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30 SPSS Weiterbildungen auf Jobbörse.de

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Advanced Statistical Analysis Using IBM SPSS Statistics (V26) [0G09BG]

Global Knowledge Network Netherlands B.V. | VIRTUAL TRAINING CENTER

OVERVIEW: This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases. Virtueel en Klassikaal™. Virtueel en Klassikaal™ is een eenvoudig leerconcept en biedt een flexibele oplossing voor het volgen van een klassikale training. Met Virtueel en Klassikaal™ kunt u zelf beslissen of u een klassikale training virtueel (vanuit huis of kantoor) of fysiek op locatie wilt volgen. ‒ +

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Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2) SPVC [0E039G]

Global Knowledge Network Netherlands B.V. | Nieuwegein

This course presents advanced models available in IBM SPSS Modeler. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core factors, referred to as components or factors. The next topics focus on supervised models, including Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed on how to analyze text data, combine individual models into a single model, and how to enhance the power of IBM SPSS Modeler by adding external models, developed in Python or R, to the Modeling palette. If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. ‒ +

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IBM SPSS Modeler Foundations (V18.2) SPVC [0E069G]

Global Knowledge Network Netherlands B.V. | Nieuwegein

OVERVIEW: This course provides the foundations of using IBM SPSS Modeler and introduces the participant to data science. The principles and practice of data science are illustrated using the CRISP-DM methodology. The course provides training in the basics of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and introduces the student to modeling. OBJECTIVES: Please refer to course overview. CONTENT: Introduction to IBM SPSS Modeler; Introduction to data science; Describe the CRISP-DM methodology; Introduction to IBM SPSS Modeler; Build models and apply them to new data Collect initial data; Describe field storage; Describe field measurement level; Import from various data formats; Export to various data formats Understand the data; Audit the data; Check for invalid values; Take action for invalid values; Define blanks Set the unit of analysis; Remove duplicates; Aggregate data; Transform nominal fields into flags; Restructure data Integrate data; Append datasets; Merge datasets; Sample records Transform fields; Use the Control Language for Expression Manipulation; Derive fields; Reclassify fields; Bin fields Further field transformations; Use functions; Replace field values; Transform distributions Examine relationships; Examine the relationship between two categorical fields; Examine the relationship between a categorical and continuous field; Examine the relationship between two continuous fields Introduction to modeling; Describe modeling objectives; Create supervised models; Create segmentation models Improve efficiency; Use database scalability by SQL pushback; Process outliers and missing values with the Data Audit node; Use the Set Globals node; Use parameters; Use looping and conditional execution. ‒ +

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Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) SPVC [0E079G]

Global Knowledge Network Netherlands B.V. | Nieuwegein

OVERVIEW: This course provides an introduction to supervised models, unsupervised models, and association models. This is an application-oriented course and examples include predicting whether customers cancel their subscription, predicting property values, segment customers based on usage, and market basket analysis. OBJECTIVES: Please refer to course overview. CONTENT: Introduction to machine learning models; Taxonomy of machine learning models; Identify measurement levels; Taxonomy of supervised models; Build and apply models in IBM SPSS Modeler Supervised models: Decision trees; CHAID; CHAID basics for categorical targets; Include categorical and continuous predictors; CHAID basics for continuous targets; Treatment of missing values Supervised models: Decision trees; C&R Tree; C&R Tree basics for categorical targets; Include categorical and continuous predictors; C&R Tree basics for continuous targets; Treatment of missing values Evaluation measures for supervised models; Evaluation measures for categorical targets; Evaluation measures for continuous targets Supervised models: Statistical models for continuous targets; Linear regression; Linear regression basics; Include categorical predictors; Treatment of missing values Supervised models: Statistical models for categorical targets; Logistic regression; Logistic regression basics; Include categorical predictors; Treatment of missing values Supervised models: Black box models; Neural networks; Neural network basics; Include categorical and continuous predictors; Treatment of missing values Supervised models: Black box models; Ensemble models; Ensemble models basics; Improve accuracy and generalizability by boosting and bagging; Ensemble the best models Unsupervised models: K-Means and Kohonen; K-Means basics; Include categorical inputs in K-Means; Treatment of missing values in K-Means; Kohonen networks basics; Treatment of missing values in Kohonen Unsupervised models: Two Step and Anomaly detection; Two Step basics; Two Step assumptions; Find the best segmentation model automatically; Anomaly detection basics; Treatment of missing values Association models: Apriori; Apriori basics; Evaluation measures; Treatment of missing values Association models: Sequence detection; Sequence detection basics; Treatment of missing values Preparing data for modeling; Examine the quality of the data; Select important predictors; Balance the data. ‒ +

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Advanced Statistical Analysis Using IBM SPSS Statistics (V26) - SPVC [0K09BG]

