IBM Cognos Analytics Reporting: Grundlagen der ... · IBM Cognos Analytics Dashboard & Story Online...

IBM Cognos Analytics Reporting: Grundlagen der BerichtserstellungOnline oder Präsenz

Dauer : 2 Tage (12 Stunden) Nr. : 54153

Preis : 1.290,00 € netto1.496,40 € inkl. 16 % MwSt.

Inhouse-Paket : Auf Anfrage

Der Kurs „IBM Cognos Analytics Reporting: Grundlagen der Berichtserstellung“ ist ein zweitägiges Praxistraining fürBerichtsersteller. Anhand von Beispielen und Trainingsaufgaben wird die Verwendung des Produktes nähergebracht.

Nach Abschluss des Trainings verfügen Sie über Basiskenntnisse zur Erstellung professioneller und interaktiver Berichte.

Wer sollte teilnehmen:

Zielgruppe

Dieser Kurs richtet sich an Berichtsautoren, die Techniken der Reporterstellung und Verwaltung von professionellenBerichten auf Basisrelationaler Datenmodelle erlernen möchten.

Voraussetzungen

Verständnis für Daten und betriebswirtschaftliche Anforderungen.

Trainingsprogramm

Übersicht IBM Cognos Analytics:

Allgemeiner Überblick zu IBM Cognos Analytics

Grundlagen zum IBM Cognos Analytics Reporting:

Einführung ins IBM Cognos Analytics ReportingErkunden von IBM Cognos Analytics Reporting sowie verschiedener BerichtstypenErstellen von einfachen Listen und KreuztabellenBerichtserstellung und -formatierung mithilfe von Gruppierung, Kopf- und FußzeilenFokussierung von Berichtsinhalten durch DatenfilterBerichtserweiterung und -aufwertung durch Verwendung von BerechnungenBerichtsoptimierung mit umfassenden Formatierungs- und LayouttechnikenGrafische Darstellung von InhaltenParameterweitergabe für die Filterung in Zielberichten per Drill-through-DefinitionEingrenzen von Daten mithilfe von EingabeaufforderungenOptimierung der Eingabeaufforderungsseiten durch kaskadierende Quellen

Schulungsmethode

Vortrag, Demonstrationen und praktische Übungen.

Online Anmeldung:Kundenservice | Tel. 0711 62010 100 | Fax: 0711 62010 267 | [email protected]://www.integrata-cegos.de/54153Generated on 02/10/2020

Termine und Orte

Düsseldorf

26. Okt bis 27. Oktvom 26. Okt bis 27. Oktvom 26. Okt bis 27. Okt

online Training


München

15. Mär bis 16. Märvom 15. Mär bis 16. Märvom 15. Mär bis 16. Mär

Frankfurt

01. Jul bis 02. Julvom 01. Jul bis 02. Julvom 01. Jul bis 02. Jul

IBM Planning Analytics (TM1): DatenmodellierungOnline oder Präsenz




Der Kurs „IBM Planning Analytics: Datenmodellierung“ ist ein dreitägiges Aufbautraining für Planungsanwender. DieTeilnehmer erlernen, wie sie Unternehmensdaten in IBM Planning Analytics bereitstellen. Außerdem werden diegrundlegenden Funktionen zur Datenmodellierung anhand von Planszenarien und Trainingsaufgaben erarbeitet.

In diesem Aufbautraining erweitern Sie Ihr IBM Planning Analytics-Wissen um die Möglichkeiten der Modellierung vonDimensionen und Würfeln. Sie lernen Abhängigkeiten zwischen Würfeln zu schaffen, die Datenbeschaffung zuautomatisieren und die workflowgesteuerte Planung einzurichten.


Zielgruppe

Planungsanwender.

Voraussetzungen

Verständnis für IT-Systeme, Daten und betriebswirtschaftliche AnforderungenTeilnahme an dem Kurs Nr. 54151 „IBM Cognos Analytics Basistraining“ oder vergleichbares Wissen

Trainingsprogramm

Überblick über die Architektur und die Komponenten von IBM Planning AnalyticsErstellen und Bearbeiten von DimensionenErstellen von Cubes und AnsichtenLaden und Verwalten von Daten in IBM Planning AnalyticsVerwendung von Rules und FeedersSkripte zum Laden von Daten in ein ModellErstellen von Subset, Ansichten und FilternVerwendung der Workflowkomponente (optional)Integration in IBM Cognos Analytics (optional)

Schulungsmethode



Termine und Orte

Frankfurt

08. Feb bis 10. Febvom 08. Feb bis 10. Febvom 08. Feb bis 10. Feb

Berlin


IBM Cognos Analytics (V.11x) BasistrainingOnline oder Präsenz




Das IBM Cognos Analytics Basistraining ist ein dreitägiger, von einem Dozenten geführter Basiskurs zur Einführung in dieNutzung von IBM Cognos Analytics. Die Teilnehmer erlernen von Grund auf die ersten Schritte im Umgang mit dieser BI-Lösung. Zudem werden grundlegende praktische Beispiele im IBM Cognos Dashboard und IBM Cognos Bericht erarbeitet.

Nach Abschluss des Trainings sind Sie mit den Funktionalitäten Ihrer Reporting-Umgebung vertraut. Sie sind in der Lage,eigene leistungsfähige und interaktive Arbeitsbereiche zu erzeugen und einfache und übersichtliche Dashboards im IBMCognos Dashboard zu erstellen. Weiterhin verfügen Sie über Basiskenntnisse, um erste professionelle und interaktiveBerichte im IBM Cognos Analytics Reportings zu erstellen.


Zielgruppe

Dieser Kurs richtet sich an Anwender und Berichtsersteller.

Voraussetzungen


Trainingsprogramm

Übersicht IBM Cognos Analytics:

Datenherkunft (dimensional & relational) und ArchitekturNavigation, Verwaltung von Objekten und persönlichen Einstellungen im IBM Cognos Portal ‚Willkommen‘Anlegen und Verwalten von Ordnern, Zeitplänen, Aufgaben und BerichtsansichtenBerichtsexporte in PDF, Excel, CSV

Einführung ins IBM Cognos Dashboard:

Einführung in die Nutzung von IBM Cognos DashboardErstellen einfacher DashboardsNutzen neuer Visualisierungen zur besseren TransparenzEinbindung von Berichtselementen diverser BerichteErstellen einfacher Interaktionsfelder zur besseren NavigationDatenanalyse über grafische Auswertungen und Diagramme

Grundlagen des IBM Cognos Analytics Reportings:

Einführung ins IBM Cognos Analytics ReportingErkunden von IBM Cognos Analytics Reporting sowie verschiedener BerichtstypenErstellung von einfachen Listen und KreuztabellenBerichtserstellung und -formatierung mithilfe von Gruppierung, Kopf- und FußzeilenFokussierung von Berichtsinhalten durch DatenfilterBerichtserweiterung und -aufwertung durch Verwendung von BerechnungenBerichtsoptimierung mit umfassenden Formatierungs- und LayouttechnikenGrafische Darstellung von InhaltenParameterweitergabe für die Filterung in Zielberichten per Drill-through-DefinitionEingrenzen von Daten mithilfe von EingabeaufforderungenOptimierung der Eingabeaufforderungsseiten durch kaskadierende Quellen

Einführung ins IBM Cognos for Microsoft Office (optional):

Installation & Einrichtung von IBM Cognos in Microsoft Office-ProdukteNutzung von IBM Cognos for Microsoft OfficeImport von IBM Cognos Analytics-Elementen in MS Excel, MS Word und MS PowerPoint

Schulungsmethode



Termine und Orte

online Training

12. Okt bis 14. Okt Garantieterminvom 12. Okt bis 14. Oktvom 12. Okt bis 14. Okt

Frankfurt


München


IBM Cognos Analytics Dashboard & StoryOnline oder Präsenz

Dauer : 1 Tag ( 7 Stunden) Nr. : 54159

Preis : 790,00 € netto916,40 € inkl. 16 % MwSt.


