de Data-Driven Digital Transformation in Green Business

Angaben gemäß SPO
Zugehörigkeit zu Curriculum
Modulverantwortung
Günzel, Holger (Prof)
Anderl, Eva (Prof. Dr.)
Prüfungsformen
ModA
Lehr- und Lernformen
SU | 4 SWS
Lehrangebote WiSe 2024
SU | 4 SWS
wird nicht angeboten
Prüfungsangebote WiSe 2024
Status: vorläufig - kann sich jederzeit noch ändern
Erstprüfer:in
Anderl, Eva (Prof. Dr.)
Zweitprüfer:in
Brehm, Lars (Prof. Dr.)
Prüfungsform
ModA
Details zur Prüfung
  • Modularbeit: 
    • Analyse eines Datensatzes eines Green Business im Team und Ableitung von Handlungsempfehlungen 
    • Präsentation (20-30 Seiten) sowie Jupyter Notebook 
    • Abgabezeitpunkt: nicht relevant im WS2023/2024

  • Seminar paper: 
    • Analysis of a data set of a green business in a team and derivation of recommendations for action. 
    • Presentation (20-30 pages) and Jupyter Notebook 
    • Deadline: not relevant in WS2023/2024
Hilfsmittel
Voraussetzungen und Verwendbarkeit
Voraussetzungen für die Teilnahme
Verwendbarkeit
Inhalte und Qualifikationsziele
Status: vorläufig - kann sich jederzeit noch ändern
Learning Objectives / Competencies:

After completing the module, students are able to independently carry out a data analysis project on digital transformation in green companies and derive recommendations for action for sustainable companies from this.

  • Professional competence
    • Explain key concepts of data-driven digital transformation and data science
    • Discuss prerequisites and potential challenges of Big Data analytics and artificial intelligence.
    • Select appropriate analysis methods for given problems
    • Assess the resource consumption of data science projects
    • Apply selected data science methods and tools in a sustainable way
    • Derive implications of data analysis projects for sustainable management
  • Methodological competence
    • Perform quantitative data analysis
    • Use the Python programming language
  • Social competence:
    • Work on topics in a collaborative and self-organized manner within the framework of group work
    • Verbalize subject content adequately and to lead corresponding subject discussions with peers

Contents:

This course is about using (Big) Data and Artificial Intelligence to drive digital transformation in green businesses. Big Data and AI offer tremendous potential for advancing sustainable business models, e.g., by supporting low-carbon energy systems with high integration of renewables and energy efficiency (Vinueasa et al. (2020) or developing sustainable manufacturing processes and capacities for the circular economy.

The course covers the basic principles or concepts underlying Data Science, provides implementation examples in Python, and discusses the application of the results in a green business context.
  • Importance and application of Data Science in the field of Digital Green Business
  • Basic concepts and techniques of applied Data Science in Python
  • Supervised vs. unsupervised learning
  • Regression
  • Classification
  • Similarity and Clustering
  • Causality vs. correlation
  • Avoidance of overfitting
  • Analysis of model performance
  • Sustainability of Machine Learning and Artificial Intelligence
  • Data Analytical Thinking

Teaching and learning methods:
  • Seminar-based teaching
  • Guest lectures
  • Project work
  • Group work
  • Learning Lab workshops

Value of the grade in the final grade of the master's degree (master's certificate): 6 / 90

Literature:
  • Chauhan, C., Parida, V. and Dhir, A., 2022. Linking Circular Economy and Digitalisation Technologies: A Systematic Literature Review of Past Achievements and Future Promises. Technological Forecasting and Social Change, 177, p.121508, https://doi.org/10.1016/j.techfore.2022.121508.
  • Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020). Towards the systematic reporting of the Energy and carbon footprints of machine learning. Journal of Machine Learning Research, 21(248), 1-43.
  • O'Neil, C. and Schutt, R. (2014), Doing Data Science: Straight Talk from the Frontline, O'Reilly, Sebastopol.
  • Provost, F. and Fawcett, T. (2013), Data Science for Business: What You Need to Know About Data Mining and Data Analytic Thinking, O'Reilly, Sebastopol.
  • Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S.D., Tegmark, M. and Fuso Nerini, F. (2020), The Role of Artificial Intelligence in Achieving the Sustainable Development Goals. Nature Communications, 11(1), 233, https://doi.org/10.1038/s41467-019-14108-y.

Additional references will be included/presented in the objective sheet for each semester of study. 
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