Applied Data Science Lab: From Raw Data to Business Impact

From 22 £ /h

Overview

Transitioning from learning data science theory to solving actual business problems is the hardest step for any aspiring data professional. [Insert Chosen Course Title] is an intensive, mentor-led program designed to simulate a real-world data team environment. Instead of working through synthetic, pre-cleaned textbook datasets, you will take on messy, complex industry scenarios and turn them into end-to-end data products.

What You’ll Experience

End-to-End Execution: Walk through the full data lifecycle—from problem scoping and data extraction to exploratory analysis, modeling, and executive stakeholder presentation.

Industry-Standard Workflows: Work with messy real-world datasets, practice Git-based version control, write production-ready code, and structure reports that business leaders actually care about.

1-on-1 & Group Mentorship: Receive continuous code reviews, architectural feedback, and project guidance mirroring the experience of working under a Senior Data Scientist or Analytics Lead.

Portfolio-Ready Deliverables: Graduate with 2–3 complete, polished projects that demonstrate actual business value to hiring managers—not just another churn prediction copy-pasted from Kaggle.

Who This Is For
Aspiring Data Analysts, Data Scientists, and recent graduates who know Python, but want the practical experience, confidence, and portfolio needed to land high-impact roles in the industry.

Extra information

Prerequisites & Tech Requirements
To fully participate in hands-on sessions, you will need a personal laptop with internet access. Exercises and project work can be completed either by installing Python locally or directly in your browser using Google Colab—no high-performance hardware required.

Location

Online from India

About Me

26 Years of Industry Experience: A seasoned tech professional and leader from India, with over two decades of experience building and delivering data solutions within a world-leading financial institution.

Friendly & Approachable Mentor: Passionate about demystifying complex concepts through a supportive, encouraging teaching style that builds student confidence from day one.

Avid Pythonista: Deeply passionate about Python programming, clean code, and leveraging open-source tools to solve complex, real-world data problems.

Dedicated to Career Growth: Enthusiastic about guiding students and young professionals as they navigate the transition from academic learning to corporate environments.

Mock Interview Specialist: Conducts structured, realistic mock technical and analytical interviews to help students sharpen their problem-solving skills and land job placements.

Education

1996 - 2000
Bachelor of Engineering, Instrumentation and Control
Bharath Institute of Science and Technology
University of Madras, Chennai, India
I am a University Rank Holder

Experience / Qualifications

26 Years at BNY

Data Scientist | Senior Vice President (2017 – Jun 2026): Led AI/ML, simulation modeling, and custom ranking engine initiatives.

Application Architect | Vice President (2013 – 2017): Architected enterprise search engines and site-wide frameworks.

Java Developer | Assistant Vice President (2000 – 2013): Led core software design and workflow development.

Age

Teenagers (13-17 years old)

Adults (18-64 years old)

Student level

Beginner

Intermediate

Advanced

Duration

60 minutes

The class is taught in

English

Skills

Python

Computer modeling

Numerical analysis

Availability of a typical week

(GMT -05:00)

New York

Online via webcam

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

Matías

4.88

(85)

Python is one of the best, if not the best, language to start learning programming. It is also one of the most widely used languages today, especially in cutting-edge areas such as machine learning.

This popularity means that Python is constantly evolving. It offers a wide range of tools and libraries, which are free and very varied.

As an aeronautical engineer, I like to share my knowledge and derive satisfaction from it by teaching and motivating others.

I'm used to working with people of different ages. I believe in the importance of segmenting learning, visualizing progress, setting concrete goals and practicing regularly.

Beyond these general principles, there is no magic rule or method. Some approaches work with some students but not with others. Adaptation to individual needs is therefore the main objective of private lessons. So I will do my best to find what motivates and helps my student.

Adam

4.88

(85)

Learn to code with method and logic
Whether it's to succeed in the NSI (Digital Sciences and Technology) specialization in high school, design personal projects, or prepare for higher scientific studies, mastering code relies on solid algorithmic thinking. I help students understand the structure of programming languages and the logic of data.

Subject areas and languages taught:

Algorithms & Logic: Designing data structures and solving problems.

Programming Languages: Python, C/C++, C# and Java.

Data Management: Analysis and SQL queries / databases.

Basic Web Development: HTML & CSS for creating structured pages.

The goal is to take the student from simply writing code to true autonomy in development.

