Predictive Analytics – Programmieren in R am Beispiel von Vorhersageanalysen (Level 3)

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Predictive Analytics 2.5 –Programmieren in R am Beispiel von Vorhersageanalysen 09.06.2016, Michael Gierok, IBsolution GmbH

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Für wen ist dieses Webinar?

• Leute, die gerne statistische Zusammenhänge in ihren Daten erkennen möchte

• Interessierte an der Programmiersprache R

• Generell Interessierte an Predictive Analytics


Was lernen Sie in diesem Webinar?

• Neuerungen rund um das Release 2.5

• Einsatz einer Custom R Component – „ForecastDaily Time Series“

• Einsatz der Programmiersprache R für Zeitreihenanalysen


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Persönliches Zertifikat für jeden Teilnehmer:

• Webinar anschauen

• Multiple-Choice-Test ablegen

• 8 von 10 Fragen richtig beantworten


Agenda

Punkt 1: Whats new in Automated Analytics?

Punkt 2: Whats new in Expert Analytics?

Punkt 3: Live Demo I (Creating Time Series Data in R)

Punkt 4: Live Demo II (Custom R Component –Forecast Daily Time Series)


Ihr Redner

Michael Gierok

IBsolution GmbH

Bild einfügen


Neu in Automated Analytics –Native Spark Modeling

Native Spark Modeling executes the automated predictive models directly on Hadoopusing Spark engine to connect to Hive.

Benefits: No data transfer between the modeling engine and the database; all calculationsare done directly in the database

Faster response times: you can learn predictive insights by training models in shorter time with big data

Better use of CPUs, especially in distributed Hadoop environment

Hadoop Hive 1.1 and 1.2.1 are now supported by Automated Analytics

Support for SAP HANA SP11: Version 2.5 is compatible with SAP HANA SPS10.102.93 an SPS11.


Neu in Automated Analytics – Native Spark Modeling


Neu in Expert Analytics – Creating andConsuming R Extensions

Benefits: The ability to write and share extensions in R scripts empowers the Predictive Analytics community to extend Expert Analytics features. This feature enables you to modify your customer solutions, write algorithms specifically for a certain sector of a business or to suit specific needs such as an unusual use case.

Creating: Authors create their R Extensions in a wizard-like dialog box called Create New R Extension. From version 2.5, authors can name an appropriate category for their newly-created R Extension algorithms from a predefined list of options.

Security: The intellectual property of authors is protected by encrypting the scripts in the local file system. On being exported, R Extensions are stored in encrypted .spar files to ensure that the scripts are not only easy to create and consume, but also safe to share.

Editing: Authors can edit their own R Extensions before sharing. They can also update the original file and re-export it. Consumers can import and use the extension in an analysis chain, but cannot edit it.

Sharing via SAP Analytics Extensions Directory: R Extensions can be consumed at the Predictive Analytics page of the SAP Analytics Extensions Directory . The directory is a portal that provides a link to the R Extension at the partner host site, rather than storing them online.

Importing: The import process starts in Expert Analytics. An actions menu points to the directory, which can be launched in a separate browser window. From here, you can browse and download extensions of your choice.


Live Demo I – Creating Time Series Data in R

Live Demo uses R Console

Use adress for download R: https://cran.r-project.org/bin/windows/base/


Live Demo II – Custom R Component – ForecastDaily Time Series

Using Predictive 2.5 Expert Mode

Download the component from GitHub as .spar file. [ForecastDailyTimeSeries.spar -> scn.sap.com/docs/DOC-68744]

The file lists the daily page views of the Wikipedia page for „Chocolate“ from the period 3.August 2015 to October 2015.


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• 8 von 10 Fragen richtig beantworten

Zum Test http://bit.ly/1TTd3iv


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Predictive Analytics – Programmieren in R am Beispiel von Vorhersageanalysen (Level 3)

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