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Big Data and Smart Hospitality BHO321

The course aims to give the students the tools and understanding to analyze and utilize Big Data.

Course description for study year 2020-2021





Vekting (SP)


Semester undervisningsstart


Antall semestre






Tilbys av

Faculty of Social Sciences, Norwegian School of Hotel Management

Learning outcome

After completing this course, it is expected that the student will have the following knowledge, skills and general competence:


  • Have knowledge of how big data can be generated and used for decision making in the organization.
  • Have knowledge of Big Data tools (software; HMS, Food etc)
  • Have an understanding of how Big Data can increase profitability by allowing more effective planning (both long and short term).
  • Have knowledge and understanding of analysis challenges related to Big Data.


  • Be able to take part in the strategic work in the organization, such as develop complex analyses (based on structured and unstructured data) and different types of analysis (descriptive, diagnostic, predictive and normative).
  • Be able to use Big Data tools (software)
  • Have the ability to harvest, organize, interpret and visualize large data sets such as; Market situation (penetration)Business cycleReal time guest feedbackCategorization of target groupsGuest profilesGuest data and historyIdentify important guest preferences

General Competences

  • Have an understanding of the significance of integrating Big Data as a decision-making tools on a strategic level.
  • Have basic understanding of "SMART hosting" (management perspective)
  • Have basic understanding of how to use BIG DATA, this includes; collection, organizing and analysis of large data sets.
  • Be able to visualize and present Big Data
Today’s technology offers the opportunity to collect large amounts of data and most organizations do. However, knowledge of how to use this data in order to optimize the running of the business is often lacking. This course aims to give students the tools and understanding needed to utilize Big Data and share the insight in a way that makes the data accessible.
Required prerequisite knowledge
Eksamen / vurdering
Vurderingsform Vekting Varighet Karakter Hjelpemiddel
Written home exam 1/1 7 Days A - F All.

The home exam can be written individually or in groups with maximum 3 students.

Course teacher(s)
Course coordinator: Trude Furunes
Method of work
Lectures, groups work, discussions.
Course assessment
The course is assessed and evaluated according to the University of Stavanger's administrative quality system.
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