(4.10-4.15) The tidyverse (8 pages) |
Scoping rules (1) (10:32 mins) Scoping rules (2) (8:34 mins) |
Exercise
Solutions |
| 7: June 8 | M(7) Ulysses' compass (47 pages); Supplementary: H&H (6 - 6.2.1) Hierarchical Bayesian Models (17 pages) |
Back door and Ulysses' compass (Lecture 7; 1:01 hours);
Slides Lecture 7 |
I(5) Importing data (10 pages); I(6) Introduction to data visualization (4 pages) |
Scoping rules (3) (9:21 mins) Coding standards (8:59 mins) |
Exercise
Solutions Link to Teams video |
|
| 8: June 9 | M(8) Conditional manatees (27 pages); Supplementary: H&H (6.2.2 - 6.2.3) Hierarchical Bayesian Models (10 pages) |
Ulysses' compass (Lecture 8; 1:05 hours)
Slides Lecture 8 |
Questions | I(7) ggplot2 (16 pages) |
Dates and times (10:29 mins)
lapply (9:23 mins) apply (7:21 mins) |
Exercise
Solutions Link to Teams video |
| 9: June 10 | M(9) Markov Chain Monte Carlo (37 pages); Supplementary: H&H (6.2.4 - 6.3) Hierarchical Bayesian Models (11 pages) |
Conditional manatees (Lecture 9; 0:59 hours)
Watch lecture 10 before next class: Markov Chain Monte Carlo (Lecture 10; 1:03 hours) Slides Lecture 9 Slides Lecture 10 |
I(8.1-8.15) Visualizing data distributions (23 pages) |
mapply (4:46 mins) tapply (3:17 mins) split (9:09 mins) |
Exercise
Solutions Data Analysis Project Data for Data Analysis Project |
|
| 10: June 15 | M(10) Big entropy and the generalized linear model (25 pages); Supplementary: H&H (7.1 - 7.3.1) Markov Chain Monte Carlo (14 pages) |
Big entropy and the generalized linear model (Lecture 11; 1:01 hours)
Slides Lecture 11 |
Binomial/Logistic Regression
Link to Teams video |
I(8.15-8.17) Visualizing data distributions (9 pages) |
Debugging (1) (12:33 mins) Debugging (2) (6:25 mins) Debugging (3) (8:21 mins) |
|
| 11: June 16 | M(11) God spiked the integers (47 pages); Supplementary: H&H (7.3.2 - 7.3.3) Markov Chain Monte Carlo (13 pages) |
God spiked the integers (Lecture 12; 1:02 hours)
Slides Lecture 12 |
I(20) Introduction to data wrangling (3 pages); I(21) Reshaping data (7 pages) |
str (2) (9:17 mins) Simulation (1) (7:47 mins) Simulation (2) (7:02 mins) |
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| 12: June 17 | This period is left for questions, catch up, group work, etc. |
Rprofiler (1) (10:39 mins) Rprofiler (2) (10:26 mins) |
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