The teaching experience Introduction to S programming: a Audience teaching experience and a manual First year Actuarial Science students Goal is to learn the programming language, not the statistical software Second programming language (after VBA) Who? Vincent Goulet Constraints Large group of students (around 80) From? École d’actuariat, Université Laval Québec, Canada Limited access to computer labs Very little time devoted to the topic (4 weeks) The teaching experience The lab: to go or not to go? Audience Pros First year Actuarial Science students Hands-on approach to learning Goal is to learn the programming language, Variable pace per student not the statistical software Lab work can be done at home Second programming language (after VBA) Cons Constraints “Theory” difficult to teach Large group of students (around 80) Easy for students to do “something else” Limited access to computer labs Limited number of PCs: some just watch Very little time devoted to the topic (4 weeks)
The lab: to go or not to go? My solution Compromise Time spent in class Pros Time spent in the lab Hands-on approach to learning Variable pace per student In class Lab work can be done at home 1 hour per week Presentation of concepts, functions, etc. Cons Basically no examples “Theory” difficult to teach Easy for students to do “something else” In lab Limited number of PCs: some just watch 2 hours per week Students mainly execute provided script files Some interventions by the instructor My solution My solution Compromise Compromise Time spent in class Time spent in class Time spent in the lab Time spent in the lab In class In class 1 hour per week 1 hour per week Presentation of concepts, functions, etc. Presentation of concepts, functions, etc. Basically no examples Basically no examples In lab In lab 2 hours per week 2 hours per week Students mainly execute provided script files Students mainly execute provided script files Some interventions by the instructor Some interventions by the instructor
The manual: Introduction to S The manual: Introduction to S programming programming What it is What it is Collection of class notes and scripts Collection of class notes and scripts Much influenced by chapters 1–3 of MASS Much influenced by chapters 1–3 of MASS Fully indexed Fully indexed Published under the GNU FDL Published under the GNU FDL Also covers Also covers Optimization functions Optimization functions Linear regression Linear regression Time series analysis Time series analysis Random number generation Random number generation Efficient simulation Efficient simulation Emacs and ESS Emacs and ESS
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