This course provides an in-depth exploration of time series data, focusing on both forecasting and identifying irregular patterns or anomalies.
Participants will learn to apply advanced algorithms and statistical methods to analyse time-structured data, uncovering trends, cycles, and forecasts. The course also delves into anomaly detection, teaching participants how to identify and interpret deviations that may indicate critical insights or warning signs in various contexts, such as financial markets, manufacturing, or cybersecurity.
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 gain :
- Foundational understanding: develop a solid comprehension of the significance of time-series analysis in operational intelligence and decision-making processes.
- Core techniques mastery: learn fundamental techniques and algorithms used in analysing and interpreting time-series data.
- Visualisation exploration: explore visualisation tools and techniques to effectively communicate insights derived from time-series data.
- Tailoring solutions: gain insights into tailoring time-series data analysis and presentations to different stakeholders and audiences, ensuring effective communication and decision-making.
Content
- Fundamentals of time-series algorithms: exploration of techniques and algorithms used in time-series analysis, including moving averages and exponential smoothing.
- Practical hands-on exploration: implementation of time-series algorithms using Python and libraries such as Pandas and Statsmodels.
- Tools and resources for time-series analysis: overview of software and tools for time-series analysis, including Python libraries, R packages, and cloud-based solutions.
- Case studies and applications of time-series analysis: analysis of successful applications of time-series analysis in various industries, including finance, healthcare, and retail
Target audience
- Data analysts: professionals responsible for analysing and interpreting textual data to extract insights and patterns relevant to operational analysis.
- Data scientists: experts in machine learning and data analysis interested in expanding their knowledge and skills in NLP techniques for text processing and analysis.
- Operational Intelligence Managers: leaders overseeing operational processes and seeking to leverage NLP solutions to extract actionable insights from textual data.
- Business intelligence professionals: individuals involved in gathering, analysing, and presenting textual data to support business decisions and strategies, looking to enhance their toolkit with NLP capabilities.
- IT professionals: those interested in understanding and implementing NLP solutions within their organisation's technological infrastructure, to extract valuable insights from textual data for operational intelligence purposes.
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.
