Training – KULeuven – Data Science in Practice – 5-6 February 2015

Data Science in Practice

INTRODUCTION

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.

IMPORTANT DATES

Registration deadline: 20 January 2015
Course: 5-6 February 2015

Click here to register.

More info: http://dsip.cs.kuleuven.be/

 

 

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Coursera – Process Mining -TU Eindhoven – starts Nov 12th

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https://www.coursera.org/course/procmin

 

Process Mining: Data science in Action

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.

Course at a Glance

4-6 hours of work / week
English
English subtitles