Master in Data Science for Business BI Norwegian Business School

Overview

In this highly quantitative interdisciplinary Data Science for Business master's degree at BI Norwegian Business School , you will build a solid foundation in mathematics, advanced statistical modelling, machine learning, and optimisation. 

You will gain expertise in data engineering, big data analytics, and AI-based techniques and algorithms, all anchored in core business principles. With a keen understanding of data-driven decision-making and the importance of communication and domain knowledge, you will emerge with strong problem-solving skills, ready to tackle complex organisational challenges. 

You will learn to: 

• Use foundations in economics, finance, strategy and risk to identify business-value relevant challenges and solutions.  

• professionally apply machine-learning algorithms and AI, and explain their assumptions and limits. 

• programme in Python for tables, text and time series, and design efficient workflows that move projects into production. 

• evaluate quantitative risk for decision-making, reflect critically, and communicate results to technical and non-technical audiences. 

• integrate sustainability and data protection, addressing the ethical and legal aspects of applied data science in international contexts. 

Career Possibilities

Graduates with a MSc in Data Science for Business background are highly sought after by leading consultancies, financial institutions, technology firms, and global corporations. They often pursue roles as data scientists, analytics managers, or data strategists, leveraging their insights to drive innovation, improve operational efficiency, and sharpen competitive advantage. 

This programme’s graduates find exciting career paths in fast-growing fields, as well as opportunities to launch entrepreneurial ventures or contribute meaningful data-driven solutions in diverse business environments. 

Examples of areas you can work in:

• Data infrastructure and machine learning in production – build data pipelines, deploy models, and operate them. 

• Experiments and causal analysis – A/B tests and quasi-experiments to measure the impact of interventions. 

• Optimisation and operations research – capacity planning, pricing, and network design. 

• Quantitative finance and risk – time-series analysis, forecasting, and risk and portfolio modelling. 

Accreditation

BI Norwegian Business School is in the top 1% of business schools worldwide having achieved the Triple Crown of international business school accreditations.

Accredited by: EQUIS, AMBA, AACSB