Der Kurs „IBM Cognos Analytics Dashboard & Story“ ist ein eintägiges Praxistraining für IBM Cognos-Anwender, die ihrenIBM Cognos-Arbeitsbereich interaktiv und individuell erweitern und mit eigenen einfachen Berichten und Analysendarstellen wollen. Ziel ist dieNutzung verschiedener Datenquellen, um die Ergebnisse in einer Dashboard Oberfläche darstellen zu lassen.

Nach Abschluss des Trainings verfügen Sie über Kenntnisse, eigene Dashboards zu erstellen. Sie sind in der Lage,bestehende, neue und abgewandelte Berichtselemente zu verwenden. Zudem sind Sie mit den Funktionalitäten zurErstellung von Ad-hoc-Analysen und einfachen Reports vertraut und können diese mit geeigneten Visualisierungendarstellen.


Zielgruppe

IBM Cognos-Anwender.

Voraussetzungen


Trainingsprogramm

Einführung in die Nutzung von IBM Cognos Analytics DashboardEinführung in die Nutzung von IBM Cognos Workspace StoryErstellen und Filtern von ArbeitsbereichenEinbindung von Berichtselementen diverser BerichteDatenuntersuchung mittels neuer VisualisierungenErstellen einfacher DashboardsEingrenzung von Daten durch FilterAnwendung von personalisierten AnsichtenAnbindung anderer Medien, Webseiten, Bilder, Formen und Texte

Schulungsmethode



Termine und Orte

München

19. Febvom 19. Feb bis 19. Febvom 19. Feb bis 19. Feb

Frankfurt

25. Junvom 25. Jun bis 25. Junvom 25. Jun bis 25. Jun

IBM Cognos Analytics Reporting: Berichtserstellung auf Basismultidimensionaler Daten

Online oder Präsenz




Der Kurs „IBM Cognos Analytics Reporting: Berichtserstellung auf Basis multidimensionaler Daten“ ist ein zweitägigesPraxistraining für fortgeschrittene Berichtsersteller. Ziel ist es, das Wissen rund um die Berichtserstellung auf Basismultidimensionaler Daten zu vertiefen.

Nach Abschluss des Trainings können Sie dimensionale Konzepte verstehen und anwenden. Sie kennen eine Reihe vonFunktionen und sind in der Lage, multidimensionale Datenquellen in Berichten zu verwenden.


Zielgruppe

Fortgeschrittene Berichtsersteller.

Voraussetzungen

Grundlagen der Berichtserstellung (Teilnahme an einem der beiden Kurse „IBM Cognos Analytics Basistraining“ oder„IBM Cognos Analytics Reporting: Grundlagen der Berichtserstellung“ oder vergleichbares Wissen).

Trainingsprogramm

Einführung in dimensionale KonzepteDimensionale und relationale Modelltypen vergleichenDatensätze in Berichten definieren und verwendenDimensionale Funktionen für Mitglieder, Datensätze und Tupel untersuchenEingrenzen von dimensionalen DatenBerichte anhand von Mitgliedern eingrenzenBerichte anhand von Kennzahlen eingrenzenBerechnungen und dimensionale FunktionenNavigieren in dimensionalen HierarchienBerichte auf aktuelle Zeiträume eingrenzenDaten mit entsprechenden Zeiträumen vergleichenFortgeschrittene Drilltechniken und erweiterte MitgliedersätzeKonfigurieren von Drill-through-Berichten

Schulungsmethode



Termine und Orte

Frankfurt

26. Nov bis 27. Novvom 26. Nov bis 27. Novvom 26. Nov bis 27. Nov


online Training


München

25. Feb bis 26. Febvom 25. Feb bis 26. Febvom 25. Feb bis 26. Feb

IBM Cognos Analytics Reporting: Aktive Berichte erstellenOnline oder Präsenz




Der Kurs „IBM Cognos Analytics Reporting: Aktive Berichte erstellen“ ist ein eintägiges Praxistraining für fortgeschritteneBerichtsersteller. Ziel ist es, das Wissen rund um die Berichtserstellung von „aktiven Berichten“ zu vertiefen, uminteraktive Berichte zu erstellen, die offline verwendet werden können.

Nach Abschluss des Trainings haben Sie Kenntnisse über einen neuen Teilbereich des IBM Cognos Analytics Reportingserlangt: IBM Cognos Analytics Active Reports. Sie sind in der Lage, interaktive Berichte zu erstellen und zu optimieren.


Zielgruppe

Fortgeschrittene Berichtsersteller.

Voraussetzungen

Grundlagen der Berichtserstellung (Teilnahme an einem der beiden Kurse „IBM Cognos Analytics Basistraining“ oder„IBM Cognos Analytics Reporting: Grundlagen der Berichtserstellung“ oder vergleichbares Wissen).

Trainingsprogramm

Einführung und Nutzung von „Active Reports“Filtern und Auswählen in „Active Reports“Erweiterte Funktionen für „Active Reports“ verwendenDrill-through in „Active Reports“Drill-up und Drill-down in „Active Reports“Kaskadierende Abfragen in „Active Reports“

Schulungsmethode



Termine und Orte

Düsseldorf

16. Novvom 16. Nov bis 16. Novvom 16. Nov bis 16. Nov

online Training

16. Novvom 16. Nov bis 16. Novvom 16. Nov bis 16. Nov

München

09. Aprvom 09. Apr bis 09. Aprvom 09. Apr bis 09. Apr

Frankfurt

02. Julvom 02. Jul bis 02. Julvom 02. Jul bis 02. Jul

IBM Cognos Analytics Reporting: Berichtserstellung für FortgeschritteneOnline oder Präsenz




Der Kurs „IBM Cognos Analytics Reporting: Berichtserstellung für Fortgeschrittene“ ist ein dreitägiges Praxistraining fürBerichtsersteller und baut auf Kenntnissen des IBM Cognos Analytics Basistrainings auf. Ziel ist es, das Wissen rund umdie Berichtserstellung mit demIBM Cognos Report Studio zu vertiefen.

Nach Abschluss des Trainings verfügen Sie über weiterführende und fortgeschrittene Techniken der Reporterstellung. Siekönnen Ihr Wissen in der Nutzung von aktiven Berichten anwenden und vertiefen. Zudem sind Sie in der Lage, ersteAgenten in IBM Cognos Analytics

Event zu erstellen und zu automatisieren.


Zielgruppe

Berichtsersteller.

Voraussetzungen

Grundlagen der Berichtserstellung (Teilnahme an einem der beiden Kurse „IBM Cognos Analytics Basis Training“ oder„IBM Cognos Analytics Reporting: Grundlagen der Berichtserstellung“ oder vergleichbares Wissen).

