Curriculum

• The structure of the programme

  The Master programme in Business and Data Science has a nominal duration of two years (120 ECTS) and consists of 90 ECTS of courses and a thesis of 30 ECTS.

  The programme is structured around two closely integrated cores: a Data Science core and a Business core. The first part of the programme focuses on mandatory core courses that build directly on the competencies acquired in the BEDS bachelor programme and ensure coherent and cumulative learning progression.

  Core courses

  Business core courses

  • Accounting & Profitability 1 & 2

  • Financial Economics 1 & 2

  • Marketing & Competitive Analytics 1 & 2

  Data science core courses

  • Data Science Technologies

  • Deep Learning and Artificial Intelligence

  • Advanced Econometrics and Scientific Methods

  • Corporate Social Responsibility in a Digital World

  Business core courses

  Accounting & Profitability 1 & 2

  These courses address how accounting information is used to understand performance and profitability in organisations. The courses cover financial statement analysis and advanced profitability analysis to identify where profits are created or lost. They also look at transfer pricing and how internal prices affect reported results and decisions. Students learn about asymmetric information inside organisations, and how this relates to compensation, incentives, and performance measurement. The courses introduce risk analysis, using Monte Carlo simulations, to analyse uncertainty in profits and financial outcomes.

  Financial Economics 1 & 2

  Financial Economics introduces students to the core analytical tools used in modern finance. The courses take a data-driven and analytical approach to key topics in corporate finance and investments. Students will learn to analyze financial problems quantitatively and to assess financial models using data. The courses draw on econometric techniques, optimization methods, and machine learning tools.

  Marketing & Competitive Analysis 1& 2

  Marketing & Competitive Analytics address how data and models are used to support marketing and pricing decisions. The courses cover fundamentals of pricing analytics and optimal pricing, with a focus on how prices affect demand, revenues, and competition. Students learn how to measure advertising effectiveness, analyse consumer behavior, and build demand models using customer and market data. The course also introduces customer analytics, experimentation (for instance A/B testing), and competitive analysis. More advanced topics include structural demand and supply models and counterfactual analysis, which are used to study market outcomes under different pricing or competitive scenarios. The overall goal is to enable students to use data and analytics to make better marketing and competitive decisions.

  Data Science core courses

  Data Science Technologies

  This applied course introduces students to essential and up-to-date tools that are central to modern analyst and data science roles. The goal is to equip students with skills needed to apply their knowledge in an industry setting from day one.

  Students learn to work efficiently with the UNIX command line and perform remote computing using virtual machines. The course also covers version control, automated testing, and adherence to coding standards, including the use of automation tools commonly found in professional workflows. In addition, students gain experience in interacting with APIs to access and integrate external data sources.

  A final component of the course focuses on “learning to learn”: developing best practices for staying current in a rapidly evolving technological landscape. This includes strategies for staying up-to-date regarding new tools, frameworks, and methods in the data science ecosystem.

  Deep Learning and Artificial Intelligence

  This course provides a thorough introduction to deep learning, with a focus on neural networks, key model variations, and practical implementation using state-of-the-art Python tools. Students develop a solid technical understanding of modern AI architectures rather than relying on point-and-click solutions.

  Building on this foundation, the course explores effective and innovative applications of AI in real business and data-driven contexts. Special attention is given to AI as a coding and productivity assistant, while acknowledging that the field evolves rapidly. The course connects naturally to earlier coursework and prepares students for advanced, responsible, and impactful use of AI technologies in professional settings.

  Advanced Econometrics and Scientific Methods

  This course equips students with advanced econometric tools for identifying causal relationships. The course begins with a brief discussion of the experimental ideal and the logic of causal inference, before turning to modern quasi-experimental approaches. The course provides in-depth coverage of difference-in-differences, instrumental variables, regression discontinuity designs, panel-data techniques, and related identification strategies. Throughout, emphasis is placed on understanding the assumptions underlying each method, diagnosing threats to validity, and implementing these approaches using real-world economic data.

  The course also develops students’ ability to design and execute independent empirical research. Students learn to formulate precise and well-motivated research questions, derive testable hypotheses from economic theory, and construct empirical strategies that align with their identification goals. The course further addresses issues such as data quality, measurement, replicability, and transparent reporting. By integrating econometric rigor with the principles of scientific inquiry, this course prepares students to critically evaluate empirical evidence and to produce high-quality economic analyses.

