The major broadly covers the following areas:

• Programming skills for data handling and problem solving in business.
• Data analysis. Statistical learning and predictive analytics for learning and extrapolating from data.
• Business modelling and prescriptive analytics. Using optimisation models and simulations for decision support, analysis and improving business processes.

The compulsory courses provide you with knowledge and skills about the following core areas:

Programming (7.5 ECTS):

Either BAN400, Programming for Data Science or BAN401, Applied Programming and Data Analysis for Business.

We strongly recommend that you take one of these courses in the first semester. The skills are needed in later courses.

Predictive analytics (7.5 ECTS).

BAN404, Statistical Learning

Business modelling and prescriptive analytics (15 ECTS).

You will learn how to use optimization models for decision support (BAN402) and how to use simulation to analyze and improve businessprocesses (BAN403).

The compulsory courses are recommended to be taken during the first year of the master program.

The remaining 15 ECTS of the major must be chosen from the list of electives. The list of electives can broadly be divided into two categories. Methodological courses focus on analytical methods, although the methods are often illustrated and applied in specific business contexts. Application-oriented courses focus on understanding how value is created in businesses and how analytics can provide better decisions.

Electives - Business Applications

BAN425 Applied Risk Management (2,5 ECTS)

BAN427 Insurance Analytics (2,5 ECTS)

BAN434 Social and Economic Networks (7,5 ECTS)

BAN435 Blockchain Technology and Applications (2,5 ECTS)

BAN439 Detecting Fraud through Textual Analysis (2,5 ECTS)

BAN440 Data Driven Business Analysis (7,5 ECTS)

BAN442 From data to value: Machine Learning with Python (2,5 ECTS)

BAN443 Transforming Business with AI: The Power of Large Language Models (7,5 ECTS)

BUS401E Strategic Profitability Analysis (7,5 ECTS)

BUS401N Strategic Profitability Analysis (7,5 ECTS)

BUS403 Supply Chain Management (7,5 ECTS)

BUS429 Pricing Analytics and Revenue Management (7,5 ECTS)

BUS432 Operation Management (7,5 ECTS)

BUS465 Detecting Corporate Crime (7,5 ECTS)

BUS470 Retail Analytics (7,5 ECTS)

ECN425 Solving Economic and Social Problems with Big Data (7,5 ECTS)

ECN435 Data-Driven Public Policy (7,5 ECTS)

ENE431 Shipping Economics and Analytics (7,5 ECTS)

ENE434 Energy Industry Analytics-(7,5 ECTS)

FIE453 Big Data with Applications to Finance (7,5 ECTS)

FIE458 Deep Learning with Applications to Finance (7,5 ECTS)

MBM433 Customer Analytics in a Digital World (7,5 ECTS)

MBM436 Strategic Marketing Analytics for Digital Businesses (7,5 ECTS)

STR453 Digitalization-(7,5 stp)

Electives - Methodological Courses

BAN437 Uncertainty in Optimization (2,5 ECTS)

BAN423 Benchmarking with DEA, SFA, and R (2,5 ECTS)

BAN426 Applied Data Science (2,5 ECTS)

BAN430 Forecasting(7,5 ECTS)

BAN432 Applied Textual Data Analysis for Business and Finance (7,5 ECTS)

BAN436 Introduction to Python (2,5 ECTS)

BAN438 Application Development in Python (7,5 ECTS)

BAN441 Introduction to the use of spatial data (2,5 ECTS)

ECN430 Empirical Methods and Applications in Macroeconomics and Finance (7,5 ECTS)

ECO401 Optimisation and Microeconomic Theory (7,5 ECTS)

STR459 Artificial Intelligence and Robotics (7,5 ECTS)

Master Thesis

BANTHE Independent work/thesis in Business Analytics (30 ECTS)

Upon successful completion of the major, the candidates shall

KNOWLEDGE

K1: Have a solid knowledge about relevant methods from statistics and operations research

