• Application deadline

    Application closed for academic year 2023/2024

    Application for the September 2024 intake will open 1 October. This is a capped programme. To find out more, please review the admission & application information below.

  • Starting date

    August 2023

  • Format

    Full-time | 12 months

  • Credits


  • Fee

    €2,314 (EEA) | €21,500 (non-EEA)

    Find more information below

  • Language


Even ‘traditional’ sectors of business have become data-rich – and such businesses need people who can find the business benefits in digital applications and technologies. Think of supermarkets with their abundance of data, or logistics and its dependence on routing and timing information.
This specialisation in our one-year (12 month) full-time MScBA study programme teaches you to understand, solve and communicate operational, tactical and strategic challenges from data in organisations. What these challenges have in common is their complexity. They need advanced analytics – but the results can be extremely valuable for business. You will find the causal relationships when you tackle real-life business problems in the business world.
After completing your core courses, you can choose from electives such as Analysing Digital Footprints, Supply Chain Analytics or Fintech: Business Models and Applications, before writing your thesis.


  • 54% international MSc students at RSM
  • 97% of MSc graduates employed within 3 months after graduation
  • Examples of your future job title: Marketing analyst, Data analyst, Financial analyst, Supply chain analyst


You learn through hands-on workshops and in-depth training to detect patterns and trends. Your instructors each have their own expertise in particular business and management domains and will train you to apply your analytical skills in each domain. This specialisation of our MScBA programme also benefits from the ‘intellectual engine’ that is the Erasmus Centre for Data Analytics at RSM; its network of companies and the on-campus Data Lab provide a rich environment for you to develop your skills and knowledge.

During this programme specialisation, you will:

Business models

Design business models that exploit technological superiority

Communicate recommendations

Learn how to effectively communicate recommendations from a business analytics project

Deal with ethical dilemmas

Discover how to deal with ethical dilemmas in data science

Positive changes

Contribute to positive changes in business

More about the programme


The RSM MSc in Business Analytics & Management specialisation is one academic year’s duration. Core courses are compulsory and will be offered during the autumn semester (20 EC). Master electives are offered in Block 2 of the autumn semester (6 EC) and in the spring semester (10 EC). The Business Analytics Workshop (6 EC) takes starts in January. During the year, students work on a master thesis project (18 EC)..

Please note that certain electives may be very popular. Although we can place most students in the elective(s) of their choice, there are no guaranteed places.

Professor Robert Rooderkerk

A teacher's story

  View all core courses below:

The advanced statistics course is about acquiring a robust understanding of advanced statistical methods and techniques for business analytics, in terms of both foundation and application. In addition, the course provides training in advanced programming skills, specifically using the statistical software R.  

In particular, the following topics will be covered: 

  • Linear and general regression models 

  • Least-squares estimation, maximum likelihood and bootstrapping 

  • Statistical inference, estimation and hypothesis testing 

  • Binary and multinomial choice modeling 

  • Functional forms, nonlinear models 

  • Models for panel data 

  • Model selection, heteroskedasticity 

Taught by Dr J. van Dalen & Dr D. Gutt.

Data is a key business resource. Web stores such as Amazon or Coolblue use customer data to dispatch purchased products and to make recommendations of new products; banking service providers such as ING or HSBC use data for processing cash and ATM transactions and for assessing a client’s credit worthiness; manufacturers such as BMW and Tesla use data to design and produce cars and to propel self-driving cars. Often, this data is spread within the organization through multiple information systems. In order for organizations to operate successfully, they need to be able to share data. Data management is the field dedicated to enabling the sharing of data such that it is available at the right time, place, user and quality. This course introduces data management topics such as database design, data preparation, manipulation, modeling, alongside with discussions on ethical frameworks that should guide the management of data and data privacy issues.  

