Applied Data Science MSc Programme at UU

INFOMVABD is a practical course on visual analytics: the art of exploring large, complex, and time-dependent data through effective visual representations, without losing sight of the analytical questions behind the data. We apply principles of data representation, visual encoding, and interactive analysis in a structured manner. Especially in the practical parts of the course, we learn how to design and use modern visual analytics workflows to explore real-world datasets efficiently.

Data representation: Large datasets come in many forms, such as time series, multidimensional, and unstructured data. Learning how to represent such data appropriately is essential for making it accessible to visual exploration and meaningful analysis.

Visual analysis techniques: Modern visual analytics combines perception, interaction, and design. Leveraging techniques such as linked views, annotations, parallel coordinates, node-link diagrams, projections, and treemaps is an important part of turning raw data into interpretable insights.

Context: Visual analytics is a vital skill in data science and decision-making, but it also applies broadly to any field where complex data must be explored, understood, and communicated clearly.

More details can be found here.

Data Science MSc Programme at UU

Data Science has applications in all corners of society and research. By showcasing a wide variety of data science projects and research subfields, the student engages with both real-world practice and current developments in data science.

The colloquium enables students to develop knowledge of the wide variety of data science applications and research areas, to become aware of the actual challenges that various companies and governmental organizations face in their data practices, and to learn from practitioners and researchers about challenges and opportunities in a career in data science. 

Additionally, the colloquium trains students in presenting a research topic and in delivering presentations that disseminate knowledge and educate others on a particular data science topic.

More details about INFOMCDASC can be found on DASCpedia.

Game and Media Technology MSc Programme at UU

INFOMOV is a practical course on optimization: the art of improving software performance, without affecting functionality. We apply high-level and low-level optimizations in a structured manner. Especially for the low-level optimizations, we must intimately understand the hardware platform (CPU, GPU, memory, caches) and modify our code to use it efficiently.

Vectorization: Modern processors achieve their performance levels using parallel execution. This happens on the thread level, but also on the instruction level. Being able to produce efficient vectorized code is an important factor in achieving peak performance.

GPGPU: Graphics processors employ a streaming code execution model, taking vectorization to extremes, both in the programming model and the underlying architecture. Leveraging GPU processing power is an important option when optimizing existing code.

Context: Optimization is a vital skill for game engine developers, but it also applies to other fields.

More details can be found here.