Curriculum

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Curriculum

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.

    • 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

      Review the course guide for more details.

      Taught by Dr Jan van Dalen & Dr Dominik 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 modeling, data definition, metadata, master data management alongside ethical frameworks that should guide the management of data.

      Review the course guide for more information. 

      Taught by Dr Anna Priante & Dr Dion 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.

      Review the course guide for more details.

      Taught by Dr Jason 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.

      Review the course guide for more details.

      Taught by Dr Angelos 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.

      More information can be found in the course guide.

      Taught by Dr Pieter 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.

      Review the course guide for more details.

      Taught by Dr Maximilian Margolin, Dr Anna PrianteDr Maciej Szymanowski & Dr Luuk Veelenturf

    • The aim of Your Future Career is to prepare students at an early stage in their MSc for their career.

      When you care about what you do, you will enjoy your work more, create greater impact, and be more successful in being a force for positive change. However, it can be difficult to identify what your passion is, where your competencies and skills will be useful, and which professional environment and culture are the best match for you. Therefore, RSM Career Centre has developed a course to put you in the driver's seat of your career, and to support you in identifying your first career step after graduation and preparing for it.

      The online modules of “Your Future Career” will help you make crucial steps towards the most suitable internship or job for you. To pass the course you need to gain a minimum of 50 points by 31 January 2022, 16:00. You can decide yourself if you want to reflect on your interests and motivations, develop knowledge of the job market, functions, companies and industries, receive peer feedback on your application materials, have contact with an alumni mentor or attend an interactive workshop.

      The course will be offered to MSc programmes who opted in for this. The Your Future Career course takes place in block 1 and 2 (30 August 2021– 31 January 2022) and is awarded 1 ECTS based on pass/fail.

      Contact: RSM Career Centre via yourfuturecareer@rsm.nl

      Review the course guide for more details.

      Taught by dr. M. Szymanowski & L. Keir.

      • 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 impacted firm internal business processes and external interactions with other businesses, consumers, policy makers. The aim of this course is to learn how digital transformation reshapes key strategic domains of firms, including internal processes, competition, value creation and innovation. One of the areas disrupted by digital transformation is supply chain management. The new operating model for purchasing and supply management, known as Procurement 4.0 will be thoroughly addressed as well as the new role of purchaser in the organization.

        In this course, we will discuss the core changes brought by digital transformation and the new approaches actively adopted by firms to stay competitive, such as artificial intelligence and data-driven decision making. We will address the following questions: How do new business models emerge as a consequence of digitalization? How do traditional businesses adapt to the markets disrupted by digital entrants? What is the impact of this transformation on supply chains and what are the resulting challenges in sourcing, inventory management, distribution and product returns? How can businesses leverage the power of data for enabling new ways of addressing customer needs and operational efficiency and competing their rivals?

        More information can be found in the course guide.

        Taught by Dr Olga SlivkoErick Haag & Prof. Rob Zuidwijk.

         

      • Marketing is the interface between the 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. For instance, advertising is usually costly for companies and marketers need to calibrate to what extent the potential revenue generated by the advertising campaign exceeds its costs. However, in practice, achieving such goal is challenging, given there are countless interacting factors that influence customer responses and not all factors are under the control of managers. To address these challenges, academics and practitioners have developed and adopted a selection of analytical tools in the form of “marketing models”, crystallized from disciplines such as economics, statistics and computer science. This course focuses on how to translate managerial problems (e.g. 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, how to build and manage heterogeneous and dynamic customer portfolios) into analytical problems and solve the analytical problems with various marketing models. During the course, you will work on cases where you solve real managerial problems with real data. Through these exercises, the course will show you how to understand, predict and influence customers using marketing models.

        Review the course guide for more details.

        Taught by Dr Xi 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 representations 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.

        Review the course guide for more details.

        Taught by Dr Dion Bongaerts & Prof. Erik Peek.

      • 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.

        Review the course guide for more details.

        Taught by Dr Aurélie Lemmens.

      • 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.

        This elective aims to make you think thoroughly about the role of algorithms in organizations. During the course you will get hands-on experience with algorithms by gradually building-up your text analysis skills on real company data, culminating with a control motivated investigation of possible problems in text-based classifiers. The course also aims at teaching you helpful workflows that will stand you in good stead outside of this class (e.g., develop your business writing skills). This elective builds on the Data Management & Ethics core course and on the core elective Principles of Financial Modeling.

        Review the course guide for more details.

        Taught by Dr Iuliana 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.

        Review the course guide for more details.

        Taught by Dr Philipp 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.

        Review the course guide for more details.

        Taught by Dr Müge 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.

        Review the course guide for more details.

        Taught by Dr Ana 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 in parallel. 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.

        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 one of these business models (an algorithmic/high-frequency trading operation) under the supervision of an instructor with ample real-life experience of doing so.

        Review the course guide for more information.

        Taught by Dr Dion Bongaerts, Dr Thomas Lambert & Roger van Daalen.

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.