Data Science / Statistics / R Foundation / Statistics / Multivariate Analysis II

Statistics - Multivariate Analysis II

Details

ID 2858615
Classroom 3 days
Webinar 5 days
Method Lecture with examples and exercises.
Prequisite General knowledge of math
Audience Data Analysts

Services:

  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until 10 days before the training

Summary

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.

Training Dates

  • 2020-Oct-26 - Oct-30
  • 2021-Jan-04 - Jan-08
  • 2021-Mar-15 - Mar-19
  • 2021-May-24 - May-28

590 EUR +VAT

Location | Enrollment


Agenda

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 Mining

Data Preprocessing

Descriptive Data Summarization - Data Cleaning - Data Integration and Transformation - Data Reduction - Data Discretization and Concept Hierarchy Generation

Mining 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 Mining

Classification 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 Selection

Cluster Analysis

Types of Data in Cluster Analysis - Partitioning Methods: k-Means and k-Medoids - Hierarchical Methods: Agglomerative and Divisive Hierarchical Clustering

Mining 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 Data

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.

Publications
  • 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
Projects

- 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.

Research

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