In addition, the course provides an introduction to blockchain, which is a new and atypical way of organizing data. This type of data solution arose in the wake of the global financial crisis of 2007-2009 with the introduction of Bitcoin, the first cryptocurrency. Ever since, there has been an enormous growth in different types of blockchain based business solutions, some of them meaningful, some of them not. We discuss the basic design, logic and economic incentives underlying the blockchain technology, and discuss real-life applications, hypes and scandals. 

Taught by Dr A. Priante & Dr D. Bongaerts.

Course content is organized into seven modules covering the following topics:

  • Introduction to Causal Inference—Overview of the course, motivating questions about causes and effects, and the challenges of measuring effects from data
  • Potential Outcomes Model—A framework for measuring causal effects from observational data or experiments that begins with what you want to measure and works backward from that
  • Graphical Models—Another framework for measuring causal effects from observational data or experiments that begins with what you are willing to assume about the world (and what you can measure) and works forward from that
  • Randomized Experiments and Hypothesis Tests—Sampling from a population and random assignment to different treatments are powerful tools for measuring causal effects. Hypothesis tests provide a way to quantify how likely our results are due to these sources of randomness.
  • Confounders and heterogeneity—The world is messy. Variables that are correlated with causes and effects can help or hinder our ability to measure causal effects for the entire population or for subsets of the population
  • Matching and Regression Estimators—Introduction to typical methods for estimating causal effects
  • Challenges to Causal Inference—If we send an email to a customer, they may or may not use that coupon on their next purchase. Here we discuss tools for inference when some people don’t do what we expect them to do.

Taught by Dr J. Roos.

The content is organized into 6 modules (the fourth one over 2 weeks), covering the following  

  1. Introduction to quantitative decision making via Decision Trees ( Introduction to mathematical modelling, strengths and limitations, time structure of decisions, handling uncertainty). Introduction to Python. 
  2. Linear Programming (Formulating and solving Linear Programs. The graphical method. Solving LP’s using Gurobi.) 
  3. Sensitivity Analysis (Analyzing sensitivity of LP solutions with respect to data.) 
  4. Integer Programming (Modeling discrete decisions. Formulating Integer programs, understanding why IP’s are harder to solve, modelling with binary variables) 
  5. Incorporating Uncertainty in Linear Programming (2-stage stochastic programming, deterministic equivalent, non-anticipativity, connection with decision trees)  
  6. Monte Carlo Simulation (Understanding the flexibility of simulation, creating and running simulation models in Python) 

Through the course, we will apply the learned tools to areas such as operations and transportation logistics.  

There will be five in-class lectures (1 x 2hr30m and 4 x 1hr15m) and five online workshops of 2hr45m each.

Taught by Dr A. Tsoukalas

Machine learning is a key driver of the success of data-driven decision-making in business. It enables practitioners to learn from experience, as encoded in data, to understand business-relevant processes or to predict the future. It is an essential tool in the modern data scientist's toolbox. 

This course provides a hands-on introduction to the concepts, principles and key algorithms for supervised and unsupervised machine learning. You will learn how these algorithms work and practice applying them to solve business problems. Attention will also be paid to principles, concepts and best practices which are required to apply these methods judiciously in practice, such as generalization, overfitting, resampling methods and evaluation metrics. The supervised and unsupervised machine learning algorithms covered will include decision trees, random forests, neural networks, cluster analysis and gradient boosting machines.  

Machine Learning & Learning Algorithms is a core course in the MScBA Business Analytics & Management programme. It builds on knowledge obtained in the pre-modules and other core courses, with specific emphasis on statistics and programming. It provides knowledge and skills which will be leveraged to varying degrees by the elective courses. 

Taught by Dr P. Schoonees.

The Business Analytics Workshop course involves completing a data analytics project for a company or organization. Together with the partner organization, teams of students formulate research questions, obtain and analyze data, and interpret and present their results.

Students are guided through the project by faculty supervisors. Guidance provided to students focuses on two domains: general skills and knowledge, and specific feedback on the project they conduct. Guidance related to general knowledge and skills focuses on project management, choice and application of research methods, and visual and oral presentation of results. In addition, students receive continuous feedback on their project progress from their peers as well as from supervisors.

