The course is connected to the following study programs

  • Master's Programme in Information Systems

Teaching language

English

Recommended prerequisites

Bachelor level course in Applied Data Science.

Course contents

The course builds on concepts and techniques from multiple fields including business, management, economics, sociology and computer science. The students will be able to have a broad perspective on real life problems, view a challenge considering different perspectives and see how different pieces fit together leading them to propose, design or develop data-driven solutions. Throughout the course, we will be using powerful Business Intelligence (BI) platforms (e.g.,Tableau software environment) and platforms for AutoML (DataRobot).

You will learn statistical concepts and data analysis techniques along with skills on BI platforms. You can better retain knowledge of a tool and how it works when you link it with a specific problem. The students will be asked, without being mandatory, to link their in-class assignments with actual cases from their own experience (e.g., an existing dataset or a business problem). Focus will be given on finding the right problem to solve while fostering the ideation of creative solutions on existing problems using existing datasets. The course gives students a systematic basis for addressing change in the digital business and bridging digital transformation with digital sustainability for shared value that impacts society. We will be discussing big data analytics ecosystems and strategies for digital transformation as paths to business and societal change.

Learning outcomes

Upon completion of the course, students should:

  • have knowledge about statistical concepts such as probability and modelling and how to apply them in practice

  • be able to promote data-analytic thinking and explain how to extract knowledge from different types of big data

  • be able to identify the potential for creating business and societal value out of big data

  • be able to discuss why and how the change in the digital era and data availability can transform business and society

  • have thorough knowledge of how big data and analytics can foster successful digital transformations

  • be able to use advanced business intelligence platforms including data visualization, communication, forecasting, and prediction

  • be able to implement machine learning algorithms (AutoML)

  • have in-depth knowledge of fundamental data science concepts through motivating realworld case studies

  • be able to evaluate and assess business problems, propose, and develop data-driven business models, strategies, and solutions

  • have achieved a common understanding that will lead to more efficient communication between management, technical/development, and data science teams

Examination requirements

Mandatory assignments must be passed. Participation in class discussions is required in order to complete and obtain certain mandatory assignments. Information about these terms is described in Canvas.  

Teaching methods

The teaching consists of a combination of lectures, group work and project assignments. There is a compulsory attendance in parts of the teaching, this will be specified at the start of the semester.  

Expected working hours are 270 hours.

Evaluation

The person responsible for the course, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.  

Admission for external candidates

No

Assessment methods and criteria

Group project report. Gradert karakter, A-F

Last updated from FS (Common Student System) July 1, 2024 1:53:20 AM