Objectives of Programme's Learning Outcomes

• Implement an engineering approach dealing with a set problem taking into account technical, economic and environmental constraints
• Understand the stages of an engineering approach
• Implement a chosen solution in the form of a drawing, a schema, a plan, a model, a prototype, software and/or digital model
• Identify and acquire the information and skills needed to solve the problem
• Understand the theoretical and methodological fundamentals in science and engineering to solve problems involving these disciplines
• Identify, describe and explain basic scientific and mathematical principles
• Identify, describe and explain the basic principles of engineering particularly in their specialising field
• Select and rigorously apply knowledge, tools and methods in sciences and engineering to solve problems involving these disciplines
• Communicate in a structured way - both orally and in writing, in French and English - giving clear, accurate, reasoned information
• Argue to and persuade customers, teachers and a board both orally and in writing
• Demonstrate thoroughness and independence throughout their studies
• Develop their scientific curiosity and open-mindedness

Learning Outcomes of UE

- analyze various kinds of data and signals
- understand the underlying theory for the development of basic components of a numerical signal processing system
- understand and explain the theory, models and techniques used for statistical data analysis
- analyse datasets with a given software (Python, MATLAB, ...)
- interpret the results from the software, showing an understanding of the theory

UE Content: description and pedagogical relevance

- linear and invariant numerical systems; frequency analysis of numerical signals and systems; Shannon theorem and sampling; discrete Fourier transform; spectral analysis of random signals; numerical filters
- descriptive techniques for data analysis (principal components analysis, discriminant factorial analysis)
- classical models of data analysis (analysis of variance, linear regression)
- data mining / machine learning (classification and clustering) 

Prior Experience

Algebra, Analysis, probability and statistics, functions of complex variables