MScBA Business Analytics & Management

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. Margolin, Dr A. Priante, Dr 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

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.