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My presentation about Oracle Analytic Cloud at SIOUG 2018

For 25+ years SIOUG is organising its conference in Portorož. This year it was run (15th/16th October) under the umbrella of MakeIT which is a combination of "old" Oracle experts, and "young "Java" gurus. As for many years now I try to make may share to the community. This year I did 3 presentations: Oracle Analytic Cloud: Enterprise Analytics Platform Machine Learning and Oracle Data Visualization Essbase Cubes in the Cloud Please feel free to download presentations above.

Oracle Mobile Analytics: Day by Day

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A couple of weeks ago I have delivered a new version of Qubix Business Intelligence training course based on Oracle Analytic Cloud to one of our major customers in Edinburgh. For that occasion we have added a new workshop module that focus on mobile analytics using the 2 new mobile apps - Oracle Day by Day and Oracle Synopsis. I am talking about Oracle Day by Day in this post. Oracle Analytics Cloud Day by Day is an innovative app that provides the right analytics at the right time and place.  This means that if you are interested in one set of reports in one particular location, these reports (in application these are actually called "cards") will show in app home page. Based on your searches for business data in the app, app would learn what you’re interested in, when and where you’re interested in it, and it displays the data in ready-to-use analytical charts. You can use Oracle Day by Day on your iPhone/iPad and any Android devices. It is important to acknowle

Getting started with Machine Learning in Oracle Data Visualisation

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Oracle Data Visualization and Machine Learning It's been a while since my last post. Since then we have been playing with the latest versions of Oracle Data Visualisation Desktop  and Oracle Analytic Cloud. I must admit that Oracle has made a significant progress with the DV tools. One of the key developments was made in the area of Machine Learning, which has been added just recently and brings machine learning algorithms closer to users. End users can actually deploy very complex algorithms with just "a click of the button". In today's post I am playing with preparing data for machine learning, creating machine learning models with different machine learning algorithms and finally applying those to new datasets in order to predict churn. Data I used a data set from Kaggle (https://www.kaggle.com/hkalsi/telecom-company-customer-churn/data). As you can see dataset is split into 4 csv files that have to be merged into one training and one test datase