Course code BUS350

BUS350 Introduction to Data Analytics

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Showing course contents for the educational year 2020 - 2021 .

Course responsible: Joachim Scholderer, Atle Guttorm Guttormsen
ECTS credits: 5
Faculty: School of Economics and Business
Teaching language: EN
(NO=norsk, EN=Engelsk)
Teaching exam periods:
This course starts in the Autumn parallel. This course has teaching/evaluation in the Autumn parallel.
Course frequency: Annually. Cancelled in 2020.
First time: Study year 2020-2021
Preferential right:
Course contents:

The aim of this course is to prepare participants for the "analytics-heavy" specialisation courses offered in our master's programmes in business administration, data science and entrepreneurship & innovation. The course will consist of six parts:

  • Typical data structures in business analytics, logistics and finance (event logs, time series, RFM data, relational data),
  • Typical database structures in business analytics, logistics and finance (relational databases, data warehouses, data lakes),
  • Analytics platforms (Python, R, SAS),
  • Important data transformations (log, logit, probit),
  • Data aggregation and feature engineering (raw data aggregation with SQL, low-rank approximation with PCA, clustering),
  • Business intelligence and data visualisation platforms (Power BI, Tableau).
Learning outcome:


  • Understand the properties of raw data structures and their implications for the use of analytics techniques,
  • Know common database structures and understand their implications for data management and data extraction,
  • Know important data aggregation and data transformation techniques,
  • Understand their effect on levels of measurement and their implications for the interpretation of estimation results and predictions.


  • Be able to use general-purpose analytics platforms such as Python, R and SAS,
  • Be able to use basic SQL queries for data extraction and aggregation,
  • Be able to perform basic feature engineering tasks (transformations, PCA and clustering) with time series data, aggregated event log data and panel data,
  • Be able to develop dashboards using business intelligence and visualisation platfroms such as Power BI and Tableau. 

General competence:

  • Understand the importance of analytics and information systems in modern, data-driven businesses,
  • Become able to independently develop further analytics skills by progressing from a broad basic foundation. 
Learning activities:
Lectures, flipped classroom activities, independent work on exercises.
Teaching support:
Canvas, flipped-classroom activities.
Detailed readings will be announced on the Canvas page of the course in the beginning of the semester.
MATH100 Introductory mathematics or ECN102 Introduction to mathematics for economists; STAT100 Statistics 
Recommended prerequisites:
INF120 Programming and data processing
Mandatory activity:
Completion of four mandatory assignments and participation in two one-hour multiple choice tests.
Continuous exam, consisting of four mandatory assignments (weight: 15% each) and two one-hour multiple choice tests (weight: 20% each).
Nominal workload:
150 hours. This is a work-intensive course.
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: :