TECH6 Data Science and Econometrics

• Læringsutbytte

  Upon completion of the course, the student can:

  Knowledge

  • interpret key differences between programming languages commonly used in data analytics (Python and R)
  • identify the assumptions needed to interpret estimates as relevant for policy and decision making
  • describe the central concepts and terminology of econometrics and machine learning
  • recognize the difference between causal and predictive analysis

  Skills

  • read, translate and write code in R and Python with help from AI tools
  • use R to import, store and manage data in different formats, conduct econometric analyses, build machine learning models and produce tables and figures
  • design simple experiments to gather data in business environments
  • assess the validity of causal claims, and to disentangle correlations and causality
  • present results of empirical analyses in a clear and convincing manner to various audiences

  General competence

  • provide data-driven answers to business-oriented questions
  • interpret and critically assess empirical work in business and economics
  • reflect on the ethical issues in collecting, storing and using data
  • demonstrate good background knowledge for more advanced courses in econometrics and data science taught at Master's level

• Undervisningsopplegg

  The course is a combination of lectures, practical computer sessions, assignments in groups, and presentations by the students. Videos explaining key concepts and calculations will also be available. The course will focus on practical applications and empirical examples of the various techniques presented.

  We put emphasis on research-based teaching by encouraging students to work on their own term papers. The students should also learn how to present the results of their own analyses, both in written form and in front of an audience.

• Anbefalte forkunnskaper

  TECH1-TECH4

• Dataverktøy

  R and RStudio

  Access to secure AI tools (eg KI-chat)