Trainingsprogramm

Vertiefung der Kenntnisse in Filter- und AggregationsmethodenVerwenden und Verstehen von komplexen BerichtsabfragenErstellen von Beziehungen zwischen BerichtsabfragenAbfragemodelle erstellen und mit dem Berichts-Layout verbindenBerichtszustellung durch automatisierte ZielgruppenverteilungAutomatische Berichtsverteilung ins DateisystemErstellen erweiterter dynamischer Berichte mit Lesezeichen und InhaltsverzeichnissenDesign effektiver Berichte durch fortgeschrittene BerichtstechnikenVerbesserung der Benutzerinteraktionen mit HTML-ElementenEinführung und Nutzung von „Active Reports“Filtern und Auswählen in „Active Reports“Erweiterte Funktionen für „Active Reports“ verwendenDrill-through in „Active Reports“Drill-up und Drill-down in „Active Reports”

Kaskadierende Abfragen in „Active Reports“Einführung in das IBM Cognos Analytics Event (optional)

Schulungsmethode



Termine und Orte

Düsseldorf


online Training


Frankfurt


München


IBM Cognos Framework Manager: Erstellen von Metadaten-ModellenOnline oder Präsenz




Der Kurs "IBM Cognos Framework Manager: Erstellen von Metadaten-Modellen" ist ein zweitägiges Praxistraining fürModellentwickler. Ziel ist es, Wissen im Aufbau von Metadaten-Modellen zu erwerben und Packages innerhalb von IBMCognos BI zu veröffentlichen.

Nach dem Abschluss des Trainings sind Sie in der Lage, Metadaten im IBM Cognos Framework Manager zu modellierenund Datenquellen so zu strukturieren, dass diese von den Berichtserstellern einfach genutzt werden können. Sieverstehen das Prinzip professioneller Metadaten-Strukturen und wenden diese in der Praxis an.


Zielgruppe

Die Schulung richtet sich an Modellentwickler.

Voraussetzungen

Verständnis für IT-Systeme, Daten und betriebswirtschaftliche Anforderungen.

Trainingsprogramm

Überblick zur SystemarchitekturAufbau performanter Datenstrukturen

Erstellen eines Projektes und unternehmensspezifischer Ansichten

Arbeiten mit Namespaces

Wiederverwendbare Metadaten

Erstellen von Berechnungen und Filtern

Multidimensionale Datenquellen (OLAP, DMR)

Rechte- und Rollenkonzepte integrieren

Package-Administration

Modellwartung und -erweiterung

Verwendung von Determinanten

Schulungsmethode



Termine und Orte

Hamburg


online Training


Frankfurt

07. Jan bis 08. Janvom 07. Jan bis 08. Janvom 07. Jan bis 08. Jan

München

05. Aug bis 06. Augvom 05. Aug bis 06. Augvom 05. Aug bis 06. Aug

IBM Planning Analytics (TM1): BasistrainingOnline oder Präsenz




Nach Abschluss dieses Trainings sind Sie mit den Basiskomponenten von IBM Cognos Planning Analytics vertraut. Siesind in der Lage, die Unternehmensdaten zu analysieren und zu planen sowie statische und dynamische Berichte zuerstellen und zu veröffentlichen.


Zielgruppe

Der Kurs „IBM Planning Analytics Basistraining“ ist ein zweitägiges Praxistraining für Planungsanwender zurEinführung in die Nutzung von IBM Planning Analytics. Die Teilnehmer erlernen anhand von Planszenarien undTrainingsaufgaben von Grund auf die ersten Schritte im Umgang mit der Planungsanwendung.

Voraussetzungen

Verständnis für IT-Systeme, Daten und betriebswirtschaftliche Anforderungen.

Trainingsprogramm

Überblick über die Architektur und die Komponenten von Planning AnalyticsBearbeiten von DimensionenErstellen von AnsichtenDatenanalyse mit IBM Planning Analytics Web und IBM PAx (Planning Analytics for MS Excel)Verwendung von Subset, Ansichten und FilternSchreiben von Daten in CubesArbeiten mit SandboxesErstellen dynamischer Formulare mithilfe von Active FormsIntegration in IBM Cognos Analytics (optional)

Schulungsmethode



Termine und Orte

Frankfurt


Berlin

05. Aug bis 06. Augvom 05. Aug bis 06. Augvom 05. Aug bis 06. Aug

Big Data - Workshop für FührungskräfteOnline oder Präsenz



Nach dem Besuch dieses Seminars haben Sie ein generelles Verständnis der aktuellen Möglichkeiten undVorgehensweisen im Kontext des Schlagwortes "Big Data". Sie können Potentiale in Ihrer Organisation erkennen undzielgerichtet Projekte auf den Weg bringen.


Zielgruppe

Führungskräfte

Voraussetzungen

Voraussetzung für die erfolgreiche Teilnahme ist ein allgemeines Verständnis für IT und IT-Management.

Trainingsprogramm

Methodische und technische Evolution - von BI zu Big Data und cognitive Computing

Data Warehouse und Business IntelligenceEinsatzszenarien, Nutzen und GrenzenAnalyse großer polystrukturierter DatenmengenBatch- und Echtzeitverarbeitungneue Geschäftsmodelle, neue Herausforderungen

Human readable II - Visual Analytics und Information Design

Anwendungsbeispiele und MusterTraue keiner Statistik, die du nicht selber gefälscht hastStatistik und GrafikenData ScienceMustersuche, Optimierung und Prognose

Cognitive Computing - Natürliche Sprache als Schnittstelle

Aufbau von Teams und Projekten im Bereich Big Data

Projektmanagement

Schulungsmethode

Workshop


Termine und Orte

Hamburg


IBM 0A018G - Data science without a Ph.D. Using IBM SPSS Modeler(v18.1.1)

Präsenztraining




Overview

This course focuses on reviewing concepts of data science, where participants will learn the stages of a data scienceproject. Topics include using automated tools to prepare data for analysis, build models, evaluate models, and deploymodels. To learn about these data science concepts and topics, participants will use IBM SPSS Modeler as a tool.


Zielgruppe

Audience

• Business Analysts• Data Scientists• Participants who want to get started with data science

Voraussetzungen

Prerequisites

• It is recommended that you have an understanding of your business data

Trainingsprogramm

Course Outline

1: Introduction to data science and IBM SPSS Modeler • Explain the stages in a data-science project, using the CRISP-DM methodology • Create IBM SPSS Modeler streams • Build and apply a machine learning model2: Setting measurement levels

• Explain the concept of field measurement level • Explain the consequences of incorrect measurement levels • Modify a field's measurement level3: Exploring the data • Audit the data • Check for invalid values • Take action for invalid values • Impute missing values • Replace outliers and extremes4: Using automated data preparation • Automatically exclude low quality fields • Automatically replace missing values • Automatically replace outliers and extremes5: Partitioning the data • Explain the rationale for partitioning the data • Partition the data into a training set and testing set6: Selecting predictors • Automatically select important predictors (features) to predict a target • Explain the limitations of automatically selecting features7: Using automated modeling • Find the best model for categorical targets • Find the best model for continuous targets • Explain what an ensemble model is8: Evaluating models • Evaluate models for categorical targets • Evaluate models for continuous targets9: Deploying models • List two ways to deploy models • Export scored data

Objective

Please refer to course overview

Schulungsmethode

presentation, discussion, hands-on exercises


Termine und Orte

Hamburg

23. Nov

Leinfelden-Echterdingen

23. Nov

Krefeld

11. Jan

IBM 0A028G - Introduction to Time Series Analysis Using IBM SPSSModeler (v18.1.1)

Präsenztraining




Overview

This course gets you up and running with a set of procedures for analyzing time series data. Learn how to forecast usinga variety of models, including regression, exponential smoothing, and ARIMA, which take into account differentcombinations of trend and seasonality. The Expert Modeler features will be covered, which is designed to automaticallyselect the best fitting exponential smoothing or ARIMA model, but you will also learn how to specify your own custommodels, and also how to identify ARIMA models yourself using a variety of diagnostic tools such as time plots andautocorrelation plots.