Before the start of the course, students must indicate their preference for one of three tracks: (1) marketing, (2) supply chain management & business information management, or (3) accounting & finance. Based on these preferences, faculty supervisors form teams and allocate the teams to projects ahead of the course start.

Taught by Dr M. MargolinDr A. PrianteDr M. Szymanowski & Dr L. Veelenturf

The aim of ‘Your Future Career’ is to prepare RSM students at an early stage in their master's for their careers.

The online modules will help you make crucial steps towards the most suitable career step, whether an internship or a job.

To pass the course, you need to gain a minimum number of points within a few months. You can decide if you want to reflect on your interests and motivations, develop knowledge of the job market, receive peer feedback on application materials, learn to love networking, or attend an interactive alumni career panel or workshop.

See this page for more details.


This course is overseen and guided by Dr Maciej Szymanowski and Lisanne Keir


All courses of this track are listed below:

In the last decades, the unprecedent growth of digital technologies has led to a rapid decline in the cost of storage, computation, and transmission of data. It deeply impacts firm internal business processes and interactions with other businesses, consumers, and policy makers. The aim of this course is to learn how digitization transforms production and supply chains, and leads to new products, services and business models, including platform businesses, such as Bol.com, Uber or Airbnb. 

We will discuss changes brought in by digital technologies at the three levels: digitization, digitalization, and digital transformation. First, at the level of digitization we will see how the rise of new information & communication technologies affects the economy. At the level of digitalization, we will analyze how firms adopting these new technologies can benefit by making production processes and supply chains more effective. We will see how firms can transform their business models and discover new sources of value they can offer to their customers. We will particularly discuss digital platforms, focusing on their sources of value creation and the challenges they face. 

Digitalization helps firms to use data generated by digitized processes as a new source of innovation and competitive advantage. At the level of digital transformation, we will focus on how companies can profit from innovating in the digital world and how data can support data-driven decision making. 

In the second part of this course, we will look at specific challenges of digitalization for supply chain management and how they can be solved, based on practical examples. We will also learn how digital technologies revolutionize the customer journey through omnichannel distribution models. 

Taught by Dr O. Slivko and E. Haag.

Marketing is the interface between a firm and its environment. It is the managerial practices that ensure customer-oriented managerial actions, including product design, pricing, market communication and product distribution, generate value for “customers”. The basic tenet is that managers should be able to justify that intended value-creation actions lead to sufficient customer responses such that they surpass the costs of these actions. However, in practice, achieving such goal is challenging, because of the sheer number of uncontrollable factors that influence customer responses. To address these challenges, academics and practitioners have developed and adopted a selection of analytical tools in the form of “marketing models”, which are crystallized from disciplines such as economics, statistics and computer science. 

This course focuses on 1) how to translate managerial problems into analytical problems, and 2) how to solve these analytical problems with marketing models. In paricular, the course covers important marketing problems, such as how to successfully design and diffuse a new product, how to price a product or service to gain short-term profits and long-term equity, and how to build and manage heterogeneous and dynamic customer portfolios. The course adopts a hands-on approach, where you are expected to work on cases with actual data to solve real managerial problems. Overall, the course will show you how to understand, predict and influence customers using marketing models.

Taught by Dr X. Chen.

This course lays the groundwork for financial modeling in Market-and Bank-Based Financial Markets. As such, the course is a building block for later the electives FinTech and Algorithms in control. We start by discussing sources of company financial information, how different financial statements are connected, and procedures for financial statements forecasting, accuracy assessments, and valuation. In particular, we discuss how data analytics can help in this process.  

The results of such analyses are important for investors who use them in assessing the risk and return of investments in corporate credit and equity markets. In each of those two markets we derive a standard optimization representation both for individual investments as well as for portfolios. We link these representations to real-world applications such as platform (P2P) lending, asset management, robo-advising, and high-frequency trading. 