Amin

4.88

(85)

Are you a university student, engineer, or professional who needs to actually use data — not just learn theory about it?
This course is built around real problems and real code. We skip the textbook formulas and go straight to applying statistics and data science the way professionals do: with Python (pandas, NumPy, scikit-learn, matplotlib) and R (RStudio).
What we cover, adapted to your level and goals:
- Descriptive and inferential statistics (the ones that actually matter)
- Data cleaning, exploration, and visualization
- Regression, classification, and intro to machine learning
- Time series and forecasting basics
- R for statistical analysis and academic research

Who this is for:
- University students in statistics, economics, engineering, or biology
- Professionals wanting to move into data analysis or data science
- Researchers who need to process and present data properly

I use Python and R professionally as a working engineer — everything I teach comes from real application, not just academic exercises.

Mohamed

4.88

(85)

As a highly qualified maths teacher, a graduate of the college of teachers and with 11 years of teaching experience in public high schools, I am happy to offer tutoring lessons in mathematics at home for students from level T and Common Core Sciences, TC Technological, 1st Baccalaureate Experimental Sciences and final of all the sectors (SVT-PC-SC.Math-L), as well as for the classes of 2nd and 1st general, Terminale specialty of the French system, as well than the 5th, 4th and 3rd levels of college.

My primary objective is to help students improve their level, deepen their knowledge, assimilate their lessons, fill their gaps and improve their skills in the discipline of mathematics. In addition, I am perfectly able to support them in the preparation of their exams and competitions for access to the Grandes Ecoles, and to provide them with homework help so that they can succeed in this subject.

With my advanced math skills and knowledge, I am confident that I can provide my students with effective tools and techniques to help them progress. My goal is to give them confidence and help them develop a passion for mathematics, a subject that can seem daunting at first, but can be exciting and rewarding if taught in an interesting and fun way.

By choosing my tutoring courses in mathematics, students can expect to receive individual attention and personalized help to overcome their difficulties and achieve their goals. My teaching approach is interactive and student-centered, which allows for a deeper understanding of mathematical concepts and a more practical application of acquired knowledge.

In summary, I am confident in my skills as a math teacher to help students of all levels progress and succeed in this demanding subject. I am convinced that my dynamic and stimulating teaching methods will help my students achieve their math goals and build a confidence that will follow them throughout their lives.

Are you looking for help with statistics or data analysis?
To prepare for an exam, accomplish a coursework, a bachelor's, master's or doctoral thesis?

I have a PhD title in physics and have been teaching statistics for 10 years. I have already helped more than 150 students succeed.

I make the lecture material more understandable using concrete examples. I correct papers, explain the best methods to solve tasks or put up my own exercices to achieve the necessary level.

I help in interpreting the output of common statistical programs (R/RStudio, Jamovi, Jasp) or in carrying out entire analyses.

I am an affable character and am completely committed to one goal: the success of the student.

Salah

4.88

(85)

Master Python with Personalized Courses

Discover the art of programming with Python courses tailor-made to meet your specific needs. Whether you are a beginner, intermediate or professional, my lessons are suitable for all levels.

Why Choose My Courses?

Personalized Teaching Approach: Each course is tailored to your skill level and individual goals.

Practical Experience: Learn by doing with real-world projects that build your understanding and skills.

Ongoing Support: Get unlimited email support for any questions you have between sessions.

As a Python expert, I have a passion for teaching and sharing my knowledge. My goal is to guide you effectively in your learning journey.

Book Your First Lesson:

Start your journey to Python mastery now by booking your first lesson. Whether you aspire to enter the development field or hone your existing skills, these courses are designed for you.

Roger

4.88

(85)

Do you need a boost in mathematics to better understand lessons, pass your exams or prepare for a competition? Do you want to learn to program in C, C++, Python or Java to develop skills sought after in the digital world?
This comprehensive and personalized academic support program is designed to meet your needs and help you succeed!

💡 Why Choose this Program?
This course offers tailor-made support, adapted to your level and your objectives:

🎯 Progress in mathematics by strengthening your foundations and mastering advanced concepts.
💡 Understand theoretical concepts in depth to better apply them in exercises and problems.
💻 Learn to program in C, C++, Python and Java with clear explanations and practical exercises.
🚀 Develop essential skills in algorithms and computer problem solving.
🎓 Effectively prepare for your exams (Bac, Licence, competitive exams) thanks to targeted revisions and practice subjects.
With a caring educational approach, this course helps you gain confidence and achieve your academic goals.