Zielgruppe

Audience

Roles: Business Analyst, Data ScientistSpecifically, this is an introductory course for:• Anyone who is interested in getting up to speed quickly and efficiently using the IBM SPSS Modeler forecastingcapabilities

Voraussetzungen

Prerequisites

• Familiarity with the IBM SPSS Modeler environment (creating, editing, opening, and saving streams).• General knowledge of regression analysis is recommended but not required

Trainingsprogramm

Course Outline

1: Introduction to time series analysis• Explain what a time series analysis is• Describe how time series models work• Demonstrate the main principles behind a time series forecasting model2: Automatic forecasting with the Expert Modeler• Examine fit and error• Examine unexplained variation• Examine how the Expert Modeler chooses the best fitting time series model3: Measuring model performance• Discuss various ways to evaluate model performance• Evaluate model performance of an ARIMA model• Test a model using a holdout sample4: Time series regression• Use regression to fit a model with trend, seasonality and predictors• Handling predictors in time series analysis• Detect and adjust the model for autocorrelation• Use a regression model to forecast future values5: Exponential smoothing models• Types of exponential smoothing models• Create a custom exponential smoothing model• Forecast future values with exponential smoothing• Validate an exponential smoothing model with future data6: ARIMA modeling• Explain what ARIMA is• Learn how to identify ARIMA model types• Use sequence charts and autocorrelation plots to manually identify an ARIMA model that fits the data• Check your results with the Expert Modeler

Objective


Schulungsmethode



Termine und Orte

Hamburg

24. Nov

München

24. Nov

Krefeld

12. Jan

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

Präsenztraining




Overview

This course presents advanced models available in IBM SPSS Modeler. The participant is first introduced to a techniquenamed PCA/Factor, to reduce the number of fields to a number of core factors, referred to as components or factors. Thenext topics focus on supervised models, including Support Vector Machines, Random Trees, and XGBoost. Methods arereviewed on how to analyze text data, combine individual models into a single model, and how to enhance the power ofIBM SPSS Modeler by adding external models, developed in Python or R, to the Modeling palette.


Zielgruppe

Audience

Data scientistsBusiness analystsExperienced users of IBM SPSS Modeler who want to learn about advanced techniques in the software

Voraussetzungen

Prerequisites

Knowledge of your business requirementsRequired: IBM SPSS Modeler Foundations (V18.2) course (0A069G/0E069G) or equivalent knowledge of how toimport, explore, and prepare data with IBM SPSS Modeler v18.2, and know the basics of modeling.Recommended: Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) course(0A079G/0E079G), or equivalent knowledge or experience with the product about supervised machine learningmodels (CHAID, C&R Tree, Regression, Random Trees, Neural Net, XGBoost), unsupervised machine learningmodels (TwoStep Cluster), and association machine learning models such as APriori.

Trainingsprogramm

Course Outline

Introduction to advanced machine learning models• 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

Objective

Introduction to advanced machine learning models • 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

Schulungsmethode



Termine und Orte

Hamburg

25. Nov

München

25. Nov

Krefeld

13. Jan

IBM 0A058G - Advanced Data Preparation Using IBM SPSS Modeler(v18.1.1)

Präsenztraining




Overview

This course covers advanced topics to aid in the preparation of data for a successful data science project. You will learnhow to use functions, deal with missing values, use advanced field operations, handle sequence data, apply advancedsampling methods, and improve efficiency.


Zielgruppe

Audience

This advanced course is intended for anyone who wants to become familiar with the full range of techniquesavailable in IBM SPSS Modeler for data preparation.

Voraussetzungen

Prerequisites

• Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, readingdata files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and basicknowledge of modeling.• Prior completion of the Introduction to IBM SPSS Modeler and Data Science course is recommended.

Trainingsprogramm

Course Outline

1: Using functions to cleanse and enrich data• Use date functions• Use conversion functions

• Use string functions• Use statistical functions• Use missing value functions2: Using additional field transformations• Replace values with the Filler node• Recode continuous fields with the Binning node• Change a field’s distribution with the Transform node3: Working with sequence data• Use sequence functions• Count an event across records• Expand a continuous field into a series of continuous fields with the Restructure node• Use geospatial and time data with the Space-Time-Boxes node4: Sampling, partitioning and balancing data• Draw simple and complex samples with the Sample node• Create a training set and testing set with the Partition node• Reduce or boost the number of records with the Balance node5: Improving 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

Objective


Schulungsmethode



Termine und Orte

Hamburg

30. Nov


30. Nov

Krefeld

18. Jan

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




Overview

This course provides the foundations of using IBM SPSS Modeler and introduces the participant to data science. Theprinciples and practice of data science are illustrated using the CRISP-DM methodology. The course provides training inthe basics of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and introduces the student tomodeling.


Zielgruppe

Audience

Data scientistsBusiness analystsClients who are new to IBM SPSS Modeler or want to find out more about using it

Voraussetzungen

Prerequisites

Knowledge of your business requirements

Trainingsprogramm

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

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

Objective

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

Schulungsmethode



Termine und Orte


19. Okt bis 20. Okt

Hamburg

14. Dez bis 15. Dez

München

14. Dez bis 15. Dez

Krefeld

15. Feb bis 16. Feb

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

Präsenztraining




Overview

This course provides an introduction to supervised models, unsupervised models, and association models. This is anapplication-oriented course and examples include predicting whether customers cancel their subscription, predictingproperty values, segment customers based on usage, and market basket analysis.


Zielgruppe

Audience

Data scientistsBusiness analystsClients who want to learn about machine learning models

Voraussetzungen

Prerequisites

Knowledge of your business requirements

Trainingsprogramm

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• 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: TwoStep and Anomaly detection• TwoStep basics• TwoStep 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

Objective

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

Association models: Sequence detection • Sequence detection basics • 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: TwoStep and Anomaly detection • TwoStep basics • TwoStep 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

Preparing data for modeling • Examine the quality of the data • Select important predictors • Balance the data

Schulungsmethode



Termine und Orte

Hamburg

26. Nov bis 27. Nov

München

26. Nov bis 27. Nov

Krefeld

14. Jan bis 15. Jan

IBM 0A0V8G - Predictive Modeling for Continuous Targets Using IBMSPSS Modeler (v18.1.1)

Präsenztraining




Overview

This course provides an overview of how to use IBM SPSS Modeler to predict a target field that describes numeric values.Students will be exposed to rule induction models such as CHAID and C&R Tree. They will also be introduced totraditional statistical models such as Linear Regression. Students are introduced to machine learning models, such asNeural Networks. Business use case examples include: predicting the length of subscription for newspapers,telecommunication, and job length, as well as predicting insurance claim amounts.


Zielgruppe

Audience

IBM SPSS Modeler Analysts who have completed the Introduction to IBM SPSS Modeler and Data Mining course whowant to become familiar with the modeling techniques available in IBM SPSS Modeler to predict a continuous target.

