Administrator
Level
Bachelor
Topics
Natural Sciences / Computer science / Game development
Language
English
Delivery method
In person
University
University of Inland Norway
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Applied Data Science

This course gives students the knowledge to explain what insights businesses can obtain from various forms of descriptive statistics and data visualizations, as well as explain the key features of Artificial Intelligence and its significant areas of application in business and data science, and more.

This is a 5th-semester course of the 6-semester Bachelor's Degree in Business Administration (Norwegian degree). This course has unlimited spots for exchange students.

Three out of four homework assignments must be passed to be allowed to take the exam. Graded A-F, where E is the minimum for passing the exam.

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

Recommended:

  • Students should have completed a quantitative methods course.

Mandatory:

This single course is only available to exchange students from our partner institutions. To find out whether you are eligible for an exchange study at the University of Inland Norway, contact your home institution. All exchange students must be nominated by their home institution before they can apply.

Information on the application process and the latest semester information will be sent by email to all nominated students. You will apply for relevant courses as part of your exchange application. Please review the application guidelines for preparing the required documents and applying for the exchange.

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

Free for exchange students.