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Analytics and Big Data

Course Logistics

Prof. Dr. Gerit Wagner

Agenda

  1. Introduction to Analytics
  2. Descriptive Analytics
    • Data Warehouse Systems
    • Online Analytical Processing (OLAP)
  3. Predictive Analytics
    • Subject of Predictive Analytics
    • The Analytics Process
    • Data Preparation
    • Methods, Algorithms and Applications
  4. Big Data

Literature

Part 1:

  • Sharda, R., Delen, D., & Turban, E. (2018). Business intelligence, analytics, and data science: a managerial perspective.
  • Martínez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernández-Orallo, J., Kull, M., Lachiche, N., Ramirez-Quintana, M. J. & Flach, P. (2019). CRISP-DM twenty years later: From data mining processes to data science trajectories. IEEE Transactions on Knowledge and Data Engineering, 33(8), 3048-3061.
  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly 36(4), 1165-1188.

Part 2:

  • Vaisman, A.; Zimányi, E. (2016): Data Warehouse Systems: Design and Implementation (Data-Centric Systems and Applications), Springer.
  • Schutt, R.; O’Neil, C. (2014): Doing Data Science, O’Reilly Media.
  • Raschka, Sebastian (2015): Python Machine Learning, Packt Publishing.

Part 3:

  • Schmarzo, B. (2016): Big Data MBA, Wiley.
  • Bengfort, B., Bilbro, R., & Ojeda, T. (2018). Applied text analysis with python: Enabling language-aware data products with machine learning. O’Reilly Media, Inc. 

Part 4:

  • Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98.
  • Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626-639.