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The creation of this data model requires the data connection to the source system (e.g. SAP ERP), the extraction of the data and, above all, the data modeling for the event log.
Data scientists will typically perform dataanalytics when collecting, cleaning and evaluating data. By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model.
In the era of Industry 4.0 , linking data from MES (Manufacturing Execution System) with that from ERP, CRM and PLM systems plays an important role in creating integrated monitoring and control of business processes.
Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and dataengineers, and determining appropriate key performance indicator (KPI) metrics.
This track will focus on helping you build skills in text mining, data storytelling, datamining, and predictiveanalytics through use cases highlighting the latest techniques and processes to collect, clean, and analyze growing volumes of structured data.
You’ll also learn the art of storytelling, information communication, and data visualization using the latest open-source tools and techniques. You’ll also hear use cases on how data can be used to optimize business performance. You’ll also hear use cases on how data can be used to optimize business performance.
Companies use Business Intelligence (BI), Data Science , and Process Mining to leverage data for better decision-making, improve operational efficiency, and gain a competitive edge. Process Mining offers process transparency, compliance insights, and process optimization.
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