Study programme

The course work contains 1 compulsory course:

• Knowledge and Data Engineering
   "Graph It Like It’s Hot: Mastering Data Science with the Semantic Web”
  Study how to build large knowledge graphs to understand the semantics of the data, and explains how they can be used to power search engines and augment AI tools like chat-GPT.
   

The elective training distincts primary and secondary electives.

Primary electives are programme related electives. You must select 8 courses out of a pre-defined list: 
 

• Algorithms for Decision Support
  "Model Complex Systems and their Interactions"
  Learn how to model complex problems and trace and describe complexity problems.
   
• Cloud and Edge Computing
  “Watching Cloud 9 & Edge of Tomorrow with maximum Fault Tolerance”
  Understand the key concepts and underlying principles of cloud and edge computing architectures of Amazon Web Services, Microsoft Azure or Google Cloud.
   
• Data Intensive Systems
  "Wrangling Big Data: Engineering the Future with noSQL & Spark”
  Discover the principles and technologies on which big computing is based (Spark, and Hadoop) and on which the new generation of databases  (Key-value Systems, Document stores, Graph Databases) operate.
   
• Data Mining
  ''Learning to avoid overfitting dramas and dimensionality curses one model at a time!”
  Master ways to mine the data and turn raw values into valuable information that can help into taking informative business decisions.
   
• Data Visualization
  "Visualize the Future and Relate to High Dimensions in Data Science"
  Data Visualization is key to unlocking insights from complex data, and this course equips you with the fundamental methods to analyze relational and high-dimensional datasets. Master the core principles by learning, implementing, and critically evaluating advanced visualization techniques.
   
• Data Wrangling
  "Where Dirty Data Goes In… and Slightly Less Suspicious Data Comes Out”
  Methods and tools required to transform heterogeneous, noisy, and large-scale data into reliable inputs for analytics and machine learning. Covers data extraction, validation, cleaning, integration, transformation, bias detection, and mitigation, with emphasis on data quality, integrity, and suitability for downstream analysis.
   
• Deep Learning
  "Deep Dive into Data Science by Navigating the Neural Waves and Optimizing the Future"
  Pick up easily and fast the details of Deep Learning, Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs), LSTMs,  Transformers, and Deep Neural Kernel Machines
   
• Explainable AI
  "ML-ucid & Explain will shed Light on AI Transparency"
  Acquire the necessary qualifications so that the concepts of Machine Learning explainability, explainability-by-design, social explainability, and assessment of explanations are central in the systems you build.
   
• Human Centered Machine Learning
  “Fair and explainable machine learning”
  Dive into techniques of developing and assessing machine learning systems in terms of fairness and explainability.
   
• Information Visualization
  “Visually Stunning: Designing Data with a View”
  Be prepared to design, construct, and evaluate information visualization pipelines and interactive data visualizations.
   
• Internship
  "Data Science in Practice”
  Conduct a (research) internship at a company or the university to explore a real-world data science problems and develop solutations.
   
• Machine Learning in Dynamic Data Environments
  "Understanding Dynamics in Machine Learning Systems…… It’s about time”
  Comprehend data stream mining, online machine learning, concept drift, Hoeffding Trees, change detection and windowing.
   
• Natural Language Generation
  “Extracting Knowledge from Natural Language”
  Learn the principles of Natural Language Understanding and Generation.
   
• Network Science
  "Linking Insights: Connecting the Dots in Data”
  Learn how to analyze the many aspects of networks: model their structure and growth, design algorithms that detect their communities, and investigate how ideas and diseases spread through them.
   
• Process Mining
  "From Events to Insights”
  Become proficient in process discovery, conformance checking, predictive process monitoring, and process analytics to optimize workflows and drive intelligent automation.
   
• Quantitative Data Analysis
  “High-Dimensional Data Is Just Noise Until You Learn Its Language—Then It Becomes Gossip with Statistical Significance”
  Extract reliable insights from high-dimensional, noisy datasets, by transforming complex measurements into robust conclusions while quantifying uncertainty.
   
• Reinforcement and Causal Learning
  "Beyond the Data: Cause & Effect, Action & Reward"
  Practice the Causal Inference and Reinforcement Learning even from data that have a changing probability distribution.
   
• Statistical Learning and Stochastic Processes
  "Statistically Speaking: A Stochastic Journey Through Data"
  Familiarize yourself with Monte Carlo simulations, Markov Chains, Poisson and, Gaussian processes, linear regression, and autoregressive models to be able to model stock markets, manage the queues in networks and in call centers, or predicts failure rates in machines to optimize maintenance schedules.

Secondary electives can be considered any master course offered by any department of the university with at least 7.5 ECTS, that has not been used as mandatory or primary elective. You have room for 1 course.

In order to expand the thematic range of your Master's programme, you can also choose to do a profile in your (elective) course part. A profile is a coherent set of courses of 30 EC on a single theme that is usually not a standard part of the programme. Within this Master's you can choose from the following profiles:

• Complex systems profile