Taught by Dr D. Bongaerts & Prof Dr E. Peek.

All courses of this track are listed below:

The emergence of information and data technologies, together with the sophistication of tools in econometrics and machine learning, have led to a radical shift in the way marketing operations are run. The focus of companies has been shifted away from product-centric approaches and mass marketing campaigns to customer-centric campaigns tailored to the needs and wants of each individual customer. Such campaigns target a well-chosen subset of customers, at a well-chosen time, and with a well-chosen incentive. At their core, they require strong data analytics tools in order to be able to predict each customer’s behavior and derive optimal marketing interventions.

This elective will provide students with the necessary knowledge and skills to tackle these challenges. The course will focus as much on the managerial questions as well as on the use of methodologies to address these questions. The elective will be articulated around the core metric of Customer Lifetime Value (CLV). We will especially focus on customer-centric tactics firms can use to:

  • Enhance customer acquisition (e.g. customer referral programs, seeding strategies)
  • Boost customer spending (e.g. loyalty schemes, customer engagement)
  • Prevent customer churn (e.g. proactive retention programs).

To address these challenges, we will use state-of-the-art machine learning methods, including boosting, uplift models, decision trees, etc. Finally, we use rely heavily on the notion of A/B testing and randomized control trials in order to optimize personalized interventions of firms.

Taught by Dr A. Lemmens.

“Bad” algorithms abound and can lead to lost revenue, discrimination, disinformation, or even bodily harm. New European regulations on Artificial Intelligence force organizations to control the risks introduced by algorithms. But how can one be in control when it comes to algorithms?

The role of algorithms in organizations is twofold: on the one hand algorithms are used to control organizations, yet on the other hand algorithms themselves need to be controlled by organizations. This elective considers both perspectives. Firstly, to achieve their organizational goals, that is, to “be in control”, organizations use information from algorithms. To understand the use of algorithms for control purposes, this course will zoom in on control areas where information from algorithms is used such as personnel selection, performance management, and feedback and reporting. Secondly, to be in control, organizations also require information about algorithms. To understand how organizations aim to gain control over algorithms, this course will zoom in on risk areas of algorithms such as bias, opacity, and lack of accountability.

In terms of hands-on application, this course will focus on a domain where mitigating the unintended consequences of algorithms has received a lot of attention: text classification.

Taught by Dr I. Sandu.

Business analytics is becoming a key driver of competitive advantage. Only firms that can harness their data and develop strong analytic capabilities across all their business functions will be able to survive in fast-moving modern markets. In this course, students will learn how to use Python to manage and analyse large data sets, and present and communicate the results of their analyses in a manner that makes them actionable and understandable to business managers.

Taught by Dr P. Cornelius.

Recent years have witnessed dramatic changes in the ways businesses manage their supply chain operations, engendering a fundamental reliance on massive data from varied sources and advanced business analytics. New businesses have emerged with an explicit data-centric approach to supply chain operations, e.g. Amazon, Coolblue, Booking.com, while traditional suppliers (and other organizations) are heavily investing in transforming towards data-supported supply chain operations. Data about transactions and processes are central to forecasting product demand, to predict arrival times, to identify anomalies and structural changes, to monitor customer behavior or to estimate utilization rates. The analytics outcomes support operational, tactical, and strategic decision making throughout the supply chain: assortment planning, inventory management, workforce planning, procurement and production decisions, risk assessment, pricing, and the planning of logistics resources, routes or terminal capacity. Supply chain analytics is at the heart of the modern business.

This elective will introduce modern forecasting and machine learning methods to predict supply chain outcomes. Also, it will present methods to determine optimal prices, routes and inventory levels. All discussed methods and techniques will be embedded in typical supply chain topics.

Taught by Dr M. Tekin.