Voraussetzungen

Prerequisites

• Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, readingdata files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and abasic knowledge of modeling.• Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1.1) is recommended.

Trainingsprogramm

Course Outline

1: Introduction to predicting continuous targets

• List three modeling objectives• List two business questions that involve predicting continuous targets• Explain the concept of field measurement level and its implications for selecting a modeling technique• List three types of models to predict continuous targets• Determine the classification model to use2: Building decision trees interactively• Explain how CHAID grows a tree• Explain how C&R Tree grows a tree• Build CHAID and C&R Tree models interactively• Evaluate models for continuous targets• Use the model nugget to score records3: Building your tree directly• Explain the difference between CHAID and Exhaustive CHAID• Explain boosting and bagging• Identify how C&R Tree prunes decision trees• List two differences between CHAID and C&R Tree4: Using traditional statistical models• Explain key concepts for Linear• Customize options in the Linear node• Explain key concepts for Cox• Customize options in the Cox node5: Using machine learning models• Explain key concepts for Neural Net• Customize one option in the Neural Net node

Objective

1: Introduction to predictive models for continuous targets • List three modeling objectives • List two business questions that involve predicting continuous targets • Explain the concept of field measurement level and its implications for selecting a modeling technique • List three types of models to predict continuous targets • Determine the classification model to use

2: Building decision trees interactively • Explain how CHAID grows a tree • Explain how C&R Tree grows a tree • Build CHAID and C&R Tree models interactively • Evaluate models for continuous targets • Use the model nugget to score records

3: Building decision trees directly • Customize two options in the CHAID node • Customize two options in the C&R Tree node • List one difference between CHAID and C&R Tree

4. Using traditional statistical models • Explain key concepts for Linear • Customize options in the Linear node • Explain key concepts for Cox • Customize options in the Cox node

5: Using machine learning models • Explain key concepts for Neural Net • Customize one option in the Neural Net node

Schulungsmethode



Termine und Orte

Hamburg

27. Nov


27. Nov

Krefeld

15. Jan

IBM 0S006G - Introduction to IBM SPSS Collaboration and DeploymentServices (v8)

Präsenztraining




Overview

This course is an intermediate course designed to teach Collaboration and Deployment users object and assetmanagement, security, shared resource usage, automation, and interaction with IBM SPSS Modeler Gold. Students focuson the makeup of the content repository and its objects. They will learn how to manage repository objects, the logicalhierarchy structure, and how to import, export, and promote objects for use in multi-repository environments. Studentswill become familiar with the components of jobs and the mechanisms to set up, order, and relate job steps. Scheduling,parameters, job monitoring, job history, and event notification are discussed. Finally, the role of Collaboration andDeployment Services in Modeler Gold is discussed, addressing Real Time Scoring, Analytic Data View, and ModelManagement.


Zielgruppe

Audience

Modelers, Analysts

Voraussetzungen

Prerequisites

None

Trainingsprogramm

Course Outline

1: Overview of Collaboration and Deployment Services• Identify the purpose and capabilities of IBM SPSS Collaboration and Deployment Services

• List and describe the various tools within C&DS• List and describe tools that work in conjunction with C&DS

2: Content, assets and permissions• Describe repository content management structure• Describe the file organization structure within C&DS Deployment Manager• Create and apply user specific access permissions for assets• Upload and retrieve repository files using Deployment Manager

3: Work with assets• Describe object properties• Describe how C&DS applies versioning to objects and assets• Create and use version labels• Explore custom properties for assets• Search for repository content• Describe object locking

4: Track asset changes• Identify subscriptions and notifications• Create folder and asset level subscriptions• Create a notification

5: Access files from other applications• Connect the Modeler client to the C&DS repository• Search for, and retrieve assets from the repository• Store a Modeler Stream into the repository with the correct property settings• Perform storage and retrieval operations from within IBM SPSS Modeler

6: Advanced content operations• Describe the purpose of multiple repositories• Export content from a repository• Import content into a repository• Understand object promotion

7: Jobs• Identify a job its uses• Describe the job-building tool• Create a single-step job• Create a multi-step job

8: Notifications and parameters• Define the three different notifications connected to jobs• Build notifications within jobs• Define run time parameters and describe possible uses• Add parameters to a job or a job step definition

9: Schedule jobs• Identify and describe the three schedule types• Build simple and recurring time-based schedules• Create message-based job schedules• Monitor job schedule definitions

10: Examine Job History• View job history in Deployment Manager• Manipulate the Job History View

• View job history in Deployment Portal• Use the Job History tool to monitor job execution success and failure

11: Analytic Data View and Real Time Scoring• Create an Analytic Data View• Identify how to create a scoring configuration• Configure and test Real Time Scoring

12: Model Management• Explore Model Management• Understand Model Evaluation and Model Refresh• Create and run a Champion Challenger job

Objective


Schulungsmethode



Termine und Orte

Hamburg

14. Dez bis 15. Dez

München

14. Dez bis 15. Dez

Krefeld

01. Feb bis 02. Feb

IBM 0S114G - Introduction to Analytical Decision Management (v18)Präsenztraining




Overview

This course brings the benefit of predictive analytics to real business problems, allowing you to build custom applicationstailored to your customers or industry. While applications are typically configured to solve very specific problems, all arebased on a common set of capabilities: Automate decisions using business rules; add insight using predictive models;and use prioritization, optimization, or simulation to reach the best decision based on the above. A number of packagedapplications are available, tailored to solving specific business problems. The course will not only cover how to use the packaged applications, but also how to create your own applications, howAnalytical Decision Management interplays with IBM SPSS Modeler (v18) and IBM SPSS Collaboration and DeploymentServices (v8), and how to deploy results for real-time.


Zielgruppe

Audience

This course is intended for:• Anyone with little or no experience in using Analytical Decision Management v18• Anyone who is interested in using Decision Management techniques to help them make business decisions• Anyone who is considering purchasing Analytical Decision Management v18

Voraussetzungen

Prerequisites

You should have:• Experience using applications, such as word processors or spreadsheets, in the Microsoft Windows, Macintosh orLinux environment• Experience with Analytical Decision Management v18 is not necessary, though a basic understanding of DecisionManagement theory and techniques is helpful• Some familiarity with IBM SPSS Modeler and with Predictive, Clustering, and Association modeling is helpful

Trainingsprogramm

Course Outline

1. Introduction to Decision Management• What is Decision Management?• Why Use Decision Management?• Analytical Decision Management• Five Steps of Decision Management• Use of Data• Historical and Operational Data• Classification Models• User Defined Rules• Deploying Models2. A Sample Session: Managing Customer Interactions• Five Steps in Decision Management• Demonstration: A Marketing Call Center Business Case3. Defining Data Sources• Data Structure• Field Storage• Field Measurement Level• Data Step• Project Data Source• Derived Tab• Secondary Data Sources• Compatibility of Data Sources4. Defining Global Selections• Adding Rules to Global Selections• Defining and Sharing Rules• Evaluating Rules5. Creating Rules from Models• Predictive Models• Predictive Rule Models• Clustering Models• Association Models• Automated Data Preparation & Partitioning• Evaluating Models6. Defining Outcomes• Specify Project Duration• Include / Exclude Cases from Project• Define Action Categories• Create Allocation Rules7. Prioritize, Optimize and Combine Outcomes• Selecting From Alternative Actions• Prioritizing Outcomes• Optimizing Outcomes• Combining Outcomes8. Deploying Models for Scoring• Why Deploy the Project?• Real Time Scoring Panel• Batch Scoring Panel• Scoring Configurations• Using the Scoring View9. Building a Custom Application

• Application Configuration• Creating a New Application10. Using Modeler Streams in ADM• Using Modeler Streams• Minimum Requirements for a Stream• Using a Stream in a Project

Objective


Schulungsmethode



Termine und Orte

Hamburg

16. Dez bis 17. Dez


16. Dez bis 17. Dez

Krefeld

03. Feb bis 04. Feb

IBM B5280G - IBM Cognos Data Manager: Build Data Marts withEnterprise Data (V10.2)

Präsenztraining




Overview

IBM Cognos Data Manager: Build Data Marts with Enterprise Data (V10.2) is a five-day, instructor-led course that teachesparticipants how to move, merge, consolidate, and transform data from a range of data sources to build and maintainsubject-area data marts. In the process, students will create a catalog and add connections to data sources and targets.They will also deliver fact and dimension data to a data mart through the use of builds and the dimensional framework.In addition, students will learn how to automate common functionality and handle complex data issues, such asunbalanced hierarchical structures.