K2: Have in-depth and up-to-date knowledge on how to use data to support decision making in business

K3: Understand business analytics articles published in international scientific journals and formulate relevant research questions

SKILLS

S1: Build and solve decision models for real-world business problems

S2: Bring together decision modelling and data analysis for practical business applications

S3: Be proficient in computer programming in order to write code for business analytics problems and to communicate well with softwaredevelopers

GENERAL COMPETENCE

G1: Solve business analytics problems in teams with people from different backgrounds

G2: Reflect on the ethical challenges specific to business analytics

G3: Contribute innovative solutions for business analytics problems and communicate them to executives and other stakeholders

The compulsory courses cover this requirement.

You are required to take at least 7.5 ECTS in Ethics, Responsibility and Sustainablity (ERS). You can choose any course at NHH’s list of ERS courses. You can choose whether to fulfill the ERS requirement within your major, or as part of your minor, or as one of your elective courses.

None of the ERS courses are included in the BAN major, and you will need to fulfill this requirement as one of your elective courses or as part of a minor.

Read more about ERS courses

The courses ENE434 - Energy Industry Analytics and BUS401 - Strategic Profitability Analysis treat sustainability issues. ENE434 studies,among other things, how to analyze changes in market structure caused by changes towards low-carbon technologies and other changes inregulations. BUS401 includes insights from behavioral studies on the importance of accounting for sustainability in decision-making processes.

This major is suitable for those seeking jobs as analysts, either in companies or as consultants, and in general to all those seeking tocomplement their business background with analytical skills to succeed in the modern data-driven environment. A thorough understanding of business analytics will also enable you to communicate with technological experts and be useful for digital strategy managers.

Read more about career possibilities for NHH graduates

COURSES IN BUSINESS ANALYTICS (BAN) MINOR

To take Business Analytics (BAN) as a minor, you need 22.5 ECTS from the profile’s portfolio of courses.

AT LEAST ONE OF THESE

BAN400 R Programming for Data Science (7,5 ECTS)

BAN401 Applied Programming and Data Analysis for Business (7,5 ECTS)

BAN402 Decision Modelling in Business (7,5 ECTS)

BAN403 Simulation of Business Processes (7,5 ECTS)

BAN404 Predictive Analytics with R (7,5 ECTS)

ELECTIVES

BAN423 Benchmarking with DEA, SFA, and R (2,5 ECTS)

BAN424 Applications of Business Analytics (not offered) (2,5 ECTS)

BAN425 Applied Risk Management (2,5 ECTS)

BAN426 Applied Data Science (2,5 ECTS)

BAN427 Insurance Analytics (2,5 ECTS)

BAN430 Forecasting (7,5 ECTS)

BAN431 Econometrics and Statistical Programming (expired) (7,5 ECTS)

BAN432 Applied Textual Data Analysis for Business and Finance (7,5 ECTS)

BAN433 Applied Cloud Computing for Enterprises (not offered) (2,5 ECTS)

BAN434 Social and Economic Networks (7,5 ECTS)

BAN435 Blockchain Technology and Applications (2,5 ECTS)

BAN436 Introduction to Python (2,5 ECTS)

BAN437 Uncertainty in Optimization (2,5 ECTS)

BAN438 Application Development in Python (7,5 ECTS)

BAN441 Introduction to the use of spatial data (2,5 ECTS)

BAN442 From data to value: Machine Learning with Python (2,5 ECTS)

BAN443 Transforming Business with AI: The Power of Large Language Models (7.5 ECTS)

BUS429 Pricing Analytics and Revenue Management (7,5 ECTS)

ECN430 Empirical Methods and Applications in Macroeconomics and Finance (7,5 ECTS)

FIE453 Big Data with Applications to Finance (7,5 ECTS)

FIE458 Deep Learning with Applications to Finance (7,5 ECTS)

STR459 Artificial Intelligence and Robotics (7,5 ECTS)