Students are able to
• gain in-depth knowledge of data management, including data acquisition, integration and cleansing, and apply this knowledge in practice.
• understand the individual phases of the analytics process and explain them in more detail.
• understand and derive the connection between business intelligence / business analytics and controlling.
• understand and explain the basics of the Business Intelligence (BI) concept and the importance of data analysis for decision-making in companies.
• prepare data effectively by applying data management and cleansing techniques to ensure high quality data for BI analysis.
• identify, organize and prepare data from various sources to make it usable for BI/AB analysis.
• analyze and interpret data to gain insights into business trends, patterns and problems.
• use selected BI applications such as MS Power BI / MS Azure on a case-by-case basis.
• create dynamic power queries based on MS Excel / MS Power BI or apply pivot analyses and display the results dynamically in dashboards.
• recognize the potential of cloud computing using MS Azure and its services.
• apply the content taught in practice using case studies.
• apply BI concepts in real business situations.
• independently develop BI solutions by implementing data integration, analysis and visualization in a case study.

none

• Basics of data management
• Basics of business intelligence and business analytics
• Data management with MS Excel and Power Query/Pivot
• MS Power BI dashboards and reports
• MS Power BI Enterprise Architecture
• Integration of simple MS Azure services

Carpenter, J./Hewitt, E. (2016): Cassandra: The Definitive Guide. Distributed Data At Web Scale. Beijing, Boston, Farnham, Sebastopol, Tokyo: O'Reilly.
Celko, J. (2014): Joe Celko's complete guide to NoSQL. What every SQL professional needs to know about nonrelational databases. Waltham, MA: Morgan Kaufmann.
Cordon, C./Garcia-Milà, P. et al (2016): Strategy Is Digital. How Companies Can Use Big Data in the Value Chain. Heidelberg: Springer-Verlag GmbH.
Edlich, S./Friedland, A./Hampel, J./Brauer, B. (2010): NoSQL. Einstieg in die Welt nichtrelationaler Web 2.0 Datenbanken. München: Hanser.
Freiknecht, J./Papp, S. (2018): Big Data in der Praxis. Lösungen mit Hadoop, Spark, HBase und Hive. Daten speichern, aufbereiten, visualisieren. 2., erw. Aufl. München: Hanser.
Jung, H. H./Kraft, P. (Hrsg.) (2017): Digital vernetzt. Transformation der Wertschöpfung. Szenarien, Optionen und Erfolgsmodelle für smarte Geschäftsmodelle, Produkte und Services. München: Carl Hanser Verlag.
Kleppmann, M. (2017): Designing data-intensive applications. The big ideas behind reliable, scalable, and maintainable systems. Sebastopol, CA: O'Reilly Media.
Papp, S./Weidinger, W./Meir-Huber, M. (2019): Handbuch Data Science. Mit Datenanalyse und Machine Learning Wert aus Daten generieren. München: Carl Hanser Verlag.
Pochiraju, B./Sridhar, S. (2019): Essentials of Business Analytics. An Introduction to the Methodology and its Applications. Heidelberg: Springer-Verlag GmbH.
Runkler, T. A. (2012): Data Analytics. Models and Algorithms for Intelligent Data Analysis. Wiesbaden: Vieweg+Teubner Verlag.
Schuster, H./Arendt-Theilen, F./Morgenthaler, R. (2018): Power BI Desktop - Einstieg und Lösungen. Daten gekonnt analysieren und visualisieren. München: Carl Hanser Verlag.
Werther, I. (2013): Business Intelligence. Komplexe SQL-Abfragen am Beispiel eines Online-Shops; inkl. Testdatenbank mit über zwei Millionen Datensätzen. 1. Aufl. München: Carl Hanser Verlag.
Nelles, S. (2022): Power BI mit Excel. Das umfassende Handbuch, 3. Aufl. Bonn: Rheinwerk Verlag.
Nelles, S. (2022): Excel im Controlling. Das umfassende Handbuch, 5. Aufl. Bonn: Rheinwerk Verlag.
Schels, I. (2020): Business Intelligence mit Excel. Datenanalyse und Reporting mit Power Query, Power Pivot und Power BI Desktop, 2. Aufl. München: Carl Hanser Verlag.
Schön, D. (2022): Planung und Reporting im BI-gestützten Controlling. Grundlagen, Business Intelligence, Mobile BI und Big-Data-Analytics. 4. Aufl. Wiesbaden: Springer.
Seiter, M. (2023): Business Analytics. Wie Sie Daten für die Steuerung von Unternehmen nutzen, 3. Aufl. München: Vahlen.

Lecture, discussion, individual and group work, working on practical case studies

Integrated module examination
Continuous assessment type: presentation, case work, individual and group work, written final examination