Zielgruppe

Audience

This course is intended for Developers.

Voraussetzungen

Prerequisites

You should have:

a knowledge of basic Windows functionality, database and dimensional analysis concepts, as well as a workingknowledge of SQL

Trainingsprogramm

Course Outline

Getting Started

Identify the purpose of IBM Cognos Data ManagerDefine data warehousing and its key underlying conceptsIdentify how Data Manager creates data warehousesExamine the Data Manager architecture and user interface

Create a Catalog

Examine the purpose and contents of Data Manager catalogsCreate a catalogDefine connections to source and target dataAccess data using SQLTermConfigure flat data source files using SQLTXT

Create Hierarchies

Examine the role of the dimensional framework in Data ManagerExamine hierarchies and their data sourcesIdentify how to create hierarchies from the columns of one table, the rows of one table, and from multiple tablesTest and view hierarchiesCreate a hierarchy of static date valuesHandle weeks in a date hierarchy

Create Basic Builds

Examine Data Manager builds and build-related terminologyCreate a dimension build using the Dimension Build wizardCreate a fact build using the Fact Build wizardTest and execute a fact buildDocument a catalogCreate catalog schema

Create Derivations

Examine derivationsApply operators and functions to derivationsExamine the derivation timing modelAdd derivations to a fact build

Create Conformed Dimensions

Examine conformed dimensions and their advantagesDesign conformed dimensionsCreate conformed dimensionsCreate data integrity lookups that use conformed dimensions

Customize Reference Structures

Create hierarchies manually using different approachesExamine the features of a hierarchyExamine literalsSet data access for hierarchy levelsExamine static and dynamic membersExamine fosteringUse derivations in a hierarchy

Process Dimensional History and Late Arriving Facts

Examine slowly changing dimensions (SCDs)Use surrogate keys in SCDs

Manage type 1 and type 2 changes to dimensional dataLoad historical data for a dimensionExamine late arriving factsProcess late arriving facts in a fact build

Transform Data Using Lookups and Derived Dimensions

Identify when to use lookupsIdentify the requirements for a lookupCreate a translation lookupCreate an optional lookupAdd derived dimensions to fact builds

Customize Data Delivery

Configure fact and dimension delivery modulesCreate indexes on fact and dimension tablesUpdate fact data using keys

Customize Fact Data Processing

Filter fact dataMerge duplicate fact dataExamine fact data integrity checkingReject fact data

Aggregate, Filter, and Partition Fact Data

Aggregate fact dataExamine aggregate rulesVertically restrict fact dataHorizontally restrict fact dataPartition fact data

Implement Job Control

Examine where job control fits into the data warehouse lifecycleCreate a JobStreamAdd, link, and reposition nodesExecute a JobStream and view the results

Automate Functionality Using Commands

Differentiate between the Command Line Interface (CLI) and Data Manager DesignerIdentify common commandsUse commands in a batch fileExamine variables

Customize Functionality with User-Defined Functions and Variables

Examine user defined functions (UDFs)Create an internal UDFCreate a user-defined variable

Process Unbalanced Hierarchical Data

Examine balanced, unbalanced, and ragged hierarchiesAdd a recursive level to a hierarchyIdentify ways to balance a hierarchy and delivered flattened data

Examine circular references

Pivot Fact Data

Examine pivotingUse the single pivot techniqueUse the advanced pivot techniqueExamine reverse pivoting

Resolve Data Quality Issues

Identify data quality and cleansing issuesHandle fostered and unmatched membersPerform debugging using SQLTerm and functionsAssess the quality of output data

Troubleshoot and Tune the Data Manager Environment

Use build logging to ensure that data marts are being loaded properlyPerform dimension breakingManage memory and resourcesExport DDL statements

Organize and Package Data Manager Components

Export and import components using packagesSearch for components in a catalog using Navigator

Integrate with IBM Cognos BI

Examine IBM Cognos BIIdentify the role of metadata dimensions, metadata collections, and metadata starsExport Data Manager metadata to XMLImport Data Manager XML into Framework ManagerUse Data Manager metadata with IBM Cognos BIPublish a data movement task to IBM Cognos Connection

End-to-End Workshop

Entity-Relationship Model of the GO_Demo Database (Optional)

Work in a Multi-Developer Environment (Optional)

Examine collaborative development supportExamine the source code repositoryExamine the component dependency modelIdentify planning considerations

Standardizing Dimensions and Facts Exercise (Optional)

Review of Data Manager Essentials (Optional)

Data warehouse designThe purpose of Data Manager componentsDevelopment steps in Data Manager to create data martsTrack dimensional changes and late arriving facts

Work with SAP R/3 Data (Optional)

Identify how to access SAP R/3 data sources using the IBM Cognos Data Manager Connector for SAP R/3 tool

Objective

Please refer to course overview for description information.

Schulungsmethode



Termine und Orte

Hamburg

02. Nov bis 06. Nov


02. Nov bis 06. Nov

Krefeld

11. Jan bis 15. Jan

IBM B6019G - IBM Cognos Analytics: Architecture and Logging (v11.0)Präsenztraining




Overview

This course is designed to teach participants how to identify components and sub-components of the IBM CognosAnalytics architecture and how to use tools and techniques to provide a foundation to troubleshoot issues. Throughlecture and interactive exercises participants will identify IBM Cognos Analytics components, examine how thesecomponents interact with Java, and will explore logging to assist when troubleshooting issues.


Zielgruppe

Audience

Administrators responsible for administering the IBM Cognos Analytics 11.0 environment

Voraussetzungen

Prerequisites

• IBM Cognos Analytics: Administration (v11.0) course or equivalent experience administering the IBM CognosAnalytics environment.

Trainingsprogramm

Course Outline

Architecture Overview1: Introduction and Service-Oriented Architecture• Identify IBM Cognos 11.0 architectural components• Describe Service-Oriented Architecture in IBM Cognos Analytics

2: Explore the IBM Cognos Dispatcher

• Describe IBM Cognos Dispatcher• Describe request routing and the routing process• Describe Content Manager Cache Service

3: Examine IBM Cognos services• Identify IBM Cognos services• Explore the architecture in IBM Cognos 11.0

4: Examine Java memory management• Describe Java memory layout• Manage Java memory• Use tools to monitor Java memory

5: Examine audit logging and Indication Processing Facility logging• Describe installation logs and configuration logs• Explore audit logging• Explore IPF logging

6: Perform dye tracing• Identify dye tracing requirements• Perform dye tracing

7: Explore Dynamic Query Mode• Explain Dynamic Query Mode (DQM) logging• Explain IBM Cognos Dynamic Query Analyzer (DQA)

8: Explore component logging• Explore component logging for Gateway, Dispatcher, Report Server, and Universal Data Access layer

9: Examine additional tools and special task logging• Explore diagnostic tools and utilities for special task logging

Objective

Please refer to course overview.

