Network Analytics and Data-Driven Engineering

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.