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. The course is at an introductory level with various practical assignments.
This course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. There are many examples using real-life event logs to illustrate the concepts and algorithms.
The audit-enrollment on Coursera gives opportunity to make this course for free, but without assigments feedback or grades.
- Introduction to data mining and process mining
- Process Models and Process Discovery
- Different Types of Process Models
- Process Discovery Techniques and Conformance Checking
By the end of the course, you‘ll be able to...
- understand Business Process Intelligence techniques (in particular process mining).
- understand the role of Big Data in today’s society.
- relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification.
- apply basic process discovery techniques to learn a process model from an event log (both manually and using tools).
- apply basic conformance checking techniques to compare event logs and process models (both manually and using tools).
- extend a process model with information extracted from the event log (e.g., show bottlenecks).
- understand the data needed to start a process mining project.
- characterize the questions that can be answered based on such event data.
- explain how process mining can also be used for operational support (prediction and recommendation).
- conduct process mining projects in a structured manner.
- Intermediate knowledge of programming