Global Knowledge Network Netherlands B.V. | Nieuwegein

This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases. OBJECTIVES: Introduction to advanced statistical analysis; Grouping variables with Factor Analysis and Principal Components Analysis; Grouping cases with Cluster Analysis; Predicting categorical targets with Nearest Neighbor Analysis; Predicting categorical targets with Discriminant Analysis; Predicting categorical targets with Logistic Regression; Predicting categorical targets with Decision Trees; Introduction to Survival Analysis; Introduction to Generalized Linear Models; Introduction to Linear Mixed Models. ‒ +

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Statistical Analysis Using IBM SPSS Statistics (V26) SPVC [0K51BG]

Global Knowledge Network Netherlands B.V. | Nieuwegein

OVERVIEW: Blended Live van Global Knowledge combineert trainingen van docenten met de flexibiliteit van online leren en stelt je daarmee in staat om kritieke vaardigheden effectiever te verkrijgen, terwijl je zelf kunt kiezen wanneer en waar je traint. Blended Live biedt je: Online leren; Geplande virtuele of klassikale trainingssessies; Begeleiding door mentoren; Labs en praktijkoefeningen; Begeleiding om het leren gestructureerd te voltooien. OBJECTIVES: CONTENT. ‒ +

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IBM SPSS Statistics Essentials (V26) - SPVC [0K53BG]

Global Knowledge Network Netherlands B.V. | Nieuwegein

OVERVIEW: Blended Live van Global Knowledge combineert trainingen van docenten met de flexibiliteit van online leren en stelt je daarmee in staat om kritieke vaardigheden effectiever te verkrijgen, terwijl je zelf kunt kiezen wanneer en waar je traint. Blended Live biedt je: Online leren; Geplande virtuele of klassikale trainingssessies; Begeleiding door mentoren; Labs en praktijkoefeningen; Begeleiding om het leren gestructureerd te voltooien. OBJECTIVES: CONTENT. ‒ +

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IBM 0A079G - Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)

Cegos Integrata GmbH | im Büro, Homeoffice, Meetingraum

Course Outline: Introduction to machine learning models; Taxonomy of machine learning models; Identify measurement levels; Taxonomy of supervised models; Build and apply models in IBM SPSS Modeler. Supervised models: Decision trees; CHAID. CHAID basics for categorical targets; Include categorical and continuous predictors; CHAID basics for continuous targets; Treatment of missing values. Supervised models: Decision trees; C&R Tree. C&R Tree basics for categorical targets; Include categorical and continuous predictors; C&R Tree basics for continuous targets; Treatment of missing values; Evaluation measures for supervised models; Evaluation measures for categorical targets; Evaluation measures for continuous targets. Supervised models: Statistical models for continuous targets; Linear regression. ‒ +

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IBM 0A039G - Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2)

Cegos Integrata GmbH | im Büro, Homeoffice, Meetingraum

Taxonomy of models; Overview of supervised models; Overview of models to create natural groupings; Group fields: Factor Analysis and Principal Component Analysis; Factor Analysis basics; Principal Components basics; Assumptions of Factor Analysis; Key issues in Factor Analysis; Improve the interpretability; Factor and component scores; Predict targets with Nearest Neighbor Analysis; Nearest Neighbor Analysis basics; Key issues in Nearest Neighbor Analysis; Assess model fit; Explore advanced supervised models; Support Vector Machines basics; Random Trees basics; XGBoost basics; Introduction to Generalized Linear Models; Generalized Linear Models; Available distributions; Available link functions; Combine supervised models; Combine models with the Ensemble node; Identify ensemble methods for categorical targets; Identify ensemble methods for flag targets; Identify ensemble methods for continuous targets; Meta-level modeling; Use external machine learning models; IBM SPSS Modeler Extension nodes; Use external machine learning programs in IBM SPSS Modeler; Analyze text data; Text Mining and Data Science; Text Mining applications; Modeling with text data. ‒ +

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IBM 0A069G - IBM SPSS Modeler Foundations (V18.2)

Cegos Integrata GmbH | im Büro, Homeoffice, Meetingraum

Course Outline: Introduction to IBM SPSS Modeler. Introduction to data science; Describe the CRISP-DM methodology; Introduction to IBM SPSS Modeler; Build models and apply them to new data. Collect initial data. Describe field storage; Describe field measurement level; Import from various data formats; Export to various data formats. Understand the data. Audit the data; Check for invalid values; Take action for invalid values; Define blanks. Set the unit of analysis. Remove duplicates; Aggregate data; Transform nominal fields into flags; Restructure data. Integrate data. Append datasets; Merge datasets; Sample records. Transform fields. Use the Control Language for Expression Manipulation; Derive fields; Reclassify fields; Bin fields. ‒ +

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Weiterbildung im Bereich SPSS auf www.jobbörse.de

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