Schulungsmethode



Termine und Orte

Hamburg

10. Nov bis 11. Nov

München

10. Nov bis 11. Nov

Krefeld

19. Jan bis 20. Jan

IBM B6152G - IBM Cognos Framework Manager: Design Metadata Models(v11.0.x)

Präsenztraining




Overview

This offering provides participants with introductory to advanced knowledge of metadata modeling concepts, and how tomodel metadata for predictable reporting and analysis results using Framework Manager. Participants will learn the fullscope of the metadata modeling process, from initial project creation, to publishing of metadata to the web, enablingend users to easily author reports and analyze data.


Zielgruppe

Audience

Data Modelers

Voraussetzungen

Prerequisites

Knowledge of common industry-standard data structures and designExperience with SQLExperience gathering requirements and analyzing dataIBM Cognos Analytics: Author Reports Fundamentals (v11.0.x) (recommended)

Trainingsprogramm

Course Outline

Introduction to IBM Cognos Framework Manager• Model data and identifying related data• Define requirements and modeling strategies

• Overview of IBM Cognos Framework Manager• Create a baseline project• Extend a model• Prepare reusable metadata

Model for predictable results in IBM Cognos Framework Manager• Identify query issues• Identify reporting traps• Model virtual star schemas• Use query subjects, modify relationships, and consolidate metadata using virtual objects• Create calculations, filter data, and customize metadata for runtime• Implement a time dimension and specify determinants

Model for presentation in IBM Cognos Framework Manager• Create a presentation view• Examine data source query subject types and stored procedure query subject types• Specify data security and package security• Specify object security and dynamic data security• Create analysis objects• Manage OLAP data sources

Advanced capabilities in IBM Cognos Framework Manager• Explore SQL generation and the use of governors• Examine the use of IBM Cognos SQL and generated SQL for DMR data• Other query considerations• Use session parameters, prompt macros, and security macro functions• Use materialized views, minimize SQL, and enable Dynamic Query Mode (DQM)• DQM, CQM, caching metadata, query processing, aggregate calculation, and other ways to improve performance

Extended capabilities in IBM Cognos Framework Manager (Optional)• Perform basic maintenance and management on a model• Remap metadata to another source and import and link additional data sources• Run scripts to automate or update a model and report on a model• Segment a project, link a project, and branch a model• Nest packages and specify package languages and functions• Explore additional modeling techniques and customize metadata for a multilingual audience

Objective


Schulungsmethode



Termine und Orte

Hamburg

16. Nov bis 19. Nov

München

16. Nov bis 19. Nov

Krefeld

25. Jan bis 28. Jan


22. Mär bis 25. Mär

IBM B6155G - IBM Cognos Analytics: Enterprise Administration (v11.0.x)Präsenztraining




Overview

This offering covers the fundamental concepts of installing and configuring IBM Cognos Analytics, and administeringservers and content, in a distributed environment. In the course, participants will identify requirements for theinstallation and configuration of a distributed IBM Cognos Analytics software environment, implement security in theenvironment, and manage the server components. Students will also monitor and schedule tasks, create data sources,and manage and deploy content in the portal and IBM Cognos Administration.


Zielgruppe

Audience

Administrators

Voraussetzungen

Prerequisites

Knowledge of Web application server architecturesSecurity systems administrationExperience using basic Windows functionalityExperience using a Web browserKnowledge of your business requirements

Trainingsprogramm

Course Outline

Introduction to IBM Cognos Analytics administration• IBM Cognos Analytics components

• Administration workflow• IBM Cognos Administration• IBM Cognos Configuration

Identify IBM Cognos Analytics architecture• Features of the IBM Cognos Analytics architecture• Examine the multi-tiered architecture, and identify logging types and files• Examine IBM Cognos Analytics servlets• Performance and installation planning• Balance the request load• Configure IBM Cognos Analytics

Secure the IBM Cognos Analytics environment• Identify the IBM Cognos Analytics security model• Define authentication in IBM Cognos Analytics• Define authorization in IBM Cognos Analytics• Identify security policies• Secure the IBM Cognos Analytics environment

Administer the IBM Cognos Analytics server environment• Administer IBM Cognos Analytics servers• Monitor system performance• Manage dispatchers and services• Tune system performance, and troubleshoot the server• Audit logging• Dynamic cube data source administration workflow

Manage run activities• View current, past, and upcoming activities• Manage schedules

Manage content in IBM Cognos Administration• Data sources and packages• Manage visualizations in the library• Deployment• Other content management tasks

Examine departmental administration capabilities• Create and manage team members• Manage activities• Create and manage content and data• Manage system settings• Manage Themes, Extensions, and Views• Share services with multiple tenants

Objective


Schulungsmethode



Termine und Orte

München

23. Nov bis 24. Nov

Hamburg

23. Nov bis 24. Nov

Krefeld

01. Feb bis 02. Feb

IBM B6158G - IBM Cognos Analytics: Author Reports Fundamentals(v11.0.x)

Präsenztraining




Overview

This offering provides Business and Professional Authors with an introduction to report building techniques usingrelational data models. Techniques to enhance, customize, and manage professional reports will be explored. Activitieswill illustrate and reinforce key concepts during this learning opportunity.


Zielgruppe

Audience

Report Authors

Voraussetzungen

Prerequisites

• Knowledge of your business requirements• IBM Cognos Analytics for Consumers (v11.0) WBT or equivalent knowledge

Trainingsprogramm

Course Outline

What is IBM Cognos Analytics - Reporting?• Create a simple list report• Create a report from a dimensionally modeled relational data source

Examine personal data sources and data modules• Upload personal data

• Upload custom images• Use navigation paths• Create a report from a personal data source

Examine list reports• Group data in a list• Format columns in a list• Include headers and footers in a list• Enhance a list report

Aggregate measure/fact data• Identify differences in aggregation• Explore data aggregation

Use shared dimensions to create multi-fact queries• Create a multi-fact query in a list report

Add repeated information to reports• Create a mailing list report

Create crosstab reports• Add measures to a crosstab• Data sources for a crosstab• Create a simple crosstab report

Create complex crosstab reports• Add items as peers• Create crosstab nodes and crosstab members• Create a complex crosstab report

Format, sort, and aggregate data in a crosstab• Sort, format, and aggregate a crosstab report

Create discontinuous crosstab reports• Present unrelated items using a discontinuous crosstab

Create a visualization report• Create and format a visualization report• Create a report that uses a Map visualization• Show the same data graphically and numerically

Focus reports using filters• Apply filters to a report• Apply a detail filter on fact data in a report• Apply a summary filter to a report

Focus reports using prompts• Create a prompt by adding a parameter• Add a value prompt to a report• Add a Select & search prompt to a report• Create a cascading prompt

Augment reports using calculations• Add calculations to a report• Display prompt selections in the report title

Customize reports with conditional formatting• Create a multilingual report• Highlight exceptional data and conditionally render a column

