Courses

Courses

Overview

Since 1992, I have created over 35 courses in Data Science (DS), Business Process Management (BPM), Process Mining (PM), Business Process Intelligence (BPI), Workflow Management (WFM), Business Information Systems (BIS), Process Scinece, Petri Nets, Systems Engineering, Simulation, and Information Systems. See the PADS website for our current courses, (pro)seminars, and labs. The two key course I give are Introduction to Data Science (IDS) and Business Process Intelligence (BPI).

Getting started with process mining

Many people ask me how to get started with process mining. The answer is simple: Read the book Process Mining: Data Science in Action and take the Coursera Process Mining Course and/or the joint RWTH/Celonis course Process Mining: From Theory to Execution to get a comprehensive overview and a deeper understanding of the concepts and techniques. For people that would like to work with us and apply process mining or seek advice, study the slide set and try to answer the questions first (download the PowerPoint show and answer the questions first before requesting a meeting).

This a broad course introducing data science at the master level. The course aims to provide a comprehensive overview of data science using analytical tools applied to real-life and synthetic datasets. At the same time the course goes deep in selected topics, e.g., data visualization, decision trees, regression, support vector machines, deep learning, neural networks, evaluation of supervised learning problems clustering, frequent items sets, association rules, sequence mining, process mining (unsupervised), text mining, NLP, big data, mapreduce, distributed computing, visual analytics, responsible data science, privacy, discrimination-aware data mining, etc. This is an essential course for anyone that wants to become a data scientist.

This course provides a comprehensive introduction to process mining at the Bachelor/Master level. The course starts with an overview of approaches and technologies that use event data to support decision-making and business process (re)design. Subsequently, the course focuses on process mining as a bridge between data mining and business process modeling. All types of process mining are covered, e.g., four different process discovery techniques, three conformance checking techniques, decision mining, organizational mining, operational support, performance analysis. Also, various process mining tools (open-source and commercial) are introduced.

Process Mining: From Theory to Execution

Celonis and RWTH Aachen University jointly created a new process mining course aiming to bridge the gap between the theory of process mining and the practical application using a commercial tool and real-life data sets. This 10-hour course is called Process Mining: From Theory to Execution and can be taken at any time. The course is led by Wil van der Aalst, professor at the RWTH Aachen University and chief scientist at Celonis. Register using this link. The course is free of charge.

After taking this compact course, participants will have learnt about current trends in process mining and automation, know the key process discovery and conformance checking algorithms, and also study comparative and predictive process mining techniques allowing organizations to perform root cause analysis of performance and compliance problems.

Coursera Process Mining Course

The course Process Mining: Data science in Action was given in 2014 for the first time. The initial run of the course already attracted 42.073 registered participants. Over 150.000 people participated already, illustrating the interest in process mining. After the initial runs the course is now continuously available on demand.

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using a booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models. Hence, we refer to this as data science in action.

The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains.

This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments.

How to Write Beautiful Process-and-Data-Science Papers?

Many researchers often struggle to write a good scientific paper and also practitioners find it hard to explain new concepts and approaches. To help, I wrote the style guide How to Write Beautiful Process-and-Data-Science Papers?. The style guide was also uploaded to arXiv and the sources can be found here.

The style guide How to Write Beautiful Process-and-Data-Science Papers? aims to provide concrete suggestions and help authors to write better papers that can stand the test of time. The goal of any researcher should be to write papers that have an impact and progress science. This is only possible if papers are accessible and of good quality. We used Dijkstra's proverb Beauty Is Our Business to set the ambition level for scientific papers.

A selection of earlier courses given at Eindhoven University of Technology

I was also responsible for several courses outside Eindhoven University of Technology including:

Business Information Systems (not maintained)

The ultimate goal of any information system is to support processes. The system itself is not to primary objective. Therefore, Business Information Systems (BIS) need to be designed and analyzed such that in the end the processes are conforming to certain rules (e.g., auditing or legal requirements), response times and flow times are a short as possible, costs are reduced, and risks are minimized. Therefore, the focus of this course is one the relation between processes and systems.

The language used in this course is high-level Petri nets as supported by CPN Tools. CPN Tools is used as a tool to test ideas, to do simple simulations and other forms of analysis, and to construct basic prototypes. The course focuses on transforming informal descriptions of business processes and systems into high-level Petri nets. Given an informal description, students should be able to map the control-flow perspective onto high-level Petri nets. Also mappings of the other perspectives (e.g., data, resources, organization, and applications) onto abstractions understandable by computer programs are considered.

Process Mining (not maintained)

Process mining provides a new means to improve processes in a variety of application domains. There are two main drivers for this new technology. On the one hand, more and more events are being recorded thus providing detailed information about the history of processes. On the other hand, in most organizations there is a need to improve process performance (e.g., reduce costs and flow time) and compliance (e.g., avoid deviations or risks). This advanced course on process mining teaches students the theoretical foundations of process mining and exposes students to real-life data sets to understand challenges related to process discovery, conformance checking, and model extension.

How to get started with process mining? (pdf)

Business Process Management Systems (not maintained)

The ultimate goal of any information system is to support processes. The system itself is not to primary objective. Therefore, Business Information Systems (BIS) need to be designed and analyzed such that in the end the processes are conforming to certain rules (e.g., auditing or legal requirements), response times and flow times are a short as possible, costs are reduced, and risks are minimized. Therefore, the focus of this course is one the relation between processes and systems.

The language used in this course is high-level Petri nets as supported by CPN Tools. CPN Tools is used as a tool to test ideas, to do simple simulations and other forms of analysis, and to construct basic prototypes. The course focuses on transforming informal descriptions of business processes and systems into high-level Petri nets. Given an informal description, students should be able to map the control-flow perspective onto high-level Petri nets. Also mappings of the other perspectives (e.g., data, resources, organization, and applications) onto abstractions understandable by computer programs are considered.

Workflow Managment Systems (not maintained)

This course introduces the basic concepts of workflow management. The emphasis is on modeling workflow processes and the characteristics of contemporary workflow management. Workflow processes are a specific type of operational processes typically associated with work processes in administrative environments. However, any case-driven operational process falls in this category. Workflow technology provides the functionality to support these processes. Since this technology is adopted in many enterprise information systems knowledge about these systems and experience in making and enacting workflow models is relevant for students in operations management.

Business Process Intelligence (not maintained)

This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments. Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between "business" and "IT". Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development. The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field.

Using My Material

We encourage lecturers, practitioners, and researchers to use the material (slides, exercises, datasets, etc.) provided in the above courses. When using it for other courses, presentations, and publications please clearly refer to the original source and credit the author(s). In case of doubt, contact me. If you would like to use the PowerPoint files, please send an e-mail to me containing a detailed request and statement.

To:

Prof.dr.ir. Wil van der Aalst
Eindhoven University of Technology
Department of Mathematics and Computer Science (MF 7.103)
PO Box 513
NL-5600 MB Eindhoven
The Netherlands

Hereby I request to use the following material:

-----specify the requested slides/ppt files

to be used in the following course:

-----list the name of the course, institution, and number of students

 

I declare that I will credit the original author(s) (Wil van der Aalst et al.) on each slide/pages using the above material.

 

Kind regards,

list name, address, position, and e-mail address