# Curricula

Data Science and Data Engineering require skills and knowledge from multiple disciplines. This prompted the Departments of Mathematics and Computer Science at TUM to jointly create an integrative study program in Data Science. An integrative study program allows the exchange of expertise amongst faculties so that the master´s programs "Mathematics in Data Science" and "Data Engineering and Analytics" can cover a large spectrum of topics.

Lectures on advanced database technology, distributed systems, IT security, machine learning, and scalable programming methods are provided by the Department of Informatics. Statistics, mathematical representation of large and high-dimensional data sets, their dimensionality reduction, and their classification to mine meaningful information, cryptography and optimization are taught by the Department of Mathematics. The program Mathematics in Data Science emphasizes optimization and statistics, adding basic knowledge to the Computer Science aspects. The program Data Engineering and Analytics targets advanced database technology and scalable programming methods.

Both master programs' curricula are structured as outlined below. Data Engineering and Analytics puts a focus on the modules in the left-hand side, whereas Mathematics in Data Science focuses on the modules on the right-hand side.

## Module Synopsis

Master Data Engineering and Analytics | and | Master Mathematics in Data Science |

Foundations Data Engineering | Foundations Data Analytics | |

Data Engineering | Data Analytics | Data Analysis |

Data Engineering | Special Topics in Data Analytics | Data Analysis |

Advanced Topics in Data Engineering | Data Science in Society | Advanced Topics in Data Science |

Master's Thesis |

For details, please consult the respective websites for Data Engineering and Analytics and Mathematics in Data Science.