Introduction to Data Mining
The term "Data Mining" refers to methods and algorithms which help to find structures and relationships in, typically very large and complex, data sets. This training session gives an overview of the most important methods in data mining. Aspects of data organization are briefly discussed and basic statistical techniques are presented (classification algorithms and neural networks).
What will you learn ?Do you think that valuable pieces of information might be hidden in your data base? How can you identify patterns and trends in your data, and understand relationships? The term "Data Mining" refers to those methods and algorithms that will help you to discover structures and relationships in, typically very large and complex, data sets. The course provides an overview of the most important methods in data mining. Aspects of data organization are discussed and the most often used statistical techniques, classification and neural networks, are presented. Their application is explained with practical examples. Further statistical methods relevant to data mining are presented in more detail in the course "Introduction to multivariate data analysis"
Who should attend ?
- For managers, scientists, marketing specialists
- No previous knowledge in statistics/mathematics required
Which topics are covered ?
| ||What is data mining? |
Data mining as part of Knowledge Discovery
Strengths and limitations of data mining
| Data mining vs. statistical data analysis|
| ||What is common to both? |
What are the differences?
Where are the difficulties?
| Data organization and data access|
| ||Obtaining data, data sources |
On-line analytical processing (OLAP)
| Selected statistical techniques (overview)|
| ||Exploratory methods, visualization |
Modelling, Classification and Regression Trees
Neural networks, genetic algorithms
Any questions ?
- Course duration: 1 day
- Participants: max. 12
- Costs: including complete course documentation, coffee and lunch: see registration form
- Dates: see registration form
- Further information: see contact page