Data Science / Data Mining / 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 (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.
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)
Frequent Itemset Mining Methods - The Apriori Algorithm - Market Basket Analysis - Pattern Evaluation Method
Decision Tree Induction - Attribute Selection Measures - Tree Pruning - Scalability and Decision Tree Induction - Rule-Based Classification
Bayes Classification Methods - Bayes´ Theorem –Naïve Bayes Algorithm – Bayesian Networks - Model Evaluation and Selection - Techniques to Improve Classification Accuracy
Classification by Backpropagation and Artificial Neural Networks - Support Vector Machines - Lazy Learners
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
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.
- 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.
