Course code BUS350

BUS350 Introduction to Data Analytics

Norsk emneinformasjon

Search for other courses here

Showing course contents for the educational year 2022 - 2023 .

Course responsible: Erlend Dancke Sandorf, Dag Einar Sommervoll
ECTS credits: 5
Faculty: School of Economics and Business
Teaching language: EN
(NO=norsk, EN=Engelsk)
Limits of class size:
120 students
Teaching exam periods:
This course starts in the Autumn parallel. This course has teaching/evaluation in the Autumn parallel.
Course frequency: Annually. 
First time: Study year 2020-2021
Preferential right:
Preference is given to students at the School of Economics and Business and masters students. The course is open to bachelor students if the pre-requisites are met.
Course contents:

The course aims to facilitate data driven decision making by giving students fundamental knowledge about data gathering, data structures, analysis, and visualization. The course will cover the following topics:

  • Common data structures (flat and relational databases including basic SQL)
  • Data analysis tools and interfaces (R, RStudio and RMarkdown)
  • Data gathering, preparation and exploration ("data wrangling")
  • Data aggregation and feature engineering
  • Data and model visualization (ggplot)
  • Data modeling
Learning outcome:


  • Understand the properties of raw data structures and their implications for the use of data analysis techniques
  • Know common database structures and their implication for data management and data extraction
  • Know important techniques for data preparation, transformation, aggregation, and exploration ("data wrangling")
  • Understand how these pre-analysis choices affect analysis and interpretation of results


  • Have basic skills in R and be able to work with these in an appropriate interface (e.g., RStudio or RMarkdown)
  • Be able to take "messy" raw data and prepare it for analysis
  • Be able to perform basic feature engineering tasks, e.g., variable selection, transformation, and aggregation
  • Be able to create informative data analysis visualizations
  • Create reproducible reports using, e.g., RMarkdown

General competence:

  • Effectively communicate results using text, tables, and visualizations
  • Understand what compromises may be necessary in the data analysis process from raw data to discussion of results and how these may affect, or bias, the analysis
Learning activities:
Flipped classroom with online videos and resources available on Canvas, computer labs and independent work with weekly problem sets that can be submitted for additional feedback.
Teaching support:
In-person computer labs with teaching assistants. Computer labs will be primarily on campus, but one will be held online.
Syllabus: for Data Science by Hadley Wickham & Garrett Grolemund, which is available under a BY-NC-ND 3.0 License.Pre-recorded videos on CanvasAdditional links and reading materials will be provided on Canvas at the beginning of the semester
MATH100 Introductory Mathematics or ECN102 Introduction to Mathematics for Economists, or equivalent; and STAT100 Statistics or equivalent.
Recommended prerequisites:
Mandatory activity:
Portfolio assessment comprising 4 assignments. The course is graded pass/not passed.
Nominal workload:
125 hours. This is a work-intensive course.
Entrance requirements:
This course is open only for students in at the School of Economics and Business.
Type of course:
Two lecture hours per week (September to December). In addition, intensive work on exercises.
The course will be taught in English. Incoming students can contact student advisors at the School of Economics and Business ( for admission to the course. 
External examiner will control the quality of syllabus, questions for the final examination, and principles for the assessment of the examination answers.
Examination details: Portfolio: Passed / Not Passed