Consumers use various technologies in their everyday life, such as mobile applications, internet browsers, social networks, and wearables. In doing so, consumers leave a digital footprint - a stream of data that describes their online activities. Sometimes, they explicitly share content with other people (e.g. social networks, forums) or have direct communication with a company (e.g. online customer service). Data that consumers intentionally submit online is defined as active digital footprint. At the same time, consumers also leave a passive digital footprint: their search history, the news they read online, the location and time when they use a device. Both active and passive digital footprints can reveal consumer attitudes, interests, and preferences. This brings numerous opportunities and challenges for companies, consumers, and public policy makers.

This course is suitable for students with a solid understanding of fundamental programming principles who want to learn advanced scientific computing practices (e.g., collecting data using APIs, version control systems). As a core component of the course assessment, students will work through the steps of a data science research project (e.g., study design, data collection, data processing, data analysis). The lectures and workshops will cover data collection and data processing with R and RStudio. It is assumed that students already have an in-depth understanding of study design and data analysis, from previous courses in the BAM program. If you are unsure about the level of your R programming experience, please contact the lecturer before signing up for this class.

Taught by Dr A. Martinovici.

The financial sector has been increasingly disrupted by the use of technology. Moreover, recent regulations such as Mifid I&II and the EU PSD2 directive, have created an environment that further facilitates the use of technology, big data, and algorithms in financing applications. One can think about payment systems, Peer to Peer Lending, Crowdfunding, Crypto-currencies, Robo Advising, and High-Frequency Trading. 

The course consists of two parts that run sequentially. In the first part, we discuss the FinTech business models and strategies, their value propositions, their impact on traditional players in the financial sector, and their impact on market outcomes. During the first part, students start to work on a business plan for a FinTech Venture.

In the second part of the course, we apply the knowledge gathered thus far in this course and the core courses by implementing a stylized version of the key technology (minimum viable product or MVP) for the FinTech venture embarked on in the first part under the supervision of an instructor with ample experience in FinTech bootcamps and hackathons.

Taught by Dr D. Bongaerts.

Note regarding taking courses if you are not an RSM master student: RSM does not offer the possibility for non-RSM students (master or otherwise) to take RSM MSc courses outside of official exchange partnerships or other inter-faculty agreements. If you are interested in learning more about corporate social responsibility, sustainability, or business ethics, please refer to our Open Programmes section.

For more information on all international opportunities offered at RSM, visit the website of our International Office.

Learn more


International exchange


Career training

The aim of the course ‘Your Future Career’ is to prepare RSM students at an early stage in their master's for their careers.

The online modules will help you make crucial steps towards the most suitable career step, whether an internship or a job.

To pass the course, you need to gain a minimum number of points within a few months. You can decide if you want to reflect on your interests and motivations, develop knowledge of the job market, receive peer feedback on application materials, learn to love networking, or attend an interactive alumni career panel or workshop.

See this page for more details.


This course is overseen and guided by Dr Maciej Szymanowski and Lisanne Keir



Career Opportunities

Business Analytics & Management is a new specialisation of our MScBA designed to fulfil the growing business demand for graduates with strategic business and management skills who can create business benefits using advanced analytics. RSM master graduates are individuals who all combine intellectual curiosity with practical skills geared towards creating positive change in business. In our most recent survey, 92.4 per cent of all RSM graduates find a job within six months of graduating – in many cases before they graduate. Students choosing this master programme may consider career paths as:

  • Supply chain analyst
  • Marketing analyst
  • Data scientist
  • Business analyst
  • Financial modeller
  • Financial analyst
  • Customer journey specialist
  • Omni-channel specialist
  • Assortment and pricing specialist

Non-EEA nationals who have earned a diploma from a higher education institute in the Netherlands can apply for a special residence permit called the orientation year after completing their studies. The 'Orientation Year for Graduates Seeking Employment' is a residence permit aimed at retaining foreign talent for the Dutch labour market. During this orientation year you are free to work without a work permit. Participants who find a job during this period can change their orientation year into a residence permit for Highly Skilled Migrants under more favourable terms.

For the most up-to-date information please visit the following website.

