Examination arrangement

Course content

The course builds on concepts and techniques from multiple fields including business, management, economics, sociology, computer science, philosophy. The students will be able to have a broad perspective on real life problems, view a challenge as a whole taking into account 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 advanced data analytics platforms that support data cleaning, exploration, visualization, and predictions (e.g., Tableau, PowerBI, KNIME, DataRobot). You will learn about statistical concepts and data analysis techniques and how they can enable better decisions. You can better retain knowledge of a tool and how it works when you link it with a specific 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 as a whole. We will be discussing big data analytics ecosystems and strategies for digital transformation as paths to business and societal change. The latter will be connected with real world examples using case studies.

Learning outcome

After completing the course, the following overall learning outcomes should be achieved:

Knowledge

Students should:

• Acquire an understanding of data analytics and its fundamental principles by offering a high-level overview of concepts and principles.
• Understand how data analytics can foster successful digital transformations.

• Gain knowledge of fundamental data analytics and machine learning concepts through motivating real-world case studies.

Skills

Students should:

• Be able to analyze, visualize, and communicate findings from large datasets using state of the art platforms for improved predictions.
• Be able to evaluate and assess business problems, propose and develop data-driven business models, strategies, and solutions.

General competence

Students should:

• Be able to promote data-analytic thinking and explain how to extract knowledge from different types of data.
• Gain a common understanding that will lead to more efficient communication between management, technical/development, and data science teams.
• Be able to discuss why and how the change in the digital era and data availability can transform business and society.

Learning methods and activities

A mix of lectures and student-active learning with utilization of relevant ICT-tools. Group work including project based mandatory exercises/presentations.

Compulsory assignments

• Exercises

Further on evaluation

Compulsory exercises:

Participation in a minimum of 75% of the learning activities. The activities are announced at the start of the course. In special cases where 75% participation is not satisfied, the student can enter into an agreement with the course coordinator (emneansvarlig) about alternative learning activities. Group work including project based mandatory exercises/presentations which must be approved before the candidate get access to the final assessment.

ASSESSMENT:

Project report written in groups of up to 3 students. Differentiated grades may be applicable if the work effort within the group has been unevenly distributed.

Deferred assessment: March. If the students have a deferred assessment, new tasks will be defined. Deferred assessment can be changed to an oral exam.

Specific conditions

Recommended previous knowledge

Basic knowledge in digitization and digital transformation.

Required previous knowledge

The course is reserved for students admitted to the Master's Degree Program in Digital Transformation.

Course materials

Course literature is determined at the start of the course. Possible research articles and books can be:

• Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.". http://www.data-science-for-biz.com
• Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures & their Consequences. Sage, 208