inf607 - Business Intelligence II (Complete module description)

No participant requirement

Current module provides advanced business intelligence, data science with focus on enterprises and strong emphasis on big data and data analytics. Students of the course are provided with knowledge, which reflects current research and development in a data analytics domain.
Professional competence
The students:

• name and recognize the role of data analytics / data science as past of a daily business process in a particular company
• able to organize from managment perspective data analytis project
• being able to analyse advantages and disadvantages of different approaches and methods of the data analytics and being able to apply them in simple case studies
• obtain theoretical knowledge about data collection and modelling processes, including state of the art approaches and available best practices

Methodological competence
The students:

• being able to execute typical tasks of data analytis, and also being able to proceed deeper with respect to different approaches and methods
• gain a hans on experience and being able to understand advantages and disadvantages of different methods and being able to use obtained knowledge

Social competence
The students:

• build solutions based on case studies given to the group, for example design of regression model based on provided dataset
• discuss solutions on a technical level
• present obtained case studies solutions as part of the exercises

Self-competence 
The students:

• critically review provided offered information

After current course students will get advanced knowledge in the domains such as business intelligence and data analytics. Besides that, students will have a chance to have a deeper look into related technical fields such as InMemory Computing, Data Mining and Machine Learning, Big Data Processing with Distributed Systems (e.g. Apache Hadoop / Spark) from both, research and practical, perspectives. Students will be provided with real-world experience gather from business intelligence and data science related projects. Materials of the course are believed to be justified with current demands of data analytics market. Thus, providing students with relevant knowledge in order to give them advantages in future job.

• Jürgen Cleve und Uwe Lämmel - Data Mining; Berlin/München/Boston: Walter de Gruyter GmbH, 2020 (German)
• Max Bramer (2013): "Principles of data mining" (English)
• Ian Witten, Eibe Frank, Mark Hall (2011): "Data mining : practical machine learning tools and techniques" (English)
• Jure Leskovec, Anand Rajaraman, Jeffrey Ullman (2014): "Mining of massive datasets" (English)
• Sebastian Raschka und Vahid Mirjalili - Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow; Birmingham Mumbai: Packt Publishing, September 2017 (English)
• Aurélien Géron - Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems; Beijing Boston Farnham Sebastopol Tokyo: O'Reilly, September 2019 (English)

http://www.wi-ol.de/

At the end of the block course

Written examination or oral examination or term paper or referat or portfolio or practical exercises and written examination or practical exercises and oral examination.