Good to know

Career Centre

Alumni networks

MSc employment report

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Studying at RSM

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The RSM Experience

Education for life

Studying at RSM will be a life-changing experience. Your master degree will prepare you for a fulfilling professional life as a capable, self-assured individual. It will make you valuable to business and attractive to employers because it teaches you skills that make the most of your innovative mind. You will be challenged in and outside of the classroom, and you will gain an education based on the latest developments in business. Your master degree from RSM will include RSM’s promise of life-long learning, and membership of the more than 40,000-strong alumni network that is present in more than 110 countries which hosts activities and events all over the world.

Open intellectual culture

Your education at RSM is valuable. You will learn from academics who produce the highest quality research and the most innovative management thinking. In the classroom, sharing and questioning opinions is encouraged – yours and those of your fellow students, as well as the professors’. Many of RSM’s faculty members are young and passionate professors and researchers with outstanding academic credentials. Their work is published in top international management journals.

Engaging environment

Professors’ doors are always open for students who have questions, projects or ideas. Depending on the study programme, students have different opportunities to tailor their programme. This can, for example, take the form of a minors course, an internship, an exchange at one of over 160 partner schools worldwide, elective choices, the participation in a consulting project with a company or public sector organisation, or a thesis project in their specific area of interest. RSM’s strong links with local and international businesses and organisations offer opportunities for practical projects and real-life collaborations.

What is your ‘I WILL’?

RSM’s I WILL movement allows you to define your goals, your ambition, your drive. It’s our forward-thinking community that asks you to say something about your future. Your I WILL statement becomes part of the spirit of RSM’s diverse community of students, researchers, staff, professors, alumni and others related to the school. Making a public commitment to your goal will allow you to achieve it faster and better. What is your goal?

Rotterdam, a future-oriented city

Living and studying in Rotterdam has never been better. Rotterdam is home to one of the largest and busiest ports in the world and many multinational companies have their headquarters here. The city is famous for its stunning modern architecture, such as the Centraal Station or its covered food market, the Markthal. At the same time, the city authorities are forward-thinking in improving its liveability. There’s no shortage of restaurants, museums and theatres, yet Rotterdam is still an extremely student-friendly city with plenty of affordable student housing, and a bustling nightlife that includes events organised by students associations.

Find out more about life in the city of Rotterdam.

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Fees & Scholarships

The combination of affordable tuition fees and living costs together with quality education and an excellent global reputation make a Masters degree at RSM a clever investment.

Tuition fees 

The 2023-2024 tuition fee for the MSc programmes is approximately €21,500 for non-EEA students. The Dutch government contributes towards this cost for students who hold a nationality from a country belonging to the European Economic Area(EEA). These students therefore only pay the statutory fee of €2,314 in 2023/2024.

For EEA nationals who have already completed a master in the Netherlands (and obtained the diploma) the tuition fee for a 2nd master is approximately €12,600.

The MSc International Management - CEMS (18 months) is a longer programme, for which the tuition fee will have to be paid for the duration of the programme. The expected tuition fee for the 18-month MSc International Management - CEMS programme is approximately €32,250 for non-EEA students and is approximately €3,471 for EEA students in 2023/2024.

Please note that all these tuition fee tariffs are subject to change.


The number of scholarships is limited and mainly merit based. If a scholarship covers only the tuition fees, be aware that you need to finance your own living expenses (rent, food and insurances) for the duration of your studies. RSM does not offer scholarships for the pre-master programme. We do however offer a maximum of 2 scholarships per academic year to RSM pre-master students enrolling in an MSc programme.

Rotterdam School of Management, Erasmus University (RSM) offers multiple scholarships to prospective students from non-EEA countries who are not entitled to pay the EEA tuition fee, provided their grades are considered ‘excellent’. RSM also offers one scholarship, the Erasmus Trustfonds Scholarship, to students from EEA countries. 

Besides scholarships awarded by RSM, there are also scholarships awarded by the Dutch government or other organisations that are available if you meet certain criteria such as nationality, age, etc We have listed some of them below but we encourage you to use resources such as Grantfinder or the Scholarship Portal to find additional scholarships.

