ØAADM2000 Business Strategy Course description

Dette emnet gir studentene en innføring i strategisk analyse og ledelse. Studentene tilegner seg innsikt i og forståelse for strategisk analyse, strategisk tenkning og tilnærming, og gjøres dermed i stand til å delta i strategiske planprosesser i en virksomhet. Foretaksstrategi bygger på og anvender kunnskap og ferdigheter fra de øvrige økonomisk-administrative emnene slik at disse sees i sammenheng.

All obligatory exercises must be completed to take the final exam. The final exam is a written examination with invigilation, 4 hours. One internal and one external examiner will assess the answer papers submitted by all candidates.

Ingen forkunnskapskrav.

Kunnskaper

Studenten har kunnskap om

• strategibegrepet, hvordan strategier kan bidra til verdiskaping, lønnsomhet og overlevelse
• strategisk analyse av ressurser og omgivelser, konkurransestrategi og konsernstrategi
• strategiske prosesser, hvilke teorier og antakelser strategiske verktøy bygger på
• dynamiske omgivelser, innovasjon og læring, og iverksetting.
• Studenten har kunnskaper om etiske dilemmaer og samfunnsansvar ved strategiske valg.

Ferdigheter

Studenten kan

• bruke sentrale strategiske verktøy, og gjennom dette arbeidet kunne gjøre reflekterte analyser og valg
• gjennomføre en strategisk analyse for en forretningsenhet med hensyn til omgivelses- og markedsfaktorer så vel som interne ressurser
• anvende og integrere relevante modeller og analyser fra øvrige økonomisk-administrative emner som inngår i utdanningen

Det vil bli brukt varierte arbeidsmåter som forelesninger, diskusjon av dagsaktuelle problemstillinger og ulike typer øvingsoppgaver. Studentene forventes å arbeide med utdelte øvingsoppgaver. Det vil også bli muligheter for frivillige innleveringer i løpet av semesteret.

Ingen obligatoriske arbeidskrav

Admission requirements

This course is primarily aimed at PhD candidates admitted to the PhD programme in Health Sciences and PhD students from Memorial University, Newfoundland. General terms for admission to the course is a completed master's degree in molecular biology or equivalent qualification (e.g. completed MABIO4400). Priority will be given to PhD candidates from OsloMet and Memorial University, Newfoundland.

Note that all students must have a laptop not more than 2 years old (windows 7 or more recent or mac with OS X). The laptop must be able to connect to wireless network.

On completion of the course, the PhD candidate has achieved the following learning outcomes, defined in terms of knowledge, skills, and general competence:

Knowledge

The PhD candidate

• is able to conduct bioinformatics analysis projects in agreement with best practice (transparency and reproducibility) in the field of bioinformatic science's philosophy
• is in the forefront of knowledge about the current high throughput sequencing (HTS) technologies and understands the differences, benefits and drawbacks of these HTS technologies
• can evaluate and make sound decisions on which platform and bioinformatic approach to use for different HTS projects.
• Can contribute to development of new knowledge and interpret results from various HTS applications

Skills

The PhD candidate can

• Plan a HTS research project and choose optimal sequencing platform
• Carry out the relevant bioinformatic analyses both on the command-line (unix) and R-studio, and utilize web-based resources like Galaxy server and Genbank E-utilities.
• Interpret the results of bioinformatics analysis of HTS (e.g. reliability, sensitivity and specificity) and judge their value for answering biological questions
• Disseminate the results of HTS based research

General competence

The PhD candidate can

• argue in favour of particular HTS technologies or bioinformatic approaches on the basis of current knowledge
• argue in favour of the kind of materials and the number of samples to select/include in different kinds of HTS projects
• can participate in discussions on HTS methodology

The course consists of three weeks consecutive work that includes the following teaching methods: self-study including exercises/questionaries related to background theory (one week), lectures and seminars (one week), and practical exercises in the use of different software programmes for analysis of HTS data (one week). The outcomes of the practical exercises in last week are discussed in plenary sessions.

It is required that students complete all the obligatory practical exercises .