(in Polish) Eksploracja i optymalizacja procesów 2400-M1ABEOP
The course introduces the concept of process mining as an approach combining data analytics and process management. It covers the structure and preparation of event log data for analysis and presents the main types of process mining analyses: process discovery, conformance checking, and performance analysis. Students learn how to interpret analytical results and identify root causes of process inefficiencies.
In the practical part, students carry out a team project using a process mining tool (e.g., Celonis, Apromore, ProM, or another selected by the lecturer). The project includes the full process analysis cycle—from data preparation, through discovery and evaluation of process performance, to developing and justifying improvement recommendations with a business impact assessment.
The final grade is based on a theoretical test, a team project, and class participation. Students are assessed on their analytical reasoning, accuracy of interpretation, and ability to formulate actionable recommendations.
Szacunkowy nakład pracy studenta: 3ECTS x 25h = 75h
(K) - godziny kontaktowe (S) - godziny pracy samodzielnej
konwersatorium (zajęcia): 30h (K) 0h (S)
konsultacje: 15h (K) 0h (S)
przygotowanie do testu: 0h (K) 5h (S)
prace domowe: 0h (K) 5h (S)
projekt: 0h (K) 10h (S)
opracowanie studium przypadku: 0h (K) 10h (S)
…: 0h (K) 0h (S)
Razem: 45h (K) + 30h (S) = 75h
Type of course
Course coordinators
Learning outcomes
A) Knowledge:
The student understands the essence of process mining as an approach linking data analytics and process management. They know the main types of process mining analyses (discovery, conformance, performance) and understand how these methods can be used to evaluate process efficiency. The student understands the structure and quality requirements of event log data and their impact on analytical results. They are familiar with major process mining tools and their business applications.
B) Skills:
The student can prepare event log data for analysis, perform process discovery and conformance checking, and interpret results in terms of efficiency, compliance, and process quality. They can identify inefficiencies, bottlenecks, and deviations, propose improvements, and justify them economically. The student can use process mining software to conduct and present an analytical project.
C) Social competences:
The student can collaborate and communicate effectively in a project team during process analysis and result presentation. They understand the importance of data-driven decision-making in process management and continuous improvement. The student is able to independently expand their knowledge and skills in process analysis and analytical tool use.
Assessment criteria
Assessment covers both theoretical knowledge and practical skills.
The final grade is based on:
Multiple-choice test assessing theoretical understanding of key process mining concepts and methods (25%),
Class participation and practical exercises during computer lab sessions (25%),
Team case study involving event log analysis and interpretation (25%),
Team project implementing a complete process analysis cycle using process mining software, including presentation of results and improvement recommendations (25%).
Evaluation criteria include analytical accuracy, quality of interpretation, soundness of conclusions, and clarity of presentation.
Bibliography
van der Aalst, W. M. P. (2016). Process Mining: Data Science in Action. Springer.
Brzychczy, E., Rostek, K. (2024). Cyfrowa analiza danych i procesów. Warszawa: PWE.
Selected scientific articles and case studies on process mining recommended by the instructor during the class
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: