Statistics - Multivariate Analysis II
Details
| ID | 2858615 |
| Duration | 3.0 days |
| Methods | Lecture with examples and exercises. |
| Prerequisites | General knowledge of math |
| Target group | Data Analysts |
Overview
Multivariate statistics is a form of statistics encompassing the simultaneous observation and analysis of more than one variable. The application of multivariate statistics is multivariate analysis. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical implementation of multivariate statistics to a particular problem may involve several types of univariate and multivariate analysis in order to understand the relationships between variables and their relevance to the actual problem being studied. This training is one part of a pair of courses on multivariate statistics. It helps you understand the techniques of complex and more advanced data analysis for marketing, controlling and engineering.
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Services
- Lunch / catering
- Help with hotel / travel
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- Flexible: free cancellation up to one day before
Content
Introduction to Data Mining
Data Mining Functionalities - Classification of Data Mining Systems - Data Mining Task Primitives - Integration of a Data Mining System with a Database or DataWarehouse System - Major Issues in Data MiningData Preprocessing
Descriptive Data Summarization - Data Cleaning - Data Integration and Transformation - Data Reduction - Data Discretization and Concept Hierarchy GenerationMining Frequent Patterns, Associations, and Correlations
Basic Concepts - Efficient and Scalable Frequent Itemset Mining Methods - Mining Various Kinds of Association Rules - From Association Mining to Correlation Analysis - Constraint-Based Association MiningClassification and Prediction
Issues Regarding Classification and Prediction - Classification by Decision Tree Induction - Bayesian Classification - Rule-Based Classification - Classification by Backpropagation - Support Vector Machines - Accuracy and Error Measures - Evaluating the Accuracy of a Classifier or Predictor: Holdout Method and Random Subsampling, Cross-validation - Model SelectionCluster Analysis
Types of Data in Cluster Analysis - Partitioning Methods: k-Means and k-Medoids - Hierarchical Methods: Agglomerative and Divisive Hierarchical ClusteringMining Time-Series and Sequence Data
Mining Time-Series Data: Trend Analysis, Similarity Search in Time-Series Analysis - Mining Sequence Patterns in Transactional Databases: Sequential Pattern Mining: Concepts and Primitives, Scalable Methods for Mining Sequential Patterns, Periodicity Analysis for Time-Related Sequence DataInstructor
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.Publications
- Grundlagen empirische Sozialforschung (Comelio Medien)
978-3-939701-23-1 - System und Systematik von Fragebögen (Comelio Medien)
978-3-939701-26-2 - Oracle PL/SQL (Comelio Medien)
978-3-939701-40-8 - MS SQL Server - T-SQL Programmierung und Abfragen (Comelio Medien)
978-3-939701-69-9