Introduction to Data Mining
Next Date
Fri, 6/11/2026
Participants
3 - 12
Duration
1 Day
Course Form
online, classroom in Basel or in-house ℹ
He has over 25 years of experience in the application of statistical methods in the process industry. He has coached many training sessions on various topics, including Machine Learning. His wide practical knowledge allows him to make even technical concepts easily understandable by participants.
Dr. Solot holds an M.Sc. and a Ph.D. in Applied Mathematics, both from the EPFL (Swiss Federal Institute of Technology, Lausanne).
Course Description
Do you suspect that untapped insights are hidden in your R&D, production, or marketing data? What if you could turn them into concrete advantages that move your work forward? This course will give you in only one day a clear overview of the key methods in Data Mining and Machine Learning. It is designed for managers and scientists who want to explore advanced data analysis and evaluate its potential application in their own work environment.
What Are You Going to Learn?
The course starts by introducing the fundamentals of Data Mining and Machine Learning and clarifying their differences with standard data analysis. The key aspects of data organization are addressed. Several widely used Data Mining and Machine Learning methods are then explained on the basis of applied examples, before the limits are discussed. Throughout the course, the emphasis lies on presenting a broad, easy‑to-grasp, global view, with minimal mathematical formalism.
Who Should Attend?
Managers and scientists
No previous knowledge in statistics required
Course Fees
- CHF 795.- per day + 8.1% VAT (incl. course documents and individual participant coaching during the practical exercises) for online courses
- CHF 865.- per day + 8.1% VAT (incl. course documents, individual participant coaching during the practical exercises, break refreshments and lunch) for classroom courses.
Which Topics Are Covered?
Introduction
What is data mining?
Data mining vs. statistical data analysis
Data Organization and Data Access
Obtaining data, data sources, data quality
Data warehouse
On-line analytical processing (OLAP)
Selected Algorithms
Learning approaches, modelling
Clustering
Classification and regression trees (CART)
Neural networks, genetic algorithms
Outlook
Limitations of data mining
Software aspects
Any Questions? Send Us a Message.
Since we are a small company, we can respond quickly to your questions. Just contact us either by telephone +41 61 686 98 77 or using the Contact form.