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Since June 2026
Instructor since June 2026
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Become a Professional Full-Stack Developer: Node.js, TypeScript, Laravel & DevOps
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From 12 € /h
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Do you want to learn to program, develop modern web applications, or prepare for a career in software development?

I am a software engineer with over 5 years of professional experience in the design, development, and deployment of applications used in real-world environments. I mentor students, junior developers, and professionals looking to acquire practical skills sought after in the job market.

Unlike purely theoretical training, my courses are oriented towards practical application, methodology and real-world projects.

We can work together on:

• Modern JavaScript and TypeScript
• Node.js and Backend Development
• Professional Laravel and PHP
• REST APIs and modern architectures
• SQL and NoSQL databases
• Docker and containerization
• DevOps, CI/CD and automation
• Git and best collaboration practices
• Software architecture and clean code
• Preparation for technical interviews
• Support for academic or professional projects
• Creation of a valuable technical portfolio

My method involves adapting each lesson to the student's objectives. Whether you are a complete beginner, a computer science student, or a developer wishing to progress to a professional level, we will build a personalized learning plan.

My goal is not just to teach you how to write code, but to pass on to you the methods, best practices, and working logic used daily by professional software engineers.

At the end of your apprenticeship, you will be able to design, develop, test and deploy your own applications with confidence and autonomy.
Extra information
The courses are fully customized according to your level and objectives.

You can bring:
• Your personal exercises or projects
• Your specific technical difficulties
• Your university projects
• Your professional or career change goals

A computer with an internet connection is recommended for practical lessons.
Location
location type icon
Online from Tunisia
About Me
Hello and welcome,

I am a software engineer with over 5 years of experience in developing modern web applications and solutions used in professional environments.

Throughout my career, I have worked on projects involving Node.js, TypeScript, Laravel, modern backend architectures, databases, and DevOps practices such as Docker, CI/CD, and deployment automation.

What I find most exciting about teaching is conveying much more than just technical knowledge. My goal is to help each student develop a genuine programming mindset, understand best practices in the field, and become more independent.

I adapt to each profile: beginners wishing to discover development, computer science students preparing for their exams or projects, developers wanting to deepen their technical skills, or professionals retraining for digital professions.

My method is based on three principles:

• Understand before you memorize
• Practice through concrete projects
• Acquire the methods used by professional software engineers

During our courses, we will work on real-life cases, exercises tailored to your level, and progressive projects that will allow you to acquire directly applicable skills.

I am convinced that anyone can learn to program with the right approach, appropriate support and a clear methodology.

Looking forward to supporting you in your progress and the achievement of your goals.
Education
Computer Engineer

• Engineering Degree in Computer Science – TEK-UP
• Specialization in Software Development and Computer Systems
• 2021

Continuing education and professional development in the following areas:
• Backend and Full-Stack Development
• Software Architecture
• DevOps and Cloud Computing
• Databases and distributed systems
• Agile methodologies and best development practices
Experience / Qualifications
• More than 5 years of professional experience in software engineering.

• Development and maintenance of high-traffic web applications using Node.js, TypeScript and Laravel.

• Design and development of secure and efficient REST APIs.

• Setting up CI/CD pipelines, automating deployments and Docker environments.

• Experience in scalable software architecture, clean code and good development practices.

• Mentoring junior developers and sharing knowledge within technical teams.

• Expertise in analyzing complex problems, debugging, and optimizing performance.

Key skills:

✓ Node.js
✓ TypeScript
✓ JavaScript
✓ Laravel
✓ PHP
✓ SQL & Databases
✓ Git & GitHub
✓ Docker
✓ DevOps & CI/CD
✓ REST APIs
✓ Software Architecture
✓ Clean Code
✓ Full-Stack Development
Age
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
90 minutes
The class is taught in
French
English
Arabic
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
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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
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• 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

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• 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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- Experience in preparing students to access world-class schools and universities, including Cambridge University, Oxford, Ivy League and other top institutions in the UK and US.
- University levels (undergraduate and postgraduate).
- High school studies and diploma programs.
- Assistance with specific projects at a professional level, including job interview preparation.
- Extensive experience working with children.

Every lesson is meticulously planned in advance to ensure that it aligns with your goals and targets areas for improvement.
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✓ What LLMs are (Large Language Models): in language they understand, not tech jargon
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For curious kids asking "How does ChatGPT actually know things?"
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Python is a powerful and versatile programming language with countless possibilities. You can use it for data analysis, image processing, automation, software development, hardware control, and much more.

Do you want to create your own software?
Work with data or images?
Automate repetitive tasks?
Control or manage your own hardware?

Whether you are just starting to learn Python or already have a specific project and need some guidance, I would be happy to help you.

My goal is to explain things clearly, adapt to your level, and help you understand not only how to make something work, but also why it works.