📘 Mathematics – From Secondary to Bachelor
Mathematics is the key to academic success in many scientific and technical fields.
This module covers:

Secondary Level (Middle and High School):

Arithmetic, fractions, percentages, proportionality.
Algebra: Equations, inequalities, functions (linear, quadratic, exponential, logarithmic).
Geometry: Theorems, trigonometry, analytical geometry.
Statistics and probability: Analyze data, calculate probabilities.
Preparation for exams: Brevet, Bac, entrance exams for grandes écoles.
University Level (Bachelor):

Differential and integral calculus: Derivatives, integrals, sequences and series.
Linear Algebra: Matrices, vectors, systems of linear equations.
Advanced Probability and Statistics: Random variables, probability laws, estimation and hypothesis testing.
Numerical analysis: Methods for approximate resolution of equations and systems of equations.
Discrete Mathematics: Graphs, Boolean logic, combinatorics.
This module offers progressive exercises, clear explanations and detailed corrections to understand in depth and train effectively.

💻 Programming – C, C++, Python and Java
Mastering programming is a major asset for success in the digital and technological field.
This module covers the fundamentals of programming to enable you to:

Understand algorithmic logic and computer problem solving.
Master the syntax of the C, C++, Python and Java languages.
Writing your first programs: Variables, conditional structures, loops, functions.
Work on practical projects: Calculator, data management, simple games, sorting and searching algorithms.
Develop advanced skills:
Object-oriented programming (C++, Java): Classes, inheritance, polymorphism.
Memory management (C, C++): Dynamic allocation, pointers.
File manipulation: Reading and writing data.
Data structures: Lists, stacks, queues, binary trees.
Code optimization for faster and more efficient programs.
This module offers concrete examples, practical exercises and motivating projects to help you learn while having fun while developing skills useful in the professional world.

🎯 Interactive and Motivating Teaching
Dynamic online courses: Learn from home in an interactive format with audio and screen sharing.
Tailor-made method: The courses are designed according to your level and your objectives for learning at your own pace.
Practical exercises and concrete projects: To apply theoretical concepts and develop your skills.
Personalized monitoring: Regular support to monitor your progress and adapt the program to your needs.
Encouragement and motivation: A positive approach to building your confidence in your abilities.
🔔 For Who?
This program is aimed at:

High school students wishing to strengthen their foundations in mathematics or learn to program.
University students in science or computer science looking to deepen their knowledge of math and programming.
Candidates for exams and competitions preparing for the Baccalaureate, a License, or entrance exams to the grandes écoles.
Programming enthusiasts wanting to learn the fundamentals of C, C++, Python or Java.
Adults in professional retraining wishing to acquire programming skills.
🚀 Ready to Succeed?
Join the "🎓 Academic Support – 📘 Maths from Secondary to Bachelor & 💻 Programming in C, C++, Python and Java!" and benefit from personalized support to achieve your goals.
Whether you want to improve your grades, pass your exams or develop programming skills, this program will give you knowledge, confidence and motivation.

Register today and take the first step towards success!

Baia

4.88

(85)

I teach Python specifically for finance and data applications - the kind used in economics, business analytics, and quantitative programs. This isn't a general "learn to code" course; it's built around real financial data, benchmarking, and the workflows you'll actually use in coursework or early career work.

Topics include:
Python fundamentals through a finance lens (data structures, functions, control flow).
Working with financial data and datasets.
Performance benchmarking and writing efficient code.
Applying concepts from Hilpisch's Python for Finance.

Hayat

4.88

(85)

This course is designed for anyone interested in learning data science using Python. It provides a hands-on introduction to fundamental data analysis tools such as NumPy, pandas, matplotlib, and seaborn. You'll learn how to manipulate datasets, create visualizations, and lay the foundations for statistical analysis and machine learning.

The course combines theory and practical exercises for effective, practical progress. No prior programming experience is necessary: we'll start with the basics to build solid, usable skills quickly.

Kevin

4.88

(85)

► EXPERT STATISTICS, DATA ANALYTICS, MACHINE LEARNING & AI TUTOR FROM SWITZERLAND
► UNIVERSITY, FOUNDATION, IB, A-LEVEL & INTERNATIONAL SCHOOL SUPPORT

I completed my Master’s degree in Business Information Systems at a Swiss University of Applied Sciences, where my academic background strongly combined mathematics, statistics, data analysis, analytical thinking and problem-solving. This technical and data-oriented foundation shaped the way I teach today: clearly, logically and with a strong focus on real understanding.

For many years, I have successfully supported students in Statistics, Data Analytics, Machine Learning and AI. My main focus is especially on Statistics — from basic descriptive statistics to advanced statistical methods, hypothesis testing, regression, probability distributions and interpretation of results.

I mainly use R for statistical analysis, data handling, visualisation and practical exercises. My goal is not only to help students calculate results, but to make sure they understand what the results mean and how to explain them correctly.

► STATISTICS, DATA ANALYTICS & AI SUPPORT

► STATISTICS & PROBABILITY
I help students understand descriptive statistics, probability, random variables, distributions, sampling, confidence intervals, hypothesis testing, p-values, correlation, regression and statistical interpretation. My lessons focus on explaining the logic behind each method, not just applying formulas.

► APPLIED STATISTICS WITH R
I support students in using R for statistical analysis, data cleaning, visualisation, hypothesis testing, regression models and interpretation of outputs. Students learn how to connect theory, calculation, code and real meaning step by step.

► QUANTITATIVE METHODS & RESEARCH STATISTICS
I help students with statistical methods used in business, economics, psychology, social sciences, science and university research. This includes choosing the correct test, understanding assumptions, interpreting results and presenting findings clearly.

► DATA ANALYTICS & DATA SCIENCE
I support students with data preparation, exploratory data analysis, visualisation, dashboards, summary statistics and practical interpretation. The focus is always on understanding the data and drawing meaningful conclusions.

► MACHINE LEARNING & AI FOUNDATIONS
For students working with modern data topics, I also provide support in the foundations of Machine Learning and AI, including regression, classification, clustering, model evaluation and practical applications. These topics are explained from a statistical point of view, so students understand the logic behind the models.

► UNIVERSITY, FOUNDATION & INTERNATIONAL COURSES
I support students in Statistics, Data Analytics, Business Analytics, Quantitative Methods, Econometrics, Research Methods and technical modules. I help with exam preparation, assignments, projects and practical data analysis tasks.

► HOW I TEACH

► I FOCUS ON REAL STATISTICAL UNDERSTANDING.
Statistics becomes much easier when students understand why a method is used, what the result means and how to interpret it correctly.

► I EXPLAIN FORMULAS STEP BY STEP.
Difficult formulas, tests and models are broken down into simple, logical parts so students can follow the reasoning clearly.

► I CONNECT THEORY WITH R PRACTICE.
Students learn not only the statistical theory, but also how to apply it in R, read the output and explain the result in proper academic language.

► I HELP STUDENTS CHOOSE THE RIGHT METHOD.
Many students struggle with deciding whether to use a t-test, chi-square test, ANOVA, regression or another method. I teach students how to recognise the correct approach from the question or dataset.

► I TRAIN INTERPRETATION AND EXAM TECHNIQUE.
Students learn how to structure statistical answers, write clear conclusions, explain p-values, interpret confidence intervals and present results professionally.

► I ADAPT EVERY LESSON TO THE STUDENT.
Some students need help with theory, others with R coding, assignments, research projects or exam preparation. I adjust every lesson to the student’s exact course, level and goals.

► YEARS OF EXPERIENCE WITH STATISTICS, DATA & UNIVERSITY STUDENTS

Over the years, I have successfully supported students from demanding academic programmes, helping them strengthen their statistical understanding, improve their analytical thinking and achieve excellent progress in Statistics, Data Analytics, Machine Learning and AI.

► ONLINE LESSONS

► Interactive whiteboard
► Clear digital notes
► Step-by-step statistical explanations
► R support for data analysis
► Exam preparation
► Assignment and project guidance
► Practical examples with real datasets
► Focused one-to-one support from Switzerland

► MY GOAL

My goal is not only to help students pass exams or complete assignments, but to help them truly understand Statistics. With the right guidance, statistical methods become logical, practical and much easier to apply.

