Introduction to Statistical Computing

At an intersection of Enigmas of Chance and Corrupting the Young.

Class homepage

Fall 2014

Class announcement

Lectures:
1. Introduction to the Course; Basic Data Types
2. Bigger Data Structures
3. Dataframes and Control
4. Introduction to Strings
5. Regular Expressions

Labs:
1. Exponentially More Fun
2. Things That Go Vroom
3. Scrape the Rich! (rich.html file)

Homework:
1. Rainfall, Data Structures, Sequences
2. Housing, Dataframes, Control

Fall 2013

Class announcement

Lectures:
1. Combined lectures 1 and 2: intro to the class, basic data types, basic data structures, structures of structures

1. Flow control, iteration, vectorization
2. Writing and Calling Functions
3. Writing Multiple Functions
4. Top-Down Design
5. Testing
6. Debugging
7. Functions as Objects
8. Optimization I: Simple Optimization
9. Abstraction and Refactoring
10. Split, Apply, Combine I: Using Basic R
11. Split, Apply, Combine II: Using plyr
12. Simulation I: Generating Random Variables
13. Simulation II: Markov Chains
14. Simulation III: Monte Carlo and Markov Chain Monte Carlo
15. Simulation IV: Quantifying uncertainty with simulations
16. Optimization II: Deterministic, unconstrained optimization
17. Optimization III: Stochastic and constrained optimization
18. Basic character/string manipulation
19. Regular expressions
20. Importing data from web pages
21. Review on text processing
22. Change of representation; text as vectors
23. Databases
24. Simulation V: Matching simulation models to data
25. Speed, computational complexity, going beyond R
26. Computing for statistics

Unnumbered because not actually delivered in class: The Scope of Names

Labs:
1. Basic Probability, Basic Data Structures
2. Only the Answers Have Changed
3. Of Big- and Small- Hearted Cats
4. Like a Jackknife to the Heart
5. Testing Our Way to Outliers
6. I Can Has Likelihood Surface?
7. Bunches of Novels
8. How Antiobiotics Came to Peoria
9. Tremors/a>
10. Scrape the Rich
11. Baseball Salaries

Homework:
1. Rainfall, Data Structures, Obsessive Doodling
2. Tweaking Resource-Allocation-by-Tweaking
3. Hitting Bottom and Calling for a Shovel
4. Standard Errors of the Cat Heart
5. Dimensions of Anomaly
6. I Made You a Likelihood Function, But I Ate It
7. The Intensity of 19th Century Literature
8. Antibiotic Diffusion and Outlier Resistance
9. Canceled
10. A Maze of Twisty Little Passages
11. Several Hundred Degrees of Separation

Exams:
1. Midterm Exam

Self-Evaluation and Lessons Learned

Fall 2012

Class announcement Lectures with no links haven't been delivered yet, and the order an topics may change.

Lectures:
1. Introduction to the class, basic data types, basic data structures
2. More data structures: matrices, data frames, structures of structures
3. Flow Control, Looping, Vectorization
4. Writing and Calling Functions
5. Writing Multiple Functions
6. Top-Down Design
7. Testing
8. Debugging
9. The Scope of Names
10. Functions as Objects
11. Split/Apply/Combine I: Using Basic R
12. Split/Apply/Combine II: Using plyr
13. Abstraction and Refactoring
14. Graphics (canceled)
15. Simulation I: Random variable generation
16. Simulation II: Monte Carlo, Markov chains, Ma