This is not your traditional math class! Rather than focusing on rote memorization or rigid formulas, this course emphasizes developing a deeper understanding of mathematical and statistical reasoning. Instead of simply teaching students how to calculate an average (mean), for instance, I will guide them toward discovering how to develop a value that they believe best represents an average. This reflective approach helps students think critically about the meaning and application of mathematical concepts, rather than just performing calculations. The course prioritizes practical applications over theoretical repetition. You will engage with real-world case studies and utilize modern tools such as MATLAB, R, and Python to solve problems across fields like medicine, social sciences, and business. We will explore statistical reasoning, data analysis, and numerical algorithms, always with the goal of helping students connect these abstract concepts to professional and real-life scenarios. By encouraging students to question, analyze, and understand the underlying logic of mathematical principles, the course fosters critical thinking and problem-solving skills. By the end, you will not only know how to perform calculations but also understand the "why" behind them, enabling you to apply mathematical reasoning effectively in academic and professional contexts.

My lessons are designed to take you from simply following code to genuinely understanding how data science works. We can cover the complete data science process, including data cleaning, exploratory data analysis, feature engineering, visualisation, statistics, machine learning, model evaluation and communicating results. Depending on your goals, lessons can include: Python, pandas, NumPy and scikit-learn Data cleaning and exploratory analysis Regression and classification Decision trees, random forests and boosting Clustering and dimensionality reduction Cross-validation and model evaluation Feature engineering and model interpretation Neural networks and deep learning foundations Bayesian modelling and PyMC Portfolio and interview preparation Support understanding university modules and projects I use diagrams, analogies and practical demonstrations to make difficult ideas easier to understand. We will normally begin with an intuitive explanation, look at the underlying logic or mathematics, and then implement the concept in Python. Lessons are personalised around your level. Complete beginners receive a structured learning path, while experienced students can focus on advanced topics, project guidance, debugging or interview preparation. You will be encouraged to explain ideas back to me, interpret results and make your own modelling decisions. My goal is not only to help you produce working code, but to help you become an independent and confident data scientist.

This lesson builds a solid foundation in numbers and variables by helping students understand how numbers are represented, how variables are used to express unknown values, and how they relate to real-world situations. Through clear explanations, guided examples, and practice activities, students develop algebraic thinking, logical reasoning, and confidence needed for advanced mathematics.

I am a mathematics teacher with university teaching experience. Holding a Master's degree and a State Engineering diploma, I support pupils and students in their mathematics learning with a clear, progressive method adapted to their level. I propose private lessons for: Middle School High school University students (Bachelor's degree) I can help you in the following areas: Algebra Analysis Differential and integral calculus Differential equations Statistics Probabilities Exam preparation and resits My goal is to help you understand essential concepts, improve your study methods, and gain confidence. Each session is tailored to the student's needs, with clear explanations, progressive exercises, and detailed feedback. Courses can be taught in French or English, online or in person depending on availability.

Are you an engineering or mathematics (Bachelor's/Master's) student who finds numerical analysis too abstract or difficult to apply? This course provides a rigorous yet practical introduction to scientific computing, showing how numerical methods are used to solve real engineering and scientific problems. You'll first learn how numerical computations behave by studying floating-point arithmetic, rounding and truncation errors, conditioning, stability, and error propagation. These concepts are essential for understanding why numerical algorithms succeed—or fail. Next, you'll learn how to solve nonlinear equations using the bisection method, fixed-point iteration, and Newton's method, before moving on to linear systems through both direct methods (Gaussian elimination, LU decomposition, Cholesky factorization) and iterative methods (Jacobi, Gauss-Seidel, and relaxation methods). The second half of the course covers polynomial interpolation and approximation (Lagrange, Newton, Hermite, least-squares approximation, and splines), numerical differentiation and integration, and numerical methods for ordinary differential equations, including Euler's method, Runge-Kutta methods, and finite difference techniques. Every algorithm is implemented step by step in Matlab so that you not only understand the mathematics behind it but also learn how to apply it to practical engineering problems. Throughout the course, we compare the accuracy, convergence, stability, and computational efficiency of the different numerical methods. This course is ideal for students preparing for university exams, engineering projects, scientific computing courses, or anyone wishing to build solid foundations in numerical analysis. Prerequisites: A good understanding of single-variable calculus, basic linear algebra, and elementary programming concepts is recommended.