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This is by far the most effective slideshow I have ever seen. When my dad, a plant manager at an auto manufacturer, was told that management wanted their monthly reports to be more clear and concise, he created this. Slide 1 is production and slide 2 is rejected parts.

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Daten sind für Unternehmen aller Branchen zu einer maßgeblichen Ressource geworden und Data Science zur Schlüsselqualifikation, um das Potenzial darin zu erschließen. Die freie Programmiersprache R ist dabei weltweit eine der populärsten Lösungen für die Analyse und Visualisierung von Daten. Die Präsentation beschreibt die verschiedenen Wege um Data Science und R im Unternehmen einzuführen und gibt Hinweise, wie Sie den Weg zur Operationalisierung von Data Science meistern.

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Die R-Akademie von eoda ist das modulare Trainingsprogramm für R, das praxisorientiert und umfassend die vielfältigen Möglichkeiten der freien Programmiersprache behandelt. Das Schulungsangebot der R-Akademie reicht von Einsteigerkursen bis hin zu Kursen für fortgeschrittene R-User und vom Datenmanagement bis hin zur Visualisierung der Ergebnisse. In Kassel oder Inhouse in Ihrem Unternehmen oder an Ihrer Universität - mit der R-Akademie stellen Sie sicher, dass Sie die richtigen Kenntnisse erwerben, um den maximalen Nutzen in der Anwendung von R zu erzielen.

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Erschließen Sie mit der Programmiersprache R das Potenzial von Data Science – wir unterstützen Sie dabei. Mit unserem R-Support erweitern wir für Sie die R-Community um einen festen Ansprechpartner für Ihre individuellen Problemstellungen rund um den produktiven Einsatz von R. Die Anforderungen und Analyseprozesse von Unternehmen unterscheiden sich deutlich von denen im wissenschaftlichen Kontext. Dessen sind wir uns bewusst: Als einer der Vorreiter im professionellen Einsatz von R verfügen wir über das Know-how und die Erfahrung aus jahrelanger Projektarbeit. Sie profitieren von einem Support-Dienstleister mit einem umfangreichen Wissen über den ganzen Analytik-Prozess vom Datenmanagement über die Analyse bis hin zur Visualisierung, kombiniert mit einem tiefgreifenden Verständnis über statistische Methoden und IT-Landschaften.

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In old economy companies, the introduction of R is typically a button-up process that follows a pattern of three major stages of maturity: At the first stage, guerrilla projects use R parallel to the "official" IT environment. The usage of R is often initiated by interns, student assistants or newly recruited graduates. At the second stage, when the results of the guerrilla projects attract the attention of business departments, R is used as analytic language in proof-of-concept projects. When the proof-of-concept has been successful, the outcome shall be transferred to the production system. At this stage R is being introduced “officially” to the IT environment. While the first and second level of maturity usually do not cause any major problems, the step to the third level is most crucial for the long term success of the implementation of R. This talk will focus on how to master the switch from proof-of-concept to production. It will show based on real world experiences typical road blocks as well as the most important success factors.

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International competition, shorter product life cycles and faster technological leaps forward – these are only a few of the challenges the production of a company is facing in the 21st century. In order to survive in an environment like this, resource-efficient and secure planning of production processes are necessary to guarantee a consistent and high quality output. Unforeseeable machine failures as well as performance drops or deterioration in quality because of defective system components can lead to shortness of supplies which will eventually weaken the market position of the entire organization. To meet these requirements organizations are increasingly focusing on the improvement of maintenance, repair and operations of their machinery. In the previous years, the industry shifted their focus away from only reactive repair mechanisms towards the predictive coordination of machine maintenance. Predictive Maintenance falls under the category of the future of maintenance developments. Originally developed in the course of the “Industrie 4.0” high-tech strategy of the German government, today Predictive Maintenance represents the informatization of production processes - intelligent IT-based production systems on the path towards a Smart Factory. Through the generation and analysis of different machine data, the predictive power of the state of industrial plants is not only enhanced, but also provides the basis for an improved planning certainty as well as the efficient planning of repair and maintenance work.

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Best PowerPoint Presentation Ever

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