Applied statistical computing

Spring 2008
(See what past students have to say about the course.)

Tuesday & Thursday. 9:30–10:50. Carver 205

Heike Hofmann,
Hadley Wickham,
Office hours MWF 1:30-2:30pm, TR 11-12am. Pearson 113


Course syllabus describing objectives, software used, modules and assessment.

Cheat sheets

Lectures and timetable

Date Lecture and resources Homework
Data collection and organisation with Excel
15 Jan Introduction and excel basics
17 Jan Data collection and storage Week 1, due 24 Jan.
22 Jan Organising data
24 Jan Summarising and exploring data Week 2, due 5 Feb
29 Jan Summarising and exploring data (2)
31 Jan Graphics in excel Project one,
due 12 Feb/21 Feb.
Introduction to R through graphics
5 Feb Introduction to R
7 Feb Introduction to R (2) Week 4, due 14 Feb.
12 Feb Draft project review
14 Feb More R Week 5, due 21 Feb.
19 Feb Loading and manipulating data in R
21 Feb Loading and manipulating data in R (continued) Week 6, due 28 Feb
Structuring and restructuring data
26 Feb Cancer data: aggregation and advanced graphics
28 Feb Cancer data (continued) Week 7, due 4 Mar
4 Mar Introduction to reshape Project 2
due 13 Mar/1 Apr
6 Mar Reshape (cont) No homework
Automating analyses with R
11 Mar Interactive graphics
13 Mar Interim project report discussion Week 9, due 25 Mar
18 Mar Spring break
20 Mar Spring break
25 Mar Introduction to reshape
27 Mar Being lazy with R
1 Apr Simulation
3 Apr Simulation continued Project 3.
Due 10 Apr/17 Apr/24 Apr.
SAS, the industry standard
8 Apr Introduction to SAS
10 Apr Project data discussion
15 Apr Introduction to SAS (2).
17 Apr Modelling.
Interim projection discussion.
22 Apr Summary and extraction
24 Apr SAS macros Week 14, due 1 May.
Project presentations
29 Apr
(dead week)
Project presentations
1 May
(dead week)
Project presentations
5 May
(finals week)
Project presentations

Useful links

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