• Course code:63549
  • Credits:6
  • Semester: winter
  • Contents

In the 21st century our civilization is getting one global data based society. Large quantities of data are collected everywhere, from traditional fields like factories, medicine, and science to completely new areas like farming, personal health, home sensors, kitchen appliances, traffic, cars, and of course internet with vast collections of numeric and textual data. Yet, data itself means nothing. We are interested in the patterns and knowledge hidden in it, which enable us to comprehend trends and decide wisely.

Data mining is an interdisciplinary approach to knowledge discovery. It helps us handle enormous quantities of data and exploit data diversity. It encompasses many ideas and methods from machine learning, statistics, artificial intelligence, and databases. Many solutions from this field are already part of everyday life. For example, contents of shop shelves is optimized according to preferences of customers which articles to buy together. Internet search engines display hits different for each individual according to the pages they visit and clicks in their social network activities. Traffic lights in cities are tuned to traffic density patterns. Medical treatment guidelines are formed according to the history of successful recoveries. Insurance companies detect fraudulent claims. Various “big brothers” detect terrorist groups, etc.

The course contents offers a review of up to date applicative knowledge. The lectures introduce main approaches without theoretical background. Some important types of data reviewed, like tables, text, networks, and surveys. For many fields comprehensibility of prediction models is very important and want to understand the causes for different phenomena.  Therefore course tackles visualization of data, trends, patterns, and predictive models. In the lab work the gained knowledge will be put into practice by using open-source data mining and visualization tools.

As a saying goes: we are drowning in data and starving for knowledge. This course can throw you a life belt and help you swim in the sea of data. It will also teach you how to harvest some knowledge, so that you don’t starve any more.

  • Study programmes
  • Distribution of hours per semester
45
hours
lectures
30
hours
laboratory work
  • Professor