The members of the section Business Analytics teach in Bachelor's, Master's and Executive Programmes. Find a selection of our teachings below.
This course gives an introduction to the theories behind the design, control and improvement of the processes that create goods and services. Creating products and services is clearly the key for the existence of any organisation. An organisation's mastery of its business processes may entail substantial competitive advantage. The demands in terms of efficiency, quality, speed, flexibility and dependability are challenging. The strategic importance of effective and efficient operations and process management has resulted in a thriving scientific discipline and a flood of commercial offerings by consulting firms in business and industry.
The course offers a wide variety of fields, including manufacuring services, governmental and healthcare organisations. Operations are often complex and dynamic systems, decisions and problems are typically fuzzy and pluralistic. Part of the course is about learning to structure such decisions and problems to allow the derivation of a rational solution.
Good strategic decision making requires first and foremost high quality information. For managers and other decision makers it is therefore crucial to understand the quantitative and statistical methods (and drawbacks) that are so often used to generate the information they are provided with. Moreover, in their theses the students often need to perform statistical analysis. This course reviews and explains the basic statistical concepts and techniques which are used in the area of Management and Business Administration and MSc theses and emphasises the practical application of the various techniques using SPSS software.
Description: Large amounts of consumer data are available in today’s (online) world. This creates many opportunities for smarter and data driven marketing. Some of the topics the students become familiar with during the course are:
Big Data refers to data that are more voluminous, but often also more unstructured than traditional data. This in particular concerns data-collection that draws on internet-based data sources such as social media, large digital archives, and public comments to news and products. One of the big challenges is to derive information from these messy data. The first step in this process is called data wrangling, which is the main subject of this course. Once the data is parsed and cleaned, it is usually analysed in an exploratory way before more advanced statistical or machine learning techniques are applied. The focus lies on:
Description: The underlying question behind this course is: how does a machine collect, represent and process textual data to algorithmically extract valuable information, identify consistent patterns and learn systematic relationships between pieces of text? The technological topics which will be covered in this course are:
Data and databases play a central role in any information system from transaction processing to enterprise systems and, of course, data science applications. The purpose of this course is to offer a solid understanding of the core concepts in this area as well as an opportunity to apply these concepts hands-on and in a ‘living case’ business setting. These core concepts are based on the relational data model and SQL - as the de facto standard database language - combined with data visualisation and the design of metrics and dashboards. The course includes a significant practical part with a focus on data modeling, on SQL and on data visualisation to solve business issues.
This course provides an introduction to machine learning for the students with little to no knowledge of the subject. The topics that will be covered in the course include:
This course provides an introduction to mathematical modelling of computational problems. It covers the common algorithms, algorithmic paradigms and data structures used to solve these problems. It also uses the Python programming language to implement and test algorithms and data structures on realistic datasets. The technological topics which will be covered in this course are:
Managing a business involves identifying, analysing and developing improvement opportunities in processes and in the organisation. Managers need professional skills in problem analysis, problem solving and decision taking. As a generic structure for the process of problem-analysis, we take Lean Six Sigma's DMAIC (Define/Measure/Analyse/Improve/Control) model which has become the world standard in business and industry. The DMAIC approach helps in structuring messy problems and translates them info a quantitative decision problem. For a selection of common business problems we learn standard quantitative analysis approaches:
Managing a business involves identifying, analysing and developing improvement opportunities in processes and the organisation. Managers need professional skills in problem analysis, problem solving and decision-making. A generic structure for improvement projects in Lean Six Sigma.
Lean Six Sigma is built on principles and methods that have proven themselves over the 20th century. It has incorporated the most effective approaches and integrated them into a full programme. It offers a management structure for organising continuous improvement of routine tasks, such as manufacturing, service delivery, healthcare, sales, nursing and other work that is done according to a routine. Furthermore, if offers a method and tools for carrying out improvement projects effectively.
Good decision-making requires, first and foremost, high quality information. For managers and other decision makers it is therefore crucial to understand the quantitative and statistical methods (and drawbacks) that are so often used to generate the information with which they are provided.
This course reviews and explains the statistical concepts and techniques that are most commonly used in the area of management, business administration and healthcare. The course emphasises the practical application of various statistical techniques using Minitab. In the lectures, the various statistical techniques will each be carefully explained and illustrated with examples of how and why they may be used in management research.
The proliferation of data in all its forms has opened up the way to a new class of intelligent methods which are able to learn from this data. A large amount of this data is in human language form, either written or spoken.
This course will focus on technologies that allow machines to read and comprehend human language and generate human language themselves, so that human language can be transformed to information and communicated back to humans. A variety of applications will be presented aligned with tasks such as text clustering, classification, retrieval, question-answering, summarisation, together with the underlying language technology which includes language representation, knowledge extraction and information retrieval.
Building on the theories, concepts, models and research introduced in Theories of Digital Business, we now look in more detail at how to model, innovate and transform customer interactions, business processes as well as business models with digital technologies and big data. Through lectures and case studies we explore theories and their application using a variety of tools for modelling and visualisation, including customer journey mapping, process modelling, business model mapping, big data visualisation and analytics dashboards.
Rapid changes in digital technologies and their application are causing major changes for individuals, organisations and industries. Most recently, the internet and the availability of big data are radically impacting our personal and professional lives and challenge our thinking on how we live, work, learn, communicate, compete, collaborate, and socialise. As part of this digital transformation, new business models are emerging, as are new types of entrepreneurship and new forms of leadership.
The new elements include management practices and views on value creation, globalisation and entrepreneurship. This transforms both established industries such as manufacturing, transport and hospitality (think of globalisation, outsourcing, open innovation and disruptors such as Uber and Airbnb) as well as newer and converged industries such as telecommunications and media (think of platforms and hubs such as those offered by Alibaba, Amazon, Apple and Google).
This course aims at providing a deeper understanding of the issues, challenges and opportunities in this area, with a specific focus on creating business value with IT and big data. Understanding the developments and the underlying principles is crucial for all aspects of business administration, from marketing to logistics and from strategy to HR. The course emphasises a global organisational and managerial approach to digital business, covering strategic issues as well as implementation and change.
The proliferation of data in all its forms, be it symbolic, numeric, textual or visual, has opened up the way to a new class of intelligent methods which are able to learn from this data. Applications of machine learning are broad and diverse and range from prediction of health parameters, understanding the content of social media, to the recommendation of products. In this course we teach the theoretical foundations of machine learning and how to apply these methods in practical analytic tasks.
The course will be comprised of the following: