This training offers a hands-on exploration of graph theory and network analysis, empowering participants to analyse and interpret complex data structures. Through practical exercises, participants will learn to apply network analysis techniques across various domains, from social network analysis to infrastructure and beyond.
The course emphasises real-world applications, teaching participants to harness the power of graph algorithms and analytics tools to uncover insights, optimise networks, and drive decision-making.
This module is part of our Data & AI curriculum.
Duration: 3h
Language: Available in English, French, and German. The supporting material is only available in English.
Number of participants: up to 15
Available under intra-company course (i.e. dedicated session on demand)
Objectives
By the end of this course, participants will be able to:
- develop a solid comprehension of the significance of graph and network analytics in operational intelligence and decision-making processes;
- learn the fundamental principles and methodologies of graph theory and its application in data analysis;
- explore a variety of technical algorithms used in graph and network analytics;
- develop practical skills in implementing and applying graph algorithms to real-world datasets through hands-on exercises.
Content
Introduction to graph theory and fundamentals:
- Basic concepts of graphs, nodes, and edges;
- Network analytics and the most common application scenarios in a business context.
Fundamentals of graph analytics:
- Common graph algorithms as centrality measure and community detection
Practical hands-on exploration:
- Graph algorithms implementation using Python and libraries like NetworkX, and tools such Gephi and Neo4j
Case studies and real-world applications:
- Case studies of graph analytics implementations across various industries
Target audience
- Data analysts: professionals responsible for analysing and interpreting data to extract meaningful insights.
- Data scientists: experts in machine learning and AI algorithms who seek to enhance their skills and knowledge in implementing AI / GenAI solutions.
- Operational managers: leaders overseeing operational processes and seeking to leverage GenAI technology to optimise efficiency and decision-making.
- Business intelligence professionals: individuals involved in gathering, analysing, and presenting data to support business decisions and strategies.
- IT professionals: those interested in understanding and implementing AI and GenAI solutions within their organisation's technological infrastructure.
Our lead experts
This training is coordinated by Thierry Kremser, Partner at PwC Luxembourg and Andreas Braun, Director at PwC Luxembourg.
Thierry is leading the Technology Consulting services. He has over 20 years of experience in IT strategy with a specific focus on the quality and efficiency of IT departments. He has managed numerous innovation and transformation programmes and has also been involved in IT risk and security projects. Thierry is also leading the data and AI services.
Andreas is an expert in biometrics and artificial intelligence.
He has more than 15 years of experience in public and private sector projects from AI strategy and regulation, to R&D projects and proof-of-concept implementations.
Prior to joining PwC, he was group leader at a leading research institution, focusing on biometrics, AI, and IoT. He has been a university lecturer, authored more than 100 scientific publications and holds several patents.
