This VAP (for TSP students) in Data Science and Network Intelligence (DANI) is home for creative problem-solvers who want to use data strategically to advance the ICT society.
We are cultivating a new type of quantitative thought leader who uses computational strategies to generate innovation and insights. Artificial Intelligence (AI) and Machine Learning (ML) approaches, well known from IT disciplines, are beginning to emerge in the networking domain. Recently, networking has become the focus of a huge transformation enabled by new technological and economic models resulting from virtualization and cloud computing. These techniques provide novel architectures supported by emerging technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV) and more recently, edge cloud and fog.
DANI combines rigorous technical training with field knowledge, industry insights and practice in critical thinking, teamwork, communication techniques, and collaborative leadership to generate data scientists with a deep understanding of how telcos/webcos evolve, who can add value a wide range of engineering fields. The program covers areas such as network intelligence, automation, communication services, large-scale data analytics, advanced machine learning and data-mining, information retrieval, natural language processing and web mining. It also includes foundational modules on topics such as programming for data analytics, Internet of things, services and optimization. Students enrolled in the program deepen their knowledge in an elective topic by working on a project in conjunction with either a research group or an industry partner. In addition to six key technical courses, a course on telecom management and economics, jointly taught with Institut Mines-Telecom Business School, gives students essential information about markets and business models.
Lectures from RS2M, RST, INF, ARTEMIS departments, IMT-BS. 30% of the lectures are taught by experts from the industry.
Here is the link to the programme.
Last modify 6 November 2020