Modern web applications can often be described in terms of cooperation and sharing, both on the level of the users of the application and on the level of the application and the service providers. This course covers the most prevalent security challenges of web applications, from a theoretical and practical perspective.
The purpose of this course is to introduce security practices within the Software Development Lifecycle (SDLC) at the requirements, design, implementation, verification, and after release stages of software development.
This course is the guide to the cybersecurity issues arising throughout the entire development process. We consider the development from the security perspective from the beginning stage until the final release and beyond. The course is adapted to give a solid introduction to non-security-experts mainly and addresses both how professionals (developers, managers, decision-makers) can utilize security to improve (software-based) products/services, and how they are affected by security issues and challenges.
Whether you are a software developer in a bank or telecom company, or you are a product manager in a gaming company, this course will be relevant for you.
The manufacturing industry collects increasingly large volumes of big data, that is, data at high speed, generated from a wide range of sources in different formats and quality levels. But what is data without insight? This course will help you master the fundamental concepts of big data, cloud computing and smart decision-making for industrial analytics.
Designed specifically for manufacturing sector professionals, this Master’s course provides knowledge and insights in handling and processing data, using machine learning and data analytics in the cloud environment. You will learn machine learning-based solutions for industrial applications, such as smart decision-making and predictive maintenance, using state of the art cloud platform tools.
The course will give insights in fundamental concepts of machine learning and actionable forecasting using predictive analytics. It will cover the key concepts to extract useful information and knowledge from big data sets for analytical modeling
In this course you will learn state-of-the-art statistical modelling for the purpose of analysing industrial data. The course also presents the basics of relational databases and data manipulation techniques needed to prepare the data for analysis.
This course makes you acquainted with the concept of systems-of-systems (SoS), which means that independent systems are collaborating. It gives you an understanding why SoS is an important topic in the current digitalisation and provides a theoretical and practical foundation for understanding important characteristics of SoS. It also gives you a deeper knowledge in a number of key concerns that need to be considered when engineering SoS.
Admitted students to this course may join the course any time between August 30 and October 24, 2021. With the recommended study pace of 25%, the course would take approximate seven calendar weeks to complete. Higher or lower study pace is possible as long as the course is finished no later than January 16, 2022.