Scholarship tips

  • Contact the Ministry for Higher Education in your home country to see whether there are scholarship options.
  • We have virtual information session covering all you need to know about scholarships and financial aid. Watch it here.

For students from the Netherlands or the EU/EEA, it may be possible to apply for limited funding towards payment of your tuition fees. Find out whether you meet the nationality and age requirements and read more information about the application process here.

Master Application Handling fee

After having filled in all of the necessary application information on the Online Application Form (OLAF) and uploaded the required documents, applicants with a degree obtained outside the Netherlands will be asked to pay a non-refundable €100 handling fee. This fee can be paid online via the Erasmus Payment System which uses either iDEAL (for those with a Dutch bank account) or PayPal (which can be linked to any bank account or credit card worldwide). It is important that applicants complete the payment process as indicated, otherwise the system cannot register the payment.

Additional programme related expenses

The additional expenses in addition to tuition and general living costs (see below) vary per programme and may include:

  • Study materials such as books, readers and business cases
  • Costs involved in kick-off meetings
  • Costs related to travel, international excursions and compulsory exchange semesters or internships abroad

Living expenses

For a reasonable standard of living in the Netherlands, you should have an income of between €1,000-€1,600 per month depending on your lifestyle. Further information about the costs of living in the Netherlands and related subjects can be found on this website. Below is an example of monthly expenditures:

Furnished accommodation, including gas and electricity  €   500-900
Medical insurance  €   50
Telephone/internet €   15-25
Food €   200 - 300
Books, recreation, clothing €   200 - 300
Public Transportation €   50
Total €  average 1000 - 1625
Other potential expenses:  
Buying or renting a bike €  100 - 250 (for the full 3 years)
In private residence (not student housing) yearly municipal and water taxes €  100 - 300 (per year)

Study and work - part-time jobs

Please ensure, prior to your arrival at RSM, that you have or will have sufficient funding available to finance your stay at RSM. Finding a part-time job, may be an option, but can not be guaranteed. You should therefore not rely on finding other ways to supplement your income during your studies. For additional information on obtaining a part-time job, visit the website of the Nuffic.

For EEA students there are no formal restrictions in finding work in the Netherlands, but students with a lack of Dutch language skills will find it difficult to secure employment. Non-EEA students are subject to labour regulations, which makes the likelihood of obtaining a work permit very small. We therefore ask students not to rely on this possibility. We do not encourage students to combine studies with the heavy workload from a part-time job.

Admission & Application

On this page we have listed the minimum admissions requirements. Please read the information carefully before moving to the application process. Also make sure to check out our extensive Frequently Asked Questions section.


Immigration & visas

Find out everything you need to know about entry visas & residence permits for non-EU or EEA students at RSM.

Release date: October 2022



Finding housing in Rotterdam can be tricky. To help you in your search for housing, we have compiled some helpful resources.

After my business bachelor, a business analysis internship at Amazon (Luxembourg) and working as a web developer at Accenture (Bulgaria), it was time for me to combine my business and technical knowledge, and extend it even further with the MSc in Business Analytics & Management at RSM.

Kameliya Stefanova, Bulgaria (first cohort, MScBA Business Analytics & Management 2021)

 Kameliya Stefanova portrait photo

Is it right for me?

The MScBA in Business Analytics & Management is aimed at bachelor graduates with a business background and a good quantitative or technical orientation, and graduates from engineering and the hard sciences who are interested in business.

It’s a specialised master programme for those who want to develop their skills in managing complex business analytics projects – from understanding the business problem and its environment, and selecting and executing the methods of analysis, to communicating the results to internal and external stakeholders.

Graduates from this programme can expect exciting employment opportunities. Data analysts and data scientists have been identified as the fastest growing jobs of the future in the Future of Work report written by McKinsey.

Are you still in doubt?

Would you like extra information or support? We are here to help!

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