Data Science / Data Mining / Data Mining / Concepts and Techniques

Data Mining - Concepts and Techniques


ID 2858813
Classroom 2 days 9:00-16:30
Webinar 4 days 9:00-12:30
Method Lecture with examples and exercises.
Prequisite Basics in Statistics
Audience Information workers, IT professionals

Data Mining Concepts and Techniques Training


  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until one day before the training


Data Mining Concepts and Techniques TrainingData mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD) is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

Training Dates

  • 2021-Jan-11 - Jan-14
  • 2021-Mar-22 - Mar-25
  • 2021-May-31 - Jun-03

590 EUR +VAT

Location | Enrollment


Data Mining Concepts and Techniques Seminar
Introduction to Data Mining

Overview: Why Data Mining? What Is Data Mining? What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? Which Technologies Are Used? - Data Preparation: Data Objects and Attribute Types, Basic Statistical Descriptions of Data, Measuring Data Similarity and Dissimilarity - Data Preprocessing: Data Cleaning, Data Integration, Data Reduction, Data Transformation and Data Discretization - Data Warehousing and Online Analytical Processing (OLAP)

Data Mining for Frequent Patterns

Frequent Itemset Mining Methods - The Apriori Algorithm - Market Basket Analysis - Pattern Evaluation Method

Classification using Decision Trees

Decision Tree Induction - Attribute Selection Measures - Tree Pruning - Scalability and Decision Tree Induction - Rule-Based Classification

Classification using Probabilistic Approaches

Bayes Classification Methods - Bayes´ Theorem –Naïve Bayes Algorithm – Bayesian Networks - Model Evaluation and Selection - Techniques to Improve Classification Accuracy

Classification: Advanced Methods

Classification by Backpropagation and Artificial Neural Networks - Support Vector Machines - Lazy Learners

Cluster Analysis

Overview of Basic Clustering Methods - Measuring Data Similarity and Dissimilarity: Data Matrix versus Dissimilarity Matrix, Proximity Measures for Nominal, Ordinal, and Binary Attributes, Dissimilarity of Numeric Data - Partitioning Methods (k-Means and k-Medoids) - Hierarchical Methods: Agglomerative versus Divisive Hierarchical Clustering


Data Mining Concepts and Techniques Trainer

Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.

  • Grundlagen empirische Sozialforschung ISBN 978-3-939701-23-1
  • System und Systematik von Fragebögen ISBN 978-3-939701-26-2
  • Oracle PL/SQL ISBN 978-3-939701-40-8
  • MS SQL Server - T-SQL Programmierung und Abfragen ISBN 978-3-939701-69-9

- He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.


He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.

Data Mining Concepts and Techniques Trainer