Let's turn your ideas into working Python projects!
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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.
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Do you want to learn programming but don't know where to start? Are you a beginner looking for clear explanations, or do you want to improve your coding skills with personalized support?

This private tutoring program is designed to help you master Python, one of the most versatile and easy-to-learn programming languages. Through a step-by-step approach, interactive exercises, and hands-on projects, you'll gain the confidence and skills to write code effectively and solve real-world problems.

💡 Why Choose this Program?
With these personalized courses, you will:
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🚀 Learn at your own pace – Whether you’re a complete beginner or want to refine your skills, lessons adapt to your level.
🎯 Master the fundamentals – Understand key concepts like variables, loops, functions, and object-oriented programming.
🏗️ Complete hands-on projects – Work on real-world coding exercises, from simple scripts to mini-apps.
🐍 Develop problem-solving skills – Learn how to break down complex tasks and write efficient code.
🎓 Prepare for exams, jobs or personal projects – Whether you are a student, professional or enthusiast, Python is an essential skill.
💻 Get live support – Get real-time feedback, coding tips, and answers to your questions.
This interactive, hands-on learning experience ensures you understand Python while having fun coding!

📚 What Will You Learn?
This program covers everything you need to become comfortable with programming in Python:

✅ Python Basics (Great for Beginners)
Introduction to Python and installation of the development environment 🖥️
Variables, data types and user input 🔢
Operators and expressions 🧮
Conditional structures (if-else) and loops (for, while) 🔄
Writing and calling functions 🏗️
🚀 Intermediate Concepts (To Go Further)
Lists, Tuples, Dictionaries – Understanding Data Structures 📊
File Management – Reading and Writing Files 📄
Exception Handling – Making Your Code More Robust ⚠️
Introduction to modules and libraries 🏛️
🎯 Advanced Concepts (For the More Ambitious)
Object-Oriented Programming (OOP) – Classes, Objects, Inheritance 🏗️
Recursion and algorithm design 🧠
API and Web – Connecting Python to the web 🌐
Introduction to Databases – Storing and Retrieving Data 🗄️
🎨 Practical Projects & Applications
Creating simple games 🎮
Automation of repetitive tasks 🔄
Data analysis with pandas 📊
Web scraping and working with APIs 🌍
Introduction to Artificial Intelligence and Machine Learning 🤖 (optional for advanced learners)
🎯 An Interactive and Fun Learning Experience
Live Online Classes – Learn from home with interactive sessions via screen sharing.
Tailor-made lessons – Content adapts to your level for an effective and personalized learning journey.
Practical exercises and projects – Less theory, more practice! Learn with real-world examples.
Caring and motivating environment – No pressure, no judgment: progress at your own pace.
Practical application – Each concept learned is applied immediately through exercises and mini-projects.
🔔 For Who?
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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This course introduces students to the fundamentals of Information and Communication Technology (ICT) and its role in modern society. Topics include computer hardware and software, digital communication tools, internet technologies, data management, cybersecurity, and emerging trends. Students will gain practical skills in using productivity software, conducting online research, and understanding the ethical and responsible use of digital resources. The course emphasizes both technical proficiency and digital literacy, preparing learners to confidently navigate and contribute to a technology-driven world.
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These courses are part of a practical and accessible introductory training course in web development 🌐💻, designed for those who want to learn how to create a website from A to Z.
Using fundamental web technologies—HTML 🧱, CSS 🎨, and JavaScript ⚙️—you'll learn how to structure pages, style them, and make them interactive.

My teaching approach is hands-on: each concept is accompanied by concrete examples and progressive exercises to consolidate your knowledge. You'll discover how to build a responsive interface 📱💡, manage user events 🖱️, and apply simple animations ✨ to improve the user experience.

Whether you are a beginner or looking to consolidate your foundations, this training will guide you step by step towards autonomy in creating your own web projects 🚀

Training plan in 10 modules:

1. Introduction to programming 🧠💡
Understand the basics: logic, Linux commands, simple instructions and algorithms.

2. Introduction to HTML 🧱📄
Structure of a web page, main tags, links, images, tables, forms.

3. Accessibility and SEO ♿🔍
Create inclusive and well-referenced sites: semantic tags, ARIA attributes, good SEO practices.

4. Introduction to CSS 🎨🖌️
Formatting, colors, typography, boxes, flexbox and responsive layout.

5. Git & GitHub 🗂️⚙️
Version your code and collaborate effectively: Git commands, creating repositories, push/pull, pull requests.

6. JavaScript - Data Types 🔢📝
Numbers, strings, booleans, objects, arrays... Learn to manipulate data.

7. JavaScript - Functions and Methods ⚙️🧩
Define and call functions, understand scope, use native methods.

8. JavaScript - Terms ❓🔁
Master the if, else, switch statements to handle specific cases.

9. JavaScript - Arrays and Loops 🔄📊
Iterate efficiently with for, