University: Frankfurt University of Applied Sciences

High-integrity systems are software systems where reliability and safety really matter: safety-, mission- and business-critical applications. High-integrity systems are software systems where reliability and safety really matter: safety-, mission- and business-critical applications. In this project course, you put what you have learned into practice: you develop a high-integrity software application for real-world requirements, from analysis through design, implementation and testing to certification. You work in a research team with distributed responsibilities, organise your workflow with professional project-management tools, and adapt to a genuine research-and-development setting. We support you as you present your results in an industrial format and document them as a scientific paper. The course is worth 10 ECTS and runs for one semester in English. After completing this course, you will be able to: develop a high-integrity software application that meets real-world requirements work across all areas of software and systems engineering, including the certification of high-integrity software assess the challenges of applying scientific knowledge in a real-world R&D situation explore and adapt to a research-and-development environment organise a research team and share responsibilities within a group use modern tools to organise and manage your project give industrial-style presentations write up your work as a scientific paper This course is a hands-on project, not a lecture. Working in a small research team, you take on a high-integrity software application with real-world requirements and see it through the full development cycle. You explore and adapt to a research-and-development environment, organise the team and share out responsibilities, and use modern tools to plan and track your work. You apply your knowledge of software and systems engineering — and of certifying critical software — to a concrete problem, and you experience first-hand what it takes to turn scientific knowledge into a working solution. To round things off, you give an industrial-style presentation and write up your results as a scientific paper.

Learn how to apply technologies to real world situations, to analyse a data analysis project and to determine pre-processing steps. Data from automated processes is often complex and high-dimensional. In this course, we teach you modern methods of multivariate data analysis and how to apply them to real process data. You work through the full workflow of an analysis project: you examine the structure of your data, prepare it, choose suitable statistical methods, and interpret your results for a given problem. Along the way, you learn to handle common challenges such as collinearity and to use techniques such as data compression, alignment, and projection methods. Much of the course is hands-on — in group work on real-world problems and with professional software, you build the skills to plan and carry out your own analyses and to communicate your findings. After completing this course, you will be able to: understand how data from automated processes is structured apply data pre-processing methods such as compression, alignment, and transformation recognise the collinearity problem and choose suitable ways to deal with it transfer these technologies to real-world situations plan a data analysis project, determine its pre-processing steps, and test different statistical approaches interpret your results within a given application and problem setting draw conclusions and communicate the results and procedures of a project find and work with relevant scientific literature In this course, we teach you modern methods of multivariate data analysis and show you how to apply them to real data from automated processes. You explore techniques for data pre-processing — data compression, alignment, and transformation — and learn how to handle common challenges such as collinearity. We work with authentic data analysis projects: you determine the necessary pre-processing steps, test different statistical approaches, and interpret your results in a concrete problem setting. We also strengthen your ability to read scientific literature and to communicate your procedures and findings clearly.