Type:Module
ECTS-Credits:6.0
Scheduled in semester:2
Semester Hours per Week / Contact Hours:52.0 L / 39.0 h
Self-directed study time:141.0 h
Module coordination/Lecturers
- Assoz. Prof. Dr. Johannes Schneider
(Modulleitung)
Curricula
Master's degree programme in Information Systems (01.09.2019)Events
Description
Data Science covers statistical and exploratory techniques that are used to make sense of the vast and complex data sets that have emerged in business. Data Science is one of the core topics of the degree programme, so the course also provides a basis on which students can choose their electives. Students learn to detect patterns in large data sets in quantitative and qualitative formats to translate them into actionable insights. The course covers seven primary topics:
• Data visualisation and exploration
• Supervised learning techniques for regression (e.g. logistic regression)
• Supervised learning techniques for classification (e.g. classification trees)
• Unsupervised learning techniques (e.g. clustering, dimensionality reduction)
• Fundamentals of deep learning
• Text mining (e.g. topic modelling)
• Hands-on labs with Python
Learning Outcomes
After successful completion of the course, students will
Professional competence
• understand the basic concepts and methods of data mining and predictive analytics
• be able to assess the assumptions and quality of statistical models
Methodological competence
• know and be able to select and apply the right statistical models for a given task or data set
• be able to derive actionable insights from statistical results
• know basic visualisation and storytelling techniques
Social competence
• communicate effectively using visualisations
• understand different stakeholder perspectives in a data mining project
Personal competence
• critically reflect on analytical outcomes
• improve and mitigate self-inflicted errors
Technological competence
• be able to use Python including their libraries such as scikit-learn and matplotlib to apply machine learning
and to create visualisations
Qualifications
Lectures Method
• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
Admission Requirements
• Basic knowledge of statistics and linear algebra is recommended.
Literature
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: With Applications in R. New York, NY: Springer.
• Witten, H., Eibe, F., & Hall, M. (2016). Data Mining: Practical Machine Learning Tools and Techniques. San Francisco, CA: Morgan Kaufmann Publishers.
• Provost, F., & Fawcett, T. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media.
Exam Modalities
Written exam (90min)