Learn how to use the free, open source process mining framework (ProM) to analyse, visualise, and improve processes based on data. Process mining combines business process management with data science. Using process mining, you can analyse and visualise business processes based on event data recorded in event logs. For example, you could analyse how people use public transportation; verify whether a loan application is processed correctly by a bank; or predict when hardware parts are likely to fail. This online course will give you an introduction to this new and exciting field.
What topics will you cover?
Process mining provides a critical, process-centric perspective on data, which is not available with classical data mining or machine learning techniques. For example, with process mining you can:
- analyse how people use public transportation;
- verify whether the loan application process in a bank is executed correctly;
- gain insights in customer journeys on a website and combine this with interactions on different channels such as phone and e-mail;
- analyse learning behaviour of students in a MOOC to improve course contents;
- predict when hardware parts are likely to fail.
Week 1 introduced process mining and showed that event data is everywhere. Furthermore, it explained how you can translate event data that you might have for use in our tool. We also discussed how to start investigating the event log and how to filter the event log based on these insights for further analysis.
Week 2 discussed process models and how to evaluate how good a process model is with respect to the data. We also demonstrated several techniques to automatically learn a process model from the data, and discussed the pros and cons of each approach.
Week 3 covered additional process mining techniques such as conformance checking, performance analysis and social network analysis. This week also showed process mining application examples.
Week 4 provided learners with time to work on the peer assignment where they were asked to write a real process mining report on real-life event data.
After this course learners can:
- work with the free and open source process mining tool ProM;
- process raw event data into an event log for further analysis;
- execute core process mining analysis techniques, and correctly interpret the results;
- gain concrete and actionable process insights from (their own) event data.
What will you achieve?
By the end of the course, you'll be able to...
- Identify event data suitable for analysis
- Apply various process mining techniques in the ProM lite tool to event logs
- Interpret the results of various process mining techniques in the ProM lite tool
- Describe the process flow, based on the event data
- Improve processes based on process mining analysis of the event data
This course is designed for anyone with an interest in business process management (BPM), data science and/or process analytics. No specific prior knowledge is required, only a healthy interest is required.
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.
MOOCs – Massive Open Online Courses – enable students around the world to take university courses online. This guide, by the instructors of edX’s most successful MOOC in 2013-2014, Principles of Written English (based on both enrollments and rate of completion), advises current and future students how to get the most out of their online study, covering areas such as what types of courses are offered and who offers them, what resources students need, how to register, how to work effectively with other students, how to interact with professors and staff, and how to handle assignments. This second edition offers a new chapter on how to stay motivated. This book is suitable for both native and non-native speakers of English, and is applicable to MOOC classes on any subject (and indeed, for just about any type of online study).