► SUBJECTS: Statistics, Probability, Data Analytics, Data Science, Machine Learning, AI, Quantitative Methods, Research Methods, Econometrics
► MAIN TOOL: R
► LEVELS: International School, IB, A-Level, Foundation Courses, University Modules, Professional Training
► FORMAT: Online tutoring from Switzerland
► FOCUS: Statistical understanding, R practice, interpretation, exam preparation, assignments, projects and long-term analytical confidence.

Ali

4.88

(85)

I teach Python, C and C++ one to one, online or in person around Birmingham.

Most of my students fall into one of three groups. Some are at GCSE or A-Level and need to get comfortable with a language before an exam or a coursework deadline. Some are at university, usually on an engineering or computing degree, and have hit something specific that isn't clicking: pointers, memory, recursion, object orientation, or a project that won't compile. And some are adults starting from nothing, often because work has started asking them to automate things.

Lessons are built around code you can run. I'll ask what you're working on and where you got stuck, then we write something small together, break it on purpose, and work out what the error message is actually telling you. Reading error messages properly is half of programming and almost nobody teaches it.

Areas I cover regularly:

Python from the basics through functions, data structures, file handling, object orientation and libraries like NumPy and Pandas
C and C++, including the parts that cause most of the trouble: pointers, memory management, structs, classes and compilation
GCSE and A-Level Computer Science across all exam boards, including pseudocode, trace tables and written paper technique
A-Level NEA projects and university coursework, plus debugging sessions and code review
Embedded C for Arduino, ESP32 and microcontroller projects, which is the work I do professionally

After each lesson I send written notes covering what we did, worked through step by step, so you have something to revise from later rather than trying to remember what was on screen.

First session is free and lasts 30 minutes. We use it to work out what you need and whether I'm the right person for it. If I'm not, I'll say so and point you somewhere better.

Message me with what you're studying and what's giving you trouble, and I'll tell you honestly how I'd approach it.

Ammar

4.88

(85)

A- TOPICS YOU CAN EXPLORE AND MASTER:
1- PYTHON FOUNDATIONS
• Variables, data types, operators, conditional structures, loops, functions, modules, files, exceptions, and object-oriented programming
• Lists, tuples, dictionaries, sets, comprehensions, debugging, and writing clear, reusable, well-structured code
• Jupyter Notebook, Anaconda, Visual Studio Code, virtual environments, and package management

2 — PROGRAMMING, ALGORITHMS, AND COMPUTER SCIENCE FOUNDATIONS
• Algorithmic thinking, problem decomposition, pseudocode, flowcharts, procedural programming, object-oriented programming, recursion, and modular program design
• Fundamental data structures including arrays, lists, stacks, queues, dictionaries/hash tables, sets, trees, graphs, and their appropriate use
• Searching, sorting, traversal, algorithm efficiency, computational complexity, Big-O notation, debugging, testing, code organization, and problem-solving strategies
• Practical programming exercises ranging from beginner problems to university-level algorithmic and computational challenges

3- DATA PREPARATION AND EXPLORATION
• NumPy and pandas for importing, cleaning, transforming, filtering, grouping, reshaping, and merging data
• Missing values, duplicates, outliers, inconsistent formats, data leakage, and data-quality validation
• Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation

4- MATHEMATICAL FOUNDATIONS
• Linear algebra, vectors, matrices, derivatives, optimization, probability, and statistics
• Loss functions, gradients, distance measures, regularization, likelihood, and model complexity
• Mathematical concepts are explained according to the learner’s level and the requirements of the selected algorithms

5- SUPERVISED MACHINE LEARNING
• Linear and polynomial regression, logistic regression, and regularized models
• k-nearest neighbours, decision trees, random forests, gradient boosting, support vector machines, and Naive Bayes classifiers
• Classification, regression, model assumptions, decision boundaries, feature importance, and interpretation of results

6- UNSUPERVISED LEARNING
• Clustering using k-means, hierarchical clustering, and density-based methods
• Principal component analysis, dimensionality reduction, anomaly detection, and pattern or structure discovery
• Method selection, evaluation of data structure, and interpretation of results without predefined labels

7- MODEL EVALUATION AND IMPROVEMENT
• Training, validation, and test sets; cross-validation; hyperparameter optimization
• Accuracy, precision, recall, specificity, F1 score, ROC–AUC, confusion matrices, MAE, MSE, RMSE, and R2
• Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization

