A new version of the free Coursera course “Process Mining: Data Science in Action” will start on November 28th 2016. The course is highly relevant for anyone that wants to improve his/her analytical skills. The focus is on data science methods applied to event data, e.g., for BPM, CRM, ERP, CEP, and (Lean) Six Sigma. A data scientist without Process Mining training is ill-equipped to uncover the organization’s real processes, analyze compliance, diagnose bottlenecks and improve processes. The next generation of process analysts, managers and auditors will depend on this new technology!

 

Over 100.000 people have registered for earlier versions of the course in the last two years. Many participants of the “Process Mining: Data Science in Action” course got “hooked to the magic of analyzing event data”. Participants that completed the course learned to automatically discover real processes, check conformance, and analyze performance. Also the new course provides access to software and real-life data sets. Hence, there are many good reasons to join this new Process Mining course.

>> Register via http://www.coursera.org/learn/procmin/ <<<

 

On demand

Since April 2016 the course is running in on-demand mode on the Coursera platform. Through the on-demand mode you can always revisit the course to refresh your process mining knowledge. This allows you to take the course at any point in time. Moreover, we are able to create special instances of the course to support web lectures at universities (e.g., flipped classrooms) and for in-company training. If you are interested, send a CSV file with two columns (name and e-mail address) to j.c.a.m.buijs@tue.nl.

 

New book on Process Mining

We are also happy to announce the availability of the new process mining book “Process Mining: Data Science in Action” (written by Wil van der Aalst, see http://www.springer.com/gp/book/9783662498507). The updated version of the course that will go live on November 28 aligns well with the new book. Participants have free access to selected parts of the book. Moreover, Springer also kindly offers the participants of this MOOC a substantial discount on both the electronic and paper version of the new book.

 

Win a signed copy of the new book!

To celebrate the launch of the new book and updated course, we will select five students of the November 28 session that passed the honors track (e.g. made all week quizzes, final quiz, tool quiz and peer assignment) and will send them a free hard-copy of the book signed by the author.

 

The uptake of Process Mining

Since the first book on Process Mining in 2011, we have witnessed a rapidly growing interest in process mining. Today, Process Mining is the primary approach to make BPM truly data-driven. The attention for Big Data and the uptake of Data Science strengthen this development. Process Mining is where Data Science and Process Science meet! The growth on Process Mining has been accelerating during 2015 and 2016. Currently, there are about 25 software vendors offering process mining tools. Next to Disco (Fluxicon’s tool is used in the course next to the open-source tool ProM), tools like Celonis Process Mining, ProcessGold Enterprise Platform, Minit, myInvenio, Signavio Process Intelligence, QPR ProcessAnalyzer, LANA Process Mining, Rialto Process, Icris Process Mining Factory, Worksoft Analyze & Process Mining for SAP, SNP Business Process Analysis, webMethods Process Performance Manager, and Perceptive Process Mining are now available. The availability and application of these tools illustrate the uptake of Process Mining.

 

We are looking forward to see you, and your colleagues and friends, in the new Process Mining MOOC on Coursera. Visit http://www.coursera.org/learn/procmin/. Please help us to spread the message and let more people experience the amazing capabilities of Process Mining!

 

Hope to see you in our course,

Wil van der Aalst

Joos Buijs

and the rest of the process mining team

Are you aware that “Business Intelligence” and “(Advanced) Analytics” are actually completely different?

This should resolve the confusion for people who think that it´s actually the same, maybe because they are told so suggested by renaming of former “Business Intelligence” solutions, departments etc. to “Analytics”.

If one thinks that it´s just another name (“old wine in a new bottle”) he might not spend the time to find out that there is actually a difference and therefore he might not be aware of the new opportunities there are to innovate in his area and to really improve applications and solutions.

The information below is just a basic list of terminology to give you a quick start on this. Please be motivated to dig deeper.

 

“The analysis of all kinds of data using sophisticated quantitative methods (for example, statistics, descriptive and predictive data mining, simulation and optimization) to produce insights that traditional approaches to business intelligence (BI) – such as query and reporting – are unlikely to discover.”

(Gartner, Magic Quadrant for Advanced Analytics Platforms, Gareth Herschel | Alexander Linden | Lisa Kart, 19 February 2014)

https://rapidminer.com/resources/advanced-analytics-introduction/

 

Analytics / Advanced Analytics / Data Science / (Predictive Analytics)

Data Mining

Data mining (the analysis step of the “knowledge discovery in databases” process, or KDD), an interdisciplinary subfield of computer science is the computational process of discovering patterns in large data sets (“big data”) involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

Machine Learning

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.

Predictive Modeling

Predictive modeling leverages statistics to predict outcomes. Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place.

 

Business Intelligence / Management Information Systems

 

Business Reporting

Business reporting or enterprise reporting is “the public reporting of operating and financial data by a business enterprise,” or “the regular provision of information to decision-makers within an organization to support them in their work.” Reporting is a fundamental part of the larger movement towards improved business intelligence and knowledge management. Often implementation involves extract, transform, and load (ETL) procedures in coordination with a data warehouse and then using one or more reporting tools.

Dashboard

In management information systems, a dashboard is “an easy to read, often single page, real-time user interface, showing a graphical presentation of the current status (snapshot) and historical trends of an organization’s or computer appliances key performance indicators to enable instantaneous and informed decisions to be made at a glance.” In real-world terms, “dashboard” is another name for “progress report” or “report.” Often, the “dashboard” is displayed on a web page that is linked to a database which allows the report to be constantly updated.

Mashup

A mashup, in web development, is a web page, or web application, that uses content from more than one source to create a single new service displayed in a single graphical interface.

“Data Analytics & R” – Blog is setup and running. The link to the blog is
“http://advanceddataanalytics.net/ “.

For the name I decided to use “Data Analytics & R”, due to a community I am also running having the same title and because I will mainly talk about “Data Analytics” and: “R”, because R is a good choice to start and to get informed about the latest publications and to have a first look at the related algorithms and run them on your own data. You can treat “Data Analytics” as an abbreviation for “Data Science, Data Mining, Machine Learning, Statistical Learning, Statistics, Analytics Modeling, Business Analytics, Knowledge Discovery (KDD), Soft Computing, Data Aggregation, Econometrics, Visualization & related Programming“. This means: Everything needed to derive something out of your data, with whatever tool or programming language necessary or appropriate to achieve this.

Thank you WordPress,

Michael

P.S. Let me apologize here in the beginning for my english. It is not as good as it should be.