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Data Science & AI Teaching

Higher Education – Data Science & AI Teaching

Design and delivery of mathematics, statistics, machine learning and AI courses.

From theory to practice

Mathematical foundations, machine learning and AI, applied to real datasets

Project Details

Client

Catholic University of Lille

Sector

Higher Education – Data Science & AI Teaching

Year

2022–2026

Key Technologies

PythonTensorFlowPyTorchRMATLAB

Mission

Lecturer - Mathematics, Data Science & Artificial Intelligence

Data Science & AI Education

Master's-level teaching, Lille, France.

Master's-level teaching: design and delivery of courses covering the mathematical and probabilistic foundations of data science, AI and machine learning. Curriculum design: creation and updating of a program balancing theory, advanced applications and hands-on work. Student support: personalized follow-up, project supervision, preparation for research or industry. Delivery of data-driven projects: hands-on application of concepts through case studies and analysis of real-world datasets.

Contexte & Environnement

Université Catholique de Lille: data science, AI and machine learning training for master's students. Audience: students from mathematics, computer science or engineering backgrounds, specializing in AI. Collaborative environment: in-person and/or hybrid courses, hands-on workshops, use of modern digital tools, multidisciplinary interaction. Lectures, tutorials, supervised projects, continuous assessment, interactive resources, personalized supervision.

Team

Lecturer in mathematics, data science and AI, for master's students

Environment

Lille, France – Python, R, MATLAB, Jupyter Notebooks, PyTorch, Tableau, Power BI

Objectifs Cles

1Foundations: Basic mathematical tools – calculus, logic, neural networks (Perceptron, MLP), epistemology...

2Systems theory: introduction to complex dynamic & adaptive systems.

3Advanced probability: measure, distributions, asymptotic statistics, stochastic processes, time series, information geometry, RNN/LSTM models, Bayesian networks.

4Data observation and analysis: descriptive statistics, exploration, visualization.

5Inference and estimation: parameter estimation, experimental design, hypothesis testing, decision trees, Bayesian models.

6Statistical modeling: linear regression (simple, multiple), alternative methods, parametric and non-parametric tests.

7Nonlinear modeling: probabilistic graphical models, networks, percolation theory, extreme values, agent-based modeling, spatial statistics.

8Practical projects: case studies, large dataset analysis, preparation for the professional environment.

Technologies & Infrastructure

PythonNumPyPandasSciPyMatplotlibTensorFlowPyTorchRMATLABLaTeXJupyter NotebooksTableauPower BISQL

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