HAL Teaching
👨🏫 Teaching Overview
As an ATER (Attaché Temporaire d'Enseignement et de Recherche) at Université Lumière Lyon 2, I have extensive teaching experience across multiple academic levels, from undergraduate to master's programs. My teaching spans statistics, data science, programming, and mathematical modeling, with a focus on practical applications and hands-on learning.
I have taught 317 hours across various courses, combining theoretical foundations with practical implementation using modern statistical software and programming languages.
📊 Teaching Statistics
317
Total Hours
10
Different Courses
4
Academic Levels
3
Years Experience
Teaching Philosophy: I believe in combining theoretical rigor with practical application, ensuring students understand both the mathematical foundations and real-world applications and usefulness of statistical methods. I always strive to adapt my courses to the needs and profiles of my students, combining academic rigor with kindness, never being judgmental. I believe that creating a supportive learning environment where students feel comfortable making mistakes and asking questions is essential for effective learning and personal growth.
📚 Current Teaching (Academic Year 2024-2025)
🎓 Lectures (Cours Magistraux)
Data Science - Analysis 2
L2 - Computer Science
10h
January 2025 - February 2025
Multivariable calculus covering continuity, differentiability, partial differentiation, convexity and optimization.
🔬 Practical Sessions (Travaux Dirigés)
Python Programming
M1 - Computer Science
40h
September 2024 - December 2024
Comprehensive Python programming for data science, focusing on text analysis. From introduction covering syntax, data structures, classes, and inheritances to popular libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn.
Inferential Statistics
L3 - MIASHS
61h total
• 21h (Sep 2022 - Dec 2022)
• 20h (Sep 2023 - Nov 2023)
• 20h (Sep 2024 - Nov 2024)
• 20h (Sep 2023 - Nov 2023)
• 20h (Sep 2024 - Nov 2024)
Statistical inference methods including hypothesis testing, confidence intervals, and parameter estimation. Practical implementation using R.
C Programming
L2 - Computer Science
40h
January 2025 - March 2025
Introductory C programming course covering syntax, data structures, memory management, and algorithm implementation.
Algebra and Data Analysis
L2 - MIASHS (Mathematics, Computer Science, Applied to Human and Social Sciences)
20h
February 2025 - April 2025
Introduction to linear algebra and its applications in data analysis, including matrix decompositions, PCA, and other dimensionality reduction techniques.
Linear Models
L3 - MIASHS
74h total
• 32h (Jan 2023 - Apr 2023)
• 22h (Feb 2024 - Apr 2024)
• 20h (Feb 2025 - Apr 2025)
• 22h (Feb 2024 - Apr 2024)
• 20h (Feb 2025 - Apr 2025)
Introduction to univariate and multivariate linear regression models, ANOVA, and model diagnostics. Practical implementation using R.
Exploratory Data Analysis
L3 - Computer Science
12h
February 2025 - March 2025
Exploratory data analysis techniques, data visualization, dimensionality reduction techniques. Practical implementation using R.
Linear Modeling
L3 - Computer Science
12h
February 2025 - March 2025
Introduction to univariate and multivariate linear regression models. Practical implementation using Python.
Supervised Learning
M1 - Computer Science
28h total
• 14h (Feb 2024 - Mar 2024)
• 14h (Feb 2025 - Mar 2025)
• 14h (Feb 2025 - Mar 2025)
Supervised machine learning algorithms including classification, regression methods, neural networks, model validation, and performance evaluation. Practical implementation using R.
Data Science - Analysis 1
L1 - Computer Science
10h
March 2025 - April 2025
Introductory calculus covering continuity, differentiability, partial differentiation, convexity and optimization for univariate functions.
Data Science - Analysis 2
L2 - Computer Science
10h
March 2025 - April 2025
Multivariable calculus covering continuity, differentiability, partial differentiation, convexity and optimization.
🎯 Teaching Subjects & Expertise
📊 Statistics
Inferential Statistics
Linear Models
Data Analysis
Linear Models
Data Analysis
💻 Programming
Python
C/C++
R
C/C++
R
🤖 Machine Learning
Supervised Learning
Unsupervised Learning
Data Mining
Unsupervised Learning
Data Mining
📈 Data Science
Exploratory Analysis
Visualization
Applied Statistics
Visualization
Applied Statistics
🎓 Academic Levels Taught
🔰 L1 - First Year Undergraduate
Data Science - Analyse 1 (10h) - Introduction to calculus.
📚 L2 - Second Year Undergraduate
Multiple courses (80h total) - Data science, programming in C, and applied mathematics for MIASHS students.
🎯 L3 - Third Year Undergraduate
Statistical methods (189h total) - Inferential statistics, linear models, and data analysis across multiple programs.
🚀 M1 - Master's Level
Advanced topics (68h total) - Machine learning, Python programming, and supervised learning algorithms.
🔧 Teaching Tools & Methods
My teaching methodology combines theoretical foundations with hands-on practice using modern statistical software and programming environments.
🛠️ Software
R, Python, C/C++
VSCode
RStudio
VSCode
RStudio
📊 Methods
Interactive Lectures
Hands-on Exercises
Real Dataset Analysis
Hands-on Exercises
Real Dataset Analysis
🎯 Focus
Practical Applications
Problem Solving
Critical Thinking
Problem Solving
Critical Thinking
📈 Assessment
Project-based Assesment
Continuous Evaluation
Sit-down Exams
Continuous Evaluation
Sit-down Exams