STIN300 Statistical Programming in R
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Showing course contents for the educational year starting in 2017 .
Course responsible: Torgeir Rhodén Hvidsten, Lars Gustav Snipen
Teachers: Solve Sæbø
ECTS credits: 5
Faculty: Faculty of Chemistry, Biotechnology and Food Science
Teaching language: EN, NO
(NO=norsk, EN=Engelsk)
(NO=norsk, EN=Engelsk)
Limits of class size:
100
Teaching exam periods:
This course starts in the January block. This course has teaching/evaluation in the January block
Course frequency: Annually
First time: 2010H
Course contents:
The first part contains an introduction to basic programming in R. Topics are operators, variables, data types, basic data structures (vector, matrix, data-frame, list), control structures (loops, conditionals) and functions.
The second part contains file handling, text handling, graphics and packages, with repetitions and applications of elements from the first part.
The third part contains a selection of statistical methods and programming related to these. This part is closely linked to a compulsory project. Topics are linear regression and discriminant analysis, K-nearest-neighbor methods for regression and classification, cross-validation and model selection, bootstrapping and other simulation based techniques.
Learning outcome:
Upon completion of the course the students should be capable of performing statistical analyses using a programming approach in R. The students should be able to make their own functions utilizing/modifying available functions in order to solve specific statistical problems. The students should also be able to present the output from statistical analyses in an accessible and scientific form using text and graphics.
Learning activities:
Lectures combined with extensive interactive programming. Students will work actively on programming exercises in the classes, with a lecturer present, so that difficult topics can be highlighted and given proper attention.
Teaching support:
Written material and videos have been developed for this course, and will be available in Canvas.
Syllabus:
The curriculum will be specified in the beginning of the course.
Prerequisites:
Statistics equivalent to STAT100
Recommended prerequisites:
Introduction to programming
Mandatory activity:
Project exercise. This must be approved before the exam.
Assessment:
Written exam, 3.5 hrs, counts 100 %.
Nominal workload:
Lectures/exercises 60 hours. Individual studies 90 hours.
Entrance requirements:
Special requirements in Science
Type of course:
Lectures/interactive computer lab 4 hours daily in three weeks.
Note:
Students must bring their own laptop with Windows, Linux or MAC OS.
Examiner:
An external examiner evaluates the exam, and grades 25 selected exam papers.
Allowed examination aids: C3 Alle typer kalkulatorer, alle andre hjelpemidler - inkludert digitale
Examination details: One written exam: Bestått / Ikke bestått