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Since September 2023
Instructor since September 2023
Python & Entrepreneurship: Tech and Power Skills for Young Minds
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From 166 C$ /h
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Is your young genius drawn to the world of coding and eager to explore the realms of entrepreneurship? 🌐💡

Join us on an exciting journey that combines the power of Python programming with the innovative spirit of entrepreneurship! 🐍🛠️ Our unique course is tailored for bright young minds in 5th-6th grade, designed to unlock their potential and set them on a path towards a future where they can shape the tech world with confidence.

🔹 Master Python Coding: Your child will delve into the world of Python, gaining essential coding skills that are the foundation of modern technology.

🔹 Ignite Entrepreneurial Thinking: We foster entrepreneurial thinking from the start, encouraging your child to dream big, innovate, and turn ideas into action.

🔹 Real-World Projects: Learning by doing is our mantra. Your child will work on exciting projects that apply their coding skills to real-world challenges.

🔹 Guidance from Experts: Our experienced mentors provide personalized guidance, helping your child navigate the world of coding and entrepreneurship.

🔹 Empower for the Future: Give your child an edge by equipping them with tech and power skills that set them up for success in the digital age.

Ignite their curiosity, fuel their creativity, and empower their future with the Python & Entrepreneurship: Tech and Power Skills for Young Minds course. 🌟 Let's inspire the next generation of tech leaders!

🔗 Ready to embark on this empowering journey? Comment below or message us for more details! Let's shape a brighter future together
Extra information
Cours en ligne et en groupe exclusivement
Location
location type icon
Online from Spain
About Me
🚀 Welcome to Code 🚀

Code is where young minds discover the thrill of coding, creativity, and innovation! 🐍🎮 Our camp is the ultimate afterschool escape for curious, tech-savvy kids and teens looking to dive into the world of programming.
Education
Civil Engineering in France
Object Oriented Programming - Full stack developer
Innovation and Entrepreneurship
Special Education Needs Teacher
STEM and Technology in the classroom
Experience / Qualifications
With over a decade of teaching experience, I've served as:

A private tutor
A STEM and new technologies teacher
An after-school facilitator of 21st-century skills
A curriculum developer for online classes for primary students in the USA (and aligned curricula with IB and Spanish educational standards for secondary students)
I've trained animators, future teachers, and fellow educators in integrating digital tools, innovative pedagogies, and online teaching during lockdowns. I am an expert in STEM education, ICT, and new tech.

I've held roles as:

A coordinator of the technology department in an international school
A leader of a leadership and creativity summer camp
An organizer of field trips for international schools in France

I seamlessly integrate pedagogy and technology both inside and outside the classroom, adapting to diverse student populations. My focus is on creating dynamic and interactive training environments where educators actively exchange experiences and enhance their teaching toolbox.
Age
Children (7-12 years old)
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Duration
60 minutes
The class is taught in
French
English
Spanish
Availability of a typical week
(GMT -05:00)
New York
at teacher icon
Online via webcam
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
Whether you're a student seeking to boost your grades or a lifelong learner aiming to acquire new skills, this course is your roadmap to success.

📖 **Science-Backed Study Strategies** 🧠

This class delves into the art and science of learning. Discover evidence-based study techniques that have been proven to enhance memory retention, comprehension, and problem-solving. From note-taking methods to active recall strategies, you'll build a toolkit of study skills that work.

⏰ **Time Management and Productivity** ⌛

Effective studying is not just about what you learn; it's about how you manage your time. Learn valuable time management techniques to maximize your productivity. Say goodbye to procrastination and hello to focused, efficient study sessions.

🗂️ **Organizational Skills and Resource Utilization** 📁

Organizing your study materials and utilizing available resources are crucial components of successful learning. Our class teaches you how to create effective study schedules, leverage digital tools, and access valuable learning resources.

🤔 **Critical Thinking and Problem-Solving** 🧩

Studying isn't just about memorizing facts; it's about developing critical thinking and problem-solving skills. Through practical exercises and real-world case studies, you'll sharpen your ability to analyze information, make informed decisions, and excel academically.

👩‍🏫 **Experienced Instructors and a Supportive Learning Community** 👨‍🏫

Our dedicated instructors are passionate about education and committed to your success. Join a supportive learning community where students share insights, provide motivation, and collaborate on study techniques. We believe in your potential for academic greatness.

Whether you're preparing for exams, pursuing a new skill, or simply eager to enhance your learning abilities, "Mastering Study Techniques: Your Path to Academic Success" equips you with the tools and strategies you need to excel in your educational journey. 📚🌟🧠
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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
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2 — PROGRAMMING, ALGORITHMS, AND COMPUTER SCIENCE FOUNDATIONS
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• 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
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• Loss functions, gradients, distance measures, regularization, likelihood, and model complexity
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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
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6- UNSUPERVISED LEARNING
• Clustering using k-means, hierarchical clustering, and density-based methods
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• Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization

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11- TOOLS AND LIBRARIES
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• 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
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-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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This program is ideal for:
✅ Complete beginners – If you’ve never written a line of code, no worries! The lessons start from scratch.
✅ Students – To prepare for programming courses, computer science exams or competitions.
✅ Self-taught – If you want to add Python to your skillset, this course offers you structured support.
✅ Professionals and people in retraining – Python is a sought-after skill in data science, automation and web development.
✅ Tech Enthusiasts – Curious to learn code? This course makes learning Python both fun and useful.

