Business Analytics as a Strategic Tool

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. Also, you will be using Generative AI tools (e.g., chatGPT, Copilot) in developing different parts of the assignments (e.g., brainstorming, business understanding, analysis). 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.

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.

• Expand on how generative AI technologies can drive digital transformations by automating tasks and generating insights, working with real-world examples of generative AI-driven transformations in various industries.

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.
• Leverage generative AI to simulate business scenarios, predict outcomes, or create new data solutions tailored to business needs.

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.

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