8- DEEP LEARNING
• Neural-network foundations, activation functions, forward propagation, backpropagation, and gradient descent
• Multilayer perceptrons, convolutional neural networks, recurrent neural networks, and Transformer foundations
• TensorFlow, Keras, or PyTorch depending on the learner’s project and working environment

9- ARTIFICIAL INTELLIGENCE APPLICATIONS
• Natural language processing, text classification, embeddings, sentiment analysis, and foundations of language models
• Computer vision, image classification, fundamental principles of object detection, and image preprocessing
• Recommendation systems, forecasting, anomaly detection, intelligent automation, and decision-support applications

10- GENERATIVE AI AND LARGE LANGUAGE MODELS
• Transformer architecture, tokens, embeddings, attention mechanisms, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation
• Use of artificial-intelligence APIs, vector databases, document-retrieval systems, and structured AI-enabled workflows when relevant
• Reliability, hallucinations, bias, privacy, responsible use, and appropriate human validation

11- TOOLS AND LIBRARIES
• Python, NumPy, pandas, Matplotlib, Seaborn, scikit-learn, SciPy, Statsmodels, TensorFlow, Keras, and PyTorch
• Jupyter Notebook, Anaconda, Visual Studio Code, Git, GitHub, SQL, Excel, and Power BI when useful to the project
• Additional libraries may be introduced depending on the selected specialization and dataset

12- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION
• Complete projects covering data preparation, model development, evaluation, interpretation, and presentation of results
• University assignments, dissertations, theses, research projects, portfolio projects, technical interviews, and professional applications
• Code review, debugging, documentation, reproducibility, model comparison, and communication of results

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B- PERSONALIZED TUTORING: LEARNING HOW TO REASON
Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected.

My lessons help you move beyond simply copying code or using models as “black boxes.” You will learn how to define the problem correctly, prepare the data, select an appropriate algorithm, understand how it works, train and evaluate the model, diagnose errors, improve performance, and interpret results rigorously and responsibly.

Each lesson is personalized according to your current level, mathematical background, programming experience, dataset, university work, research project, interview preparation, or professional objective. We begin by identifying your existing knowledge, software environment, expected outcomes, and main conceptual or technical difficulties. We then establish a structured learning plan.

The first lesson combines a discussion of your background, objectives, and tutoring needs; an initial assessment of your current knowledge; personalized planning and organization of future sessions; and a short trial lesson to determine the most effective learning approach.

A typical session may include conceptual explanation, development of mathematical intuition, live coding, guided implementation, model evaluation, technical problem solving, and a concise summary of the next steps.

You may work with your own dataset, university assignment, research project, or professional problem, provided that confidential information is handled appropriately. I can also provide structured examples and datasets suited to your level.

My goal is not simply to help you run an algorithm. It is to help you understand why it is appropriate, how it learns from data, how to evaluate it correctly, why it may fail, and how to build a reliable, interpretable, and scientifically rigorous solution.

Azhar

4.88

(85)

Are you interested in learning Python but don't know where to start? This course is designed to help you understand the fundamentals of Python programming in a simple, practical, and enjoyable way.

Python is one of the most popular programming languages in the world. It is widely used in software development, automation, data analysis, artificial intelligence, and web development.

Whether you are a complete beginner, a student, or someone looking to develop new technical skills, these lessons will help you build a strong foundation in programming.

You will learn
1. Introduction to Python
2. Python Fundamentals
3. Conditional Statements and Loops
4. Working with Data
5. Functions and Practical Programming
6. Mini Projects

Alexandre

4.88

(85)

Whether you're in a preparatory class for engineering schools (scientific track or business school), a bachelor's degree, an engineering school, or a master's program, higher-level mathematics moves quickly, and a poorly understood concept can hold you back from an entire chapter. This course is designed to help you truly understand, and then master, the material.

Graduate engineer from CentraleSupélec, eligible for the agrégation option probability-statistics (2026), I am preparing a doctorate in AI applied to automation and I have taught more than 400 hours at the university.

Each session is tailored to your needs: questions from the week, practice exams or midterms to prepare for, or a particularly challenging exercise. I begin with intuitive approaches (diagrams, concrete examples, sometimes a Python simulation), then we move on to more rigorous methods: definitions, key proofs, and the writing style expected in competitive exams. You then work through progressively challenging exercises and leave with a summary of the methods.

Areas of expertise: analysis, linear algebra, probability, statistics, financial mathematics and mathematical foundations of machine learning.