🚀 Ready to Embark on Your Python Adventure?
Join the "🐍 Personalized Private Lessons – 💻 Learn to Code and Program in Python!"
Gain the skills and confidence to write clean, efficient, and powerful code.

Sign up today and take your first steps into the exciting world of Python programming! 🔥
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I offer Microsoft Word, Microsoft Excel, Microsoft PowerPoint, Microsoft Office sessions

Word is a word processing software developed by Microsoft. It is used to create documents, letters, reports, presentations...

Excel is a spreadsheet software from the Microsoft Office suite developed and distributed by the publisher Microsoft. It is used to create spreadsheets, tables, graphs, etc.

Powerpoint is a presentation software developed by Microsoft. It is part of the Microsoft Office suite. It is used to create presentations, slide shows...

I am waiting for your message.
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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.
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Understanding algorithms is essential for anyone pursuing computer science or programming. In this course, I provide a structured approach to learning algorithms—from fundamental concepts like sorting and searching to advanced techniques such as dynamic programming and graph theory. Whether you're preparing for exams, coding interviews, or aiming to improve your problem-solving skills, this course is designed to build strong algorithmic thinking. Through practical examples and step-by-step explanations, I help students master the logic and techniques needed to write efficient and elegant code.
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I teach coding to beginners and intermediate students.
Lessons focus on logic, basic programming, and practical exercises.
Classes are adapted to the student’s pace.
Students can choose between website or mobile app development.Hands on dev
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These courses are part of a structured and progressive training in Object-Oriented Programming (OOP) with JavaScript, designed for beginner or intermediate developers who want to understand in depth how the language works, write clearer, more maintainable code and prepare themselves calmly for modern frameworks like React ⚛️.

Object-Oriented Programming is often perceived as complex or abstract.

My goal is simple: to make it logical, concrete, and immediately applicable.

🎯 Training Objectives

Upon completion of this training, you will be able to:

Understanding what Object-Oriented Programming really is (and when to use it)
Create and manipulate objects in JavaScript in a clean and efficient way
Use ES6 classes, constructors, and methods with confidence
Mastering this, the prototype, and the instantiation logic
Apply encapsulation, inheritance, and polymorphism without confusion
Avoiding common mistakes made by OOP beginners
Structure your JavaScript code like a professional developer

📖 Training Plan – Object-Oriented Programming in JavaScript
1. Introduction to Object-Oriented Programming 🧠
Understanding the concept, objectives and benefits of OOP.
2. Procedural Programming vs. OOP
Why unstructured code quickly becomes unmanageable.
3. Objects in JavaScript
Properties, methods and representation of the real world.
4. The keyword this
Understanding the execution context (often poorly understood).
5. Limitations of simple objects
Why duplicating code is a bad idea.
6. Constructive functions
Create multiple objects from the same model.
7. The keyword new
What it's actually doing under the hood.
8. The prototype
Sharing methods and memory optimization.
9. ES6 Classes
Modern syntax and best practices.
10. The builder
Proper initialization of objects.
11. Data Encapsulation
Protect the internal state of objects.
12. Inheritance between classes
Reusing code intelligently.
13. The keyword super
Communication between parent and child in the classroom.
14. Polymorphism
The same behavior, several forms.
15. Composition vs. Inheritance
Choosing the right architecture.
16. Best practices in OOP
Write readable, scalable, and maintainable code.
17. Common mistakes made by beginners
Pitfalls to absolutely avoid.
18. Guided practical exercise
Creation of a concrete class (product, user, etc.).
19. Assessment Quiz (Multiple Choice Questions)
To validate the actual understanding of the concepts.

🛠️ Teaching method: Understand before writing

This training program is based on a progressive and pragmatic approach:
Clear and illustrated explanations
Concrete examples from real projects
Simple but effective exercises
Constant questioning to avoid rote learning
Adaptation to the learner's level and pace
Here, we don't "recite OOP" — we understand it.

🚀 Learner's result

At the end of the training, you will not only know how to write a JavaScript class.
You will know:

1- Why does it exist?
2- When to use it
3- and when not to use it

You will leave with:
a solid understanding of OOP
a cleaner and more professional code
an ideal foundation for learning React, Node.js or any other modern framework
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Primary School Teacher. I offer personalized guidance in developing lesson plans and learning activities adapted to current educational regulations. During our sessions, you will learn to structure and write a complete lesson plan, including specific competencies, assessment criteria, core knowledge, methodology, addressing diversity, evaluation, and learning situations. My goal is to help you understand each section so you can independently develop a coherent, rigorous, and high-quality lesson plan.

You will learn to design practical, creative proposals, developing the necessary skills to apply them in the classroom or in competitive examination processes.
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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.
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I'm a working MEP engineer, currently building Python automation for Revit workflows daily - plan checks, model coordination, and repetitive drafting tasks. I teach other engineers, architects, and BIM professionals how to do the same, using pyRevit and real project workflows, not toy examples.

Topics include:
pyRevit fundamentals and setup
Automating repetitive Revit tasks (model checks, plan generation, data extraction)
Writing custom scripts for your firm's specific workflows
Applying Python automation to real MEP/BIM projects
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You have a kid that's interested in programming?? Set up a class for him with me. Aya a software engineer turned teacher. I was a software engineer for 3 years before I joined the teaching industry and I currently teach computer science in Chichester College in United Kingdom. I want to advocate for computer science and help familiarise kids at a young age with programming in Python.

Send me a message let's chat about what you want your kid to learn and what I can offer! :)
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