ØKA2015 Applied Data Science

    • Number of credits
      7,5
    • Teaching semester
      2025 Autumn
    • Language of instruction
      English
    • Campus
      Lillehammer
    • Required prerequisite knowledge

      None (advised: Quantitative Methods).

Course content

The topics covered are:

  • Data acquisition and preprocessing (data cleansing and wrangling)
  • Descriptive statistics and data visualization: Tables, charts, data dashboards, advanced visualizations
  • Descriptive data mining: cluster analysis, association rules
  • Predictive data mining: linear regression, logistic regression, k-nearest neighbors
  • Web scraping and text as data
  • Artificial Intelligence and ChatGPT in data science

Learning Outcome

Upon passing the course, students have achieved the following learning outcomes:

Knowledge

Students

  • can describe the key steps in the data acquisition- and preprocessing process to make data ready for further analyses
  • can explain what insights businesses can obtain from various forms of descriptive statistics and data visualizations
  • can explain in own words how the algorithms performing cluster analysis and association rules work
  • can explain how multiple regression analysis works, including models for non-linear effects and interaction effects
  • can describe the key concepts of predictive data mining and explain how various techniques used for this purpose work
  • can explain what the key features of Artificial Intelligence are and its important areas of application in business and data science
  • can summarize key findings of some research articles where data science is applied in the business domain
Skills

Students

  • can acquire data from various sources and make them ready for further analysis by properly cleaning and wrangling them
  • can estimate various descriptive statistics and present them in tables, charts, and dashboards
  • can implement cluster analysis and association rules using relevant software and interpret the output
  • can perform multiple regression analysis, including models for non-linear effects and interaction effects and present and interpret the results of such analyses
  • can perform various forms of predictive data mining and present and interpret the results in an efficient manner
  • can convert text to numerical data that subsequently can be used in relevant data science tools
  • can apply tools based on Artificial Intelligence, such as ChatGPT, to solve various tasks within the field of data science
General competence

Students

  • can plan and implement an applied data science project in a business setting
  • can critically think about how data science can lead to sub-optimal or even wrong decisions if applied wrongly
Teaching and working methods

The following teaching methods are used:

  • Lectures
  • Exercise sessions
  • Tutorial videos
  • Case studies
  • Quizzes
Required coursework
  • Mandatory assignments must be handed in and passed in order to be allowed to take the exam. These will be combinations of practical and theoretical exercises covering key topics in the course.
  • Three out of four homework assignments must be passed to be allowed to take the exam.
Form of assessment

4-hour digital school exam. All aids allowed.

Assessments
Form of assessmentGrading scaleGroupingDuration of assessmentSupport materialsProportionComment
Written examination with invigilation
ECTS - A-F
4 Hour(s)
  • All
100

Reading list

Faculty
Inland School of Business and Social Sciences
Department
Department of Business Administration