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Process Mining: Data science in Action

Approx. 22 hours to complete

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Course Summary

Process Mining is an innovative approach to extract valuable insights from business processes. This course covers the theoretical foundations of process mining and provides hands-on experience in applying process mining on real-life data sets using the ProM tool.

Key Learning Points

Learn the theoretical foundations of process mining
Gain hands-on experience in applying process mining on real-life data sets using the ProM tool
Extract valuable insights from business processes

Job Positions & Salaries of people who have taken this course might have

Process Mining Analyst
- USA: $83,000
- India: ₹6,00,000
- Spain: €42,000
Business Analyst
- USA: $75,000
- India: ₹5,50,000
- Spain: €38,000
Data Analyst
- USA: $67,000
- India: ₹4,90,000
- Spain: €34,000

Learning Outcomes

Understand the theoretical foundations of process mining
Gain hands-on experience in applying process mining on real-life data sets using the ProM tool
Extract valuable insights from business processes

Prerequisites or good to have knowledge before taking this course

Familiarity with basic concepts of data analysis and business processes
A computer with internet access

Course Difficulty Level

Intermediate

Course Format

Online Self-paced
Video Lectures
Hands-on Projects

Similar Courses

Data Analytics for Process Improvement
Process Mining: Data Science in Action

Related Education Paths

Notable People in This Field

Process Mining Expert
Co-founder and Chief Marketing Officer at Fluxicon

Related Books

Description

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 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. The course covers the three main types of process mining. 1. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the Alpha-algorithm that takes an event log and produces a process model (a Petri net) explaining the behavior recorded in the log. 2. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa. 3. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases. 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. After taking this course you should: - have a good understanding of Business Process Intelligence techniques (in particular process mining), - understand the role of Big Data in today’s society, - be able to relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification, - be able to apply basic process discovery techniques to learn a process model from an event log (both manually and using tools), - be able to apply basic conformance checking techniques to compare event logs and process models (both manually and using tools), - be able to extend a process model with information extracted from the event log (e.g., show bottlenecks), - have a good understanding of the data needed to start a process mining project, - be able to 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), and - be able to conduct process mining projects in a structured manner.

Outline

Introduction and Data Mining
Course Background and Practical Information
1.1: Data Science and Big Data
1.2: Different Types of Process Mining
1.3: How Process Mining Relates to Data Mining
1.4: Learning Decision Trees
1.5: Applying Decision Trees
1.6: Association Rule Learning
1.7: Cluster Analysis
1.8: Evaluating Mining Results
Introducing Fluxicon & Disco
Real Life Session 01: The Demo Scenario (7 min.)
Real Life Session 02: Process Discovery and Simplification (11 min.)
Real Life Session 03: Statistics, Cases and Variants (8 min.)
Real Life Session 04: Bottleneck Analysis (7 min.)
Real Life Session 05: Compliance Analysis (6 min.)
Real Life Session 06: Tip 1 - Keep Copies of your Analyses (4 min.)
Real Life Session 07: Tip 2 - Take Different Views on your Process (7 min.)
Real Life Session 08: Tip 3 - Exporting Results (4 min.)
Welcome to Process Mining: Data Science in Action
The Forum is your (Extended) Classroom
Process Mining: Data Science in Action Getting Started!
[Extra] The data used in the lectures
How is Process Mining Different from Data Mining?
Quick Note Regarding Quizzes in this Course
Real-life Process Mining Session
Quiz 1
Real-life Process Mining Session Quiz (Not for points)

Process Models and Process Discovery
2.1: Event Logs and Process Models
2.2: Petri Nets (1/2)
2.3: Petri Nets (2/2)
2.4: Transition Systems and Petri Net Properties
2.5: Workflow Nets and Soundness
2.6: Alpha Algorithm: A Process Discovery Algorithm
2.7: Alpha Algorithm: Limitations
2.8: Introducing ProM and Disco
Using Event Data to Tear Down the Towers of Babel in Process Management
Quiz 2
Tool Quiz

Different Types of Process Models
3.1: Four Quality Criteria For Process Discovery
3.2: On The Representational Bias of Process Mining
3.3: Business Process Model and Notation (BPMN)
3.4: Dependency Graphs and Causal Nets
3.5: Learning Dependency Graphs
3.6: Learning Causal nets and Annotating Them
3.7: Learning Transition Systems
3.8: Using Regions to Discover Concurrency
Process Mining in the Large: Smart Data Scientists Are More Important Than Big Computers!!
Quiz 3

Process Discovery Techniques and Conformance Checking
4.1: Two-Phase Process Discovery And Its Limitations
4.2: Alternative Process Discovery Techniques
4.3: Introduction to Conformance Checking
4.4: Conformance Checking Using Causal Footprints
4.5: Conformance Checking Using Token-Based Replay
4.6: Token Based Replay: Some Examples
4.7: Aligning Observed and Modeled Behavior
4.8: Exploring Event Data
Conformance Checking: Positive and Negative Deviants
Quiz 4

Enrichment of Process Models
5.1: About the Last Two Weeks of This Course
5.2: Mining Decision Points
5.3: Discovering Data Aware Petri Nets
5.4: Mining Bottlenecks
5.5: Mining Social Networks
5.6: Organizational Mining
5.7: Combining Different Perspectives
5.8: Comparative Process Mining Using Process Cubes
5.9: Refined Process Mining Framework
Holistic Process Mining: Integrating Different Perspectives
Quiz 5

Operational Support and Conclusion
6.1: Operational Support: Detect, Predict and Recommend
6.2: Getting the Right Event Data
6.3: Guidelines for Logging
6.4: Process Mining Software
6.5: How to Conduct a Process Mining Project
6.6: Mining Lasagna Processes
6.7: Mining Spaghetti Processes
6.8: Process Models as Maps
6.9: Data Science in Action
Process models are like maps: Which one is best depends on the questions that need to be answered!
Overview: Process Mining Software
Quiz 6
Final Quiz

Summary of User Reviews

Discover the principles, techniques, and tools used in process mining with this Coursera course. Students rated this course highly and found it to be a valuable resource for learning about process mining. One key aspect that many users thought was good is the course's practical focus, which allows students to apply what they've learned in real-world settings.

Pros from User Reviews

Practical focus allows students to apply what they've learned in real-world settings
Instructors are knowledgeable and engaging
Course materials are well-organized and easy to follow
Hands-on exercises provide valuable experience

Cons from User Reviews

Some users found the course to be too basic
Course videos are sometimes slow to load
Time commitment required for the course is significant
Some users felt that the course could benefit from more case studies
Course assignments can be challenging

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