Network Analytics and Data-Driven Engineering

About this programme

This project course introduces students to data-driven engineering of networks and cloud systems. Using methods from statistical learning, students will develop and evaluate, for instance, models for prediction and forecasting of Key Performance Indicators (KPIs) and for anomaly detection. The models will be fitted and evaluated using testbed measurements or traces from operational systems. The functions built from these models are designed for real-time execution. To develop the models, tools and packages from data science will be used, e.g., Jupyter notebook, scikit-learn, TensorFlow. The course is structured as two consecutive project blocks. Each block starts with introductory lectures that give background and discuss concepts for the specific project, followed by project execution, writing of a report, and interview. Please note that Students not located in Sweden may have problems attending a course at KTH. You could meet obstacles if you're required to pay fees or if you do not have a Swedish Mobile BankID. Please note: all information from the Course syllabus is available on this page in an accessible format. This is a project course, where the students carry out analysis projects individually or in small groups. The projects use data from real systems, for example operational data from networks or computer clouds. In total 180 higher education credits of which at least 90 higher education credits in computer science, electrical engineering or an equivalent discipline Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how