Skip to content
data mining
View original picture , opens in new tab/window

Data Mining for Industry

Published: 8 February 2022

Data Science and Machine Learning are some of the hottest concepts in business contexts during the 2000s. Nowadays, companies as well as public enterprises have a great interest in the area. Data Science is a combined study of different subjects together, including databases, warehousing, data architectures, business analysis, data mining, big data and more! This course will introduce you to data mining and how it might enable businesses to drive better business results by analyzing their different data using predictive and prescriptive tools.

About the course

The course provides knowledge to address various data science problems and datasets. At the end of the course, you will be able to define and clarify what data mining is about, be familiar with the standard CRISP data mining approach, be able to use one of the most frequently used toolkits in data mining, the rapidminer, and become familiar with several methods in data mining. A focus lies on machine learning techniques for classification, regression, and clustering. The course covers different examples of text and image analytics in association with real business scenarios.

Course coordinator / teacher

Marcus Liwicki, works as a professor and Head of Subjec,at the division of Embedded Intelligent Systems LAB, Department of Computer Science, Electrical and Space Engineering. His also Deputy Vice-Chancellor for applied AI, Luleå University of Technology. Marcus teaches Machine Learning, Advanced Data Mining,  Neural networks and learning machines, Introduction to Artificial Intelligence and Advanced deep learning.

Pernilla Tingvall

Pernilla Tingvall, Project Manager

Phone: +46 (0)920 491920
Organisation: VSS-SSR, Professional Support
Marcus Liwicki

Marcus Liwicki, Professor and Head of Subject, Deputy Vice-Chancellor for applied AI

Phone: +46 (0)920 491006
Organisation: Machine Learning, Embedded Intelligent Systems LAB, Department of Computer Science, Electrical and Space Engineering