RWTH Certificate Course Big Data in Production

Successfully Mastering Large Data Volumes

Participants will discover the difference between "Big Data" and "Industrial Big Data" in the context of production. They will gain insight into the integration tools and structures required for highly distributed data analysis. In addition, they are introduced to the basic structures of big data ecosystems and big data in the cloud.

Course Contents

▶ Data integration, analysis, and data mining: Big Data and machine learning
▶ Big Data ecosystems: from isolated solution to Industrial Data Lake
▶ Integration of heterogeneous source systems of industrial data
▶ Data-driven procedures and machine learning

Group of Participants
Engineers/Computer Scientists in the areas of mechanical engineering, electrical engineering, and computer science with programming knowledge in C++ and Python

Motivation
Data are the oil of the 21st century, and data analysis is the engine! Despite the multi-faceted collection of production data, the targeted usage of these data is often lacking since data integration and comparative observation are often fraught with difficulties. With the right analysis, though, data reveal hidden patterns and connections assisting in process optimization and continuous improvement.

Methodology
The course takes place on five consecutive days and ends with an exam. Course content is developed and consolidated in interactive lectures, group work, presentations and case studies.

Further information regarding the certificate course "Big Data in Production"

First Day: "Introduction to Data Integration, Analysis and Data Mining"

• Data Analytics in Production, Machine Learning, Big Data Definition, Industrial Big Data Scenarios and Data Ware House

Second Day: "Data Source Systems"

• Practical session: RDBMS, OPC-UA, DDS, MQTT etc.
• Hadoop (HDFS/YARN difference)
• Extracting data from databases

Third Day: "Practical Session"

• Hive table structure on top of heterogenous data
• CPS data integration
• Data driven machine learning

Fourth Day: "From Data Analytics to Big Data"

• Machine learning on Big Data
• From Data Analytics to Visual Analytics
• Target-adaptive visualization and specific problem visual analytics (histogram)

Fifth Day: "Introduction to various visual analytics tools"

• Practical session web based visual analytics, html, css, d3.js, javascript etc.
• Holistic use case Big Data/Data Analytics VisAn in a production scenario
• Transfer of learned issues to specific scenarios of participants