Data Science / Statistics / IBM / SPSS / SPSS Statistics - Statistical Data Analysis 2 (Multivariate Analysis)
| ID | 2858623 |
|---|---|
| Classroom | 3 days 9:00-16:30 |
| Webinar | 5 days 9:00-12:30 |
| Method | Lecture with examples and exercises. |
| Prequisite | Basic knowledge of statistics |
| Audience | Data Analysts |
The IBM SPSS Statistics Premium Edition helps data analysts, planners, forecasters, survey researchers, program evaluators and database marketers – among others – to easily accomplish tasks at every phase of the analytical process. It includes a broad array of fully integrated Statistics capabilities and related products for specialized analytical tasks across the enterprise. The software will improve productivity significantly and help achieve superior results for specific projects and business goals. This training covers topics as the analysis of categorical and numeric data , linear and nonlinear models, decision trees, articial neural networks, forecasting and time series and more. For legal reasons (license management), this training can only be delivered on your hardware using your (test) license. Please contact us concerning dates and handling before the actual booking.
GLM Univariate - Linear Regression - Ordinal Regression - Curve Fitting - Partial Least Squares Regression
Defining Ranges - Selecting Cases - Stepwise Method - Classifying
Selecting Cases - Descriptive Statistics - Factor Extraction - Rotation - Factor Values
Creating Decision Trees: Selecting Categories, Validation Criteria for the Construction of the Tree - Tree Editor: Working with Large Trees, Controlling the Data Displayed in the Tree - Data Assumptions and Requirements - Construct a Valuation Model - Missing Values ??in Tree Models
Distance Calculations - Nearest Neighbor Analysis - Two-Step Cluster Analysis - Hierarchical Cluster Analysis - Cluster Analysis
Structure of Neural Networks - Multilayer Perceptron: Partitions, Architecture, Training, Editing - Radial Basis Function: Architecture, Training
Transforming Data - Time Series Modeling: Exponential Smoothing, Custom ARIMA Models, Output - Seasonal Decomposition - Spectra - Forecasts - Determining Significant Predictors
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.