Drill-through definitions• Let users navigate to related data in IBM Cognos Analytics

Enhance report layout• Create a report structured on data items• Create a condensed list report

Use additional report building techniques• Section a report and reuse objects within the same report• Reuse layout components in a different report• Explore options for reports that contain no data

Objective

• What is IBM Cognos Analytics – Reporting• Examine dimensionally modelled and dimensional data sources• Examine personal data sources and data modules• Examine List reports• Aggregate measure/fact data• Use shared dimensions to create multi-fact queries• Add repeated information to reports• Create crosstab reports• Create complex crosstab reports• Format, sort, and aggregate data in a crosstab report• Create discontinuous crosstab reports• Create Visualization reports• Add business logic to reports using IBM Cognos Analytics – Reporting• Focus reports using filters• Focus reports using prompts• Augment reports using calculations• Extend report functionality in IBM Cognos Analytics - Reporting• Customize reports with conditional formatting• Conditionally format one crosstab measure based on another• Drill-through definitions• Enhance the report layout• Use additional report building techniques

Schulungsmethode



Termine und Orte


30. Nov bis 02. Dez

Hamburg

30. Nov bis 02. Dez

Krefeld

08. Feb bis 10. Feb

IBM B6252G - IBM Cognos Framework Manager: Design Metadata Models(v11.1.x)

Präsenztraining




Overview

This offering provides participants with introductory to advanced knowledge of metadata modeling concepts, and how tomodel metadata for predictable reporting and analysis results using IBM Cognos Framework Manager. Participants willlearn the full scope of the metadata modeling process, from initial project creation, to publishing of metadata to the web,enabling end users to easily author reports and analyze data.


Zielgruppe

Audience

Data Modelers

Voraussetzungen

Prerequisites

Knowledge of common industry-standard data structures and designExperience with SQLExperience gathering requirements and analyzing dataIBM Cognos Analytics: Author Reports Fundamentals (v11.1.x) (recommended)

Trainingsprogramm

Course Outline

Introduction to IBM Cognos Framework Manager• Model data and identifying related data• Define requirements and modeling strategies

• Overview of IBM Cognos Framework Manager• Create a baseline project• Extend a model• Prepare reusable metadata

Model for predictable results in IBM Cognos Framework Manager• Identify query issues• Identify reporting traps• Model virtual star schemas• Use query subjects, modify relationships, and consolidate metadata using virtual objects• Create calculations, filter data, and customize metadata for runtime• Implement a time dimension and specify determinants

Model for presentation in IBM Cognos Framework Manager• Create a presentation view• Examine data source query subject types and stored procedure query subject types• Specify data security and package security• Specify object security and dynamic data security• Create analysis objects• Manage OLAP data sources

Advanced capabilities in IBM Cognos Framework Manager• Explore SQL generation and the use of governors• Examine the use of IBM Cognos SQL and generated SQL for DMR data• Other query considerations• Use session parameters, prompt macros, and security macro functions• Use materialized views, minimize SQL, and enable Dynamic Query Mode (DQM)• DQM, CQM, caching metadata, query processing, aggregate calculation, and other ways to improve performance

Extended capabilities in IBM Cognos Framework Manager• Perform basic maintenance and management on a model• Remap metadata to another source and import and link additional data sources• Run scripts to automate or update a model and report on a model• Segment a project, link a project, and branch a model• Nest packages and specify package languages and functions• Explore additional modeling techniques and customize metadata for a multilingual audience

Objective

Introduction to IBM Cognos Framework Manager • Model data and identifying related data • Define requirements and modeling strategies • Overview of IBM Cognos Framework Manager • Create a baseline project • Extend a model • Prepare reusable metadata

Model for predictable results in IBM Cognos Framework Manager • Identify query issues • Identify reporting traps • Model virtual star schemas • Use query subjects, modify relationships, and consolidate metadata using virtual objects • Create calculations, filter data, and customize metadata for runtime • Implement a time dimension and specify determinants

Model for presentation in IBM Cognos Framework Manager • Create a presentation view • Examine data source query subject types and stored procedure query subject types • Specify data security and package security • Specify object security and dynamic data security • Create analysis objects • Manage OLAP data sources

Advanced capabilities in IBM Cognos Framework Manager • Explore SQL generation and the use of governors • Examine the use of IBM Cognos SQL and generated SQL for DMR data • Other query considerations • Use session parameters, prompt macros, and security macro functions • Use materialized views, minimize SQL, and enable Dynamic Query Mode (DQM) • DQM, CQM, caching metadata, query processing, aggregate calculation, and other ways to improve performance

Extended capabilities in IBM Cognos Framework Manager • Perform basic maintenance and management on a model • Remap metadata to another source and import and link additional data sources • Run scripts to automate or update a model and report on a model • Segment a project, link a project, and branch a model • Nest packages and specify package languages and functions • Explore additional modeling techniques and customize metadata for a multilingual audience

Schulungsmethode



Termine und Orte

München

16. Nov bis 19. Nov

Hamburg

16. Nov bis 19. Nov

Krefeld

25. Jan bis 28. Jan


22. Mär bis 25. Mär

IBM B6255G - IBM Cognos Analytics: Enterprise Administration (V11.1.x)Präsenztraining




Overview

This offering covers the fundamental concepts of installing and configuring IBM Cognos Analytics, and administeringservers and content, in a distributed environment. In the course, participants will identify requirements for theinstallation and configuration of a distributed IBM Cognos Analytics software environment, implement security in theenvironment, and manage the server components. Students will also monitor and schedule tasks, create data sources,and manage and deploy content in the portal and IBM Cognos Administration.


Zielgruppe

Audience

Administrators

Voraussetzungen

Prerequisites

Basic knowledge of Web application server architecturesBasic knowledge of security systems administrationExperience using basic Windows functionalityKnowledge of your business requirements

Trainingsprogramm

Course Outline

Introduction to IBM Cognos Analytics administration• IBM Cognos Analytics components• Administration workflow

• IBM Cognos Administration• IBM Cognos Configuration

Identify IBM Cognos Analytics architecture• Features of the IBM Cognos Analytics architecture• Examine the multi-tiered architecture, and identify logging types and files• Examine IBM Cognos Analytics servlets• Performance and installation planning• Balance the request load• Configure IBM Cognos Analytics

Secure the IBM Cognos Analytics environment• Identify the IBM Cognos Analytics security model• Define authentication in IBM Cognos Analytics• Define authorization in IBM Cognos Analytics• Identify security policies• Secure the IBM Cognos Analytics environment

Administer the IBM Cognos Analytics server environment• Administer IBM Cognos Analytics servers• Monitor system performance• Manage dispatchers and services• Tune system performance, and troubleshoot the server• Audit logging• Dynamic cube data source administration workflow

Manage run activities• View current, past, and upcoming activities• Manage schedules

Manage content in IBM Cognos Administration• Data sources and packages• Manage visualizations in the library• Deployment• Other content management tasks

Examine departmental administration capabilities• Create and manage team members• Manage activities• Create and manage content and data• Manage system settings• Manage Themes, Extensions, and Views• Share services with multiple tenants

Objective

Introduction to IBM Cognos Analytics administration • IBM Cognos Analytics components • Administration workflow • IBM Cognos Administration • IBM Cognos Configuration

Identify IBM Cognos Analytics architecture • Features of the IBM Cognos Analytics architecture

• Examine the multi-tiered architecture, and identify logging types and files • Examine

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