  Corporate Social Responsibility in a Digital World

  Corporate social responsibility (CSR) is about how companies behave ethically, create value for society, and take responsibility for the impact of their actions. In today’s digital world, this responsibility also includes how organizations use data and artificial intelligence in a fair, transparent, and safe way.

  In this course, students learn the key ideas of CSR and how they apply to modern digital business practices. They are introduced to the main principles of data protection and privacy. A central part of the course focuses on responsible AI usage, covering topics like algorithmic bias, fairness, and transparency.

  Within the business core, candidates are required to complete the first course in each of the three areas. They may then choose to complete two of the three second‑level courses. These courses build on the introductory courses and allow students to specialise in the areas of their choice.

  Electives

  Students who do not go on exchange in the third semester must choose their electives from a curated portfolio of courses that allow further specialization within analytics, economics, finance, and industry-specific applications.

  The current list (subject to change):

  • BAN402 – Decision Modelling in Business

  • BAN403 – Simulation of Business Processes

  • BAN430 – Forecasting

  • BAN432 – Applied Textual Data Analysis

  • BAN434 – Social & Economic Networks

  • BUS403 – Supply Chain Management

  • ECO423 – Advanced Derivatives

  • ECN425 – Solving Economic and Social Problems with Big Data

  • ENE431 – Shipping Economics and Analytics

  • ENE432 – Energy Industry Analytics

  • FIE425 – Derivatives and Risk Management

  • FIE453 – Big Data in Finance

  • ENGAGE.EU – Digital Transformation programme

  • ENGAGE.EU – Global Sustainability Management

  Master thesis

  The master thesis is conducted over the third and fourth semesters. Students begin working on the thesis during the third semester by developing and presenting a thesis proposal to their assigned supervisor, corresponding to a workload of 7.5 ECTS. The main work on the thesis in the fourth semester corresponds to 22.5 ECTS.

• Learning outcome

  Upon completion of the programme…

  Knowledge

  … graduates will have:

  • • Advanced knowledge in key methods and tools used to design and implement solutions in businesses.
    • In-depth knowledge of quantitative methods and data science
    • Advanced knowledge of data architectures, database systems, cloud-based platforms, and data governance principles relevant to large-scale organizational analytics.
    • Knowledge of programming concepts, data infrastructures, and AI tools.
    • A holistic perspective by understanding how analytics creates value across core business functions.

  Skills

  … graduates will be able to:

  • • Apply theories and analytical methods from business and data science to analyse complex problems in firms, industries and markets.
    • Use quantitative tools to collect, process, model and interpret data for managerial insight and decision support.
    • Develop and implement computational solutions, including programming, data pipelines, and machine-learning models.
    • Translate analytical results into clear, actionable recommendations for managerial and strategic contexts.
    • Work independently on advanced problems.

  General competence

  … graduates will:

  • • Understand the strategic and societal implications of digitalisation, data use and AI in business and public decision-making.
    • Recognise ethical, legal and sustainability considerations
    • Apply independent, analytical, and reflective judgment when evaluating complex business problems, managerial decisions, and strategic alternatives under uncertainty.
    • Communicate advanced analyses and professional advice clearly to specialist and non-specialist audiences.
    • Collaborate effectively in diverse teams involving business, technology and analytical expertise.

  • • Collaborate effectively in diverse teams involving business, technology and analytical expertise.

• International opportunities

  In the third semester, students may go on exchange to one of NHH’s partner institutions, or participate in NHH’s offerings through ENGAGE.EU.

• Admission requirements

  Academic requirements for admission:

  • Norwegian bachelor's degree, or a degree comparable to a Norwegian bachelor's degree
  • At least 80 ECTS credits in subjects relevant to the master’s programme, including:
  • Mathematics: minimum 7.5 ECTS credits (must include calculus, optimization and linear algebra)
  • Statistics: minimum 7.5 ECTS credits
  • Programming: minimum 7.5 ECTS credits
  • Accounting, finance, economics, marketing, ethics or sustainability: minimum 15 ECTS credits
  • The remaining 42.5 ECTS credits may be from any of the fields listed above, as well as artificial intelligence, automation or computer science
  • A maximum of 7.5 ECTS credits may be from ethics or sustainability