Computer Science

The course conveys an understanding of the internal structure and implementation of digital computers. To impart this knowledge, we first explain how the interface between hardware and software is typically constructed. In addition to explaining the interface between hardware and software, this course introduces the ideas behind the hardware implementation of a processor. This course includes a brief introduction to the C programming language, which is widely used as a systems programming language.

This course explores a range of key concepts and topics in Data Science, accompanied by practical implementations using Python. This ranges from the initial stages of data collection and preparation through to data analysis and modelling. Students become familiar with widely-used Python packages for data analysis, including Pandas, Matplotlib, and scikit-learn. 

This course sets the basics for an understanding of multimodal communication between humans and multimodal interaction between humans and machines. The course begins with clarifying the basic principles of human-human communication and human-machine interaction. The course then describes the processes taking place in humans when perceiving auditory, visual, and tactile signals, as well as how these perceptions are integrated in order to form a multimodal perception. The signals can be generated and received by machines which are able to interact with humans in limited domains. The set-up of such machines is discussed, and limitations as well as potential solutions to overcome these limitations is explained.

In this internship, students work closely with their host organization for up to 12 hours per week over the course of a semester. Final assessment is comprised of a mentor evaluation, a self-evaluation/reflection, and an organizational report.

Python has rapidly become the standard in scientific computing. It is however much more than that, receiving much excitement about the application of Python to finance, medicine, mobile technology, online gaming, film industry. Its appeal continues to grow in both academia and industry. Much of the advances of medical technology has been due to Python. This is a an intensive Python programming course with numerous medical and health-based applications. Due to the transferability of these skills, students also study examples from investment banking and quantitative finance. The course assumes no prior knowledge of the Python programming language. However, an interest in biomedicine/health is essential.

This course provides an introduction to the quantitative analysis of data, blending classical statistical methods with recent advances in computational, and machine learning. Students cover key topics such as the challenges of analyzing big data using statistical methods, and how machine learning and data science can aid in knowledge generation and improve decision-making. Students also explore quantitative methods of text analysis, including mining social media and other online resources.