Have you missed the Coursera MOOC1 Process Mining: Data science in Action the last time around? Or did you have to drop out, because you did not have the time to complete it? You are in luck, because the Process Mining MOOC starts again today, on October 7, 2015. It’s a free online course, where you can watch video lectures and test your knowledge through online quizzes.
Fluxicon is supporting the MOOC by providing training licenses for our process mining software Disco. The new edition of the MOOC will also include a real-life process mining session that gives you a taste of how you can solve real process problems in your organisation with process mining. You can sign up here.
We spoke with Prof. Wil van der Aalst, who created the MOOC, about how online classes compare to regular class-room studies and what established process mining analysts can get out of following the course.
Interview with Wil
The MOOC ‘Process Mining: Data Science in Action’ is starting again on 7 October in its third edition. So far, already more than 65,000 people have participated in the MOOC. That is an incredible success. Now, there will be many more new people who will come in contact with process mining for the first time. We have also heard from several people who had to drop out of one of the previous courses and who will now be taking it again.
What do you think are the advantages and what are the disadvantages of learning about a topic like process mining in an online course? Are there things that are easier and things that you see that are more difficult for online learners compared with your regular university classroom courses?
The main advantage of taking an online course is that it is not bound to a fixed location and time. It is amazing to see people from over 200 countries participating in a course. We are reaching people that would never have had the opportunity to study process mining otherwise (because of location and time constraints). It has helped to create awareness: Many BPM practitioners and Data Scientists still do not know that these powerful techniques are available and directly applicable.
However, MOOCs do not replace class rooms. Studying is also a social process. Personal contact between teachers and students is important. Students that study in groups can ask questions and motivate each other. MOOCs try to mimic this through a forum, but this is not the same thing. Nevertheless, it is interesting to see the interactions between participants in the forum of the Process Mining MOOC.
Yes, the forums have been very active and it was great to see how people are discussing the material and help each other out.
What can a practitioner who is already actively working with process mining still learn from the MOOC, why should they participate?
The topic of process mining is quite broad and extends far beyond automated process discovery. The MOOC provides a rather complete view of the spectrum and will help practitioners to think of analysis opportunities they would otherwise not see (conformance checking, data-aware process mining, predictions, etc.).
It is also important to have a basic understanding of the way algorithms work and what the foundational limitations and trade-offs are. When pushing the discovery button of your favorite process mining tool, one should understand process discovery in order to interpret the results and to get the diagnostics one is looking for. For example, there is always a trade-off between fitness, precision, generalization, and simplicity. Understanding these trade-offs is important when being confronted with “Spaghetti models”.
What do you recommend to people who – after finishing the MOOC – want to make the next step. What should they do?
There is a lot of material available. Of course people should study the book “Process Mining: Discovery, Conformance and Enhancement of Business Processes”. The website http://www.processmining.org/ also provides many pointers.
However, perhaps more important, people should also simply get started with concrete datasets. The course also helps people with this. Many datasets are available online (see for example http://data.3tu.nl/repository/collection:event_logs, http://www.processmining.org/logs/start, etc.). Also apply tools like Disco and ProM to the datasets in your organization (event data are everywhere!).
People say “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it”. We should avoid that they say the same about process mining. Process mining is very practical and the threshold to get started is much lower than for most other technologies.