AXA building boulevard du Souverain, 25 Watermael-Boitsfort, BE
450 Business & Data Science pro’s Attending
Toon Vanagt- Laurent Fayet – Filip Maertens – Kris Peeters – Vincent Blondel –David Martens – Hans ConstandtThe Data Innovation Summit in Brussels is a one day conference gathering all the Belgian actors facilitating data innovation. It is an action packed conference where more than 50 speakers will demonstrate what they do that helps us compete i…
I am looking for a PhD candidate in the area of Process Mining. Within the Leuven Institute for Research on Information Systems (LIRIS), you will be conducting research in an upcoming field that is situated at the intersection of business process management and data mining. For more details, see pdf-file below.
The PhD will be doing research on the topic of Process Mining. Process mining techniques focus on process
discovery (extracting process models from event logs), conformance checking (comparing normative models
with the reality recorded in event logs), and extension (extending models based on event logs). It is a fastgrowing
field at the intersection of the business process management and data mining fields. From a
business viewpoint, process mining mainly has an edge in terms of objectivity because it allows to extract
business process knowledge from organisational data rather than going through the cumbersome and errorprone
process of interviews, workshops, etc. As such, process mining is gaining lots of traction in industry
because it is an excellent approach for driving business process improvements.
The LIRIS research group has plenty of experience with research in this area as demonstrated by several
ongoing and completed PhD projects in this area. Concretely, you will work on topics ranging from trace
clustering, active learning-based process mining, predictive process mining, conformance checking, to
hybrid process modelling and mining. You will develop prototype software implementations and will be
able to demonstrate their usefulness in practical settings through use cases in collaboration with industry
Modern information and communication technology is increasingly capable of collecting and generating large amounts of data that need to be analyzed to become useful or profitable. In fact, these amounts quickly become too large for immediate human understanding, leading to a situation in which “we are drowning in data but starved for knowledge”.
Data science represents an essential technology to transform such data into knowledge. It allows the automated discovery of interesting regularities or anomalies in large databases, thereby surpassing standard statistical summarizing. Typical tasks include the construction of predictive and descriptive models for classification, regression, clustering, associations, and probabilistic inference.
The DTAI research group of the department of Computer Science, KU Leuven, presents a course that provides a gentle introduction to data science for professionals who need to analyze data themselves, interpret results obtained using data science techniques, or give guidance to data analysts. The course introduces the principles, techniques and methodology of data science. It provides the attendants with an overview of the wide variety of data science techniques available, insight in which techniques are useful for what kind of tasks, expertise with practical data science tools, and real-life case studies.
The target audience of this course consists of professionals who experience a need for a better understanding of data science: which tasks can be solved, which techniques can be used, which are their strengths and weaknesses.
Registration deadline: 20 January 2015
Course: 5-6 February 2015
I had a nice lunch with Prof. Dr Bart Baesens today at the MIM to discuss his recent book‘Analytics in a Big Data World: The Essential Guide to Data Science and its Applications’
One topic we discussed was knowledge transfer and certification.
Next to the recorded presentations already available on dataminingapps.com, the professor told me that his new course about Credit Risk Analytics would soon be released. Here is for you, in avant première, the content of this course that he has put together with SAS. This course will be available mid November 2014.
New e-learning course Credit Risk Analytics by professor Bart Baesens
The outline of the course is as follows:
Lesson 1: Introduction to Credit Scoring
Lesson 2: The Basel Capital Accords
Lesson 3: Preparing the data for credit scoring
Lesson 4: Classification for credit scoring
Lesson 5: Measuring the Performance of Credit Scoring Classification Models
Lesson 6: Variable Selection for Classification
Lesson 7: Issues in Scorecard Construction
Lesson 8: Defining Default Ratings and Calibrating PD
Lesson 9: LGD modeling
Lesson 10: EAD modeling
Lesson 11: Validation of Credit Risk Models
Lesson 12: Low Default Portfolios
Lesson 13: Stress testing