2IAB0 Data analytics for engineers

Learning goals

  • Students gain insight in basic techniques for processing large amounts of data in an efficient, reliable, and consistent way.
  • Students develop skills in understanding, interpreting, and documenting data and information in the context of realistic scenarios.
  • Students get understanding of the data life cycle and develop skills for structuring their solutions of practical problems along the phases of the data life cycle.
  • Students apply data analytics techniques to realistic data sets in which they can recognize the demands within their area of specialization.
  • Students obtain basic knowledge of statistical concepts and techniques and develop skills to apply them in practice.
  • Students learn to implement their solutions for data analytics problems in a programming language (Python), and apply a structured and systematic approach to data processing.

Links

Staff

  • Natalia Sidorova - Dr. Natalia Sidorova is assistant professor at the PA group. She actively works on topics related to process modeling and verification. The application domains include business processes and distributed systems. She has published more than 70 conference and journal papers. She is active in the Health and Wellbeing Action Line of EIT ICT Labs, taking Read More ...
  • Marwan Hassani - Dr. Marwan Hassani is assistant professor at the PA group with a focus on Real-Time Process Mining. His research interests include stream data mining, sequential pattern mining of multiple streams, efficient anytime clustering of big data streams and exploration of evolving graph data. He uses customer journey optimizationa and privacy-aware process mining as use cases for his Read More ...
  • Renata Medeiros de Carvalho - Position: UD Room: MF 7.146 Tel (internal): 4144 Links: Courses External assignments Projects Publications External links: Scopus page DBLP page TU/e page Recent courses Recent external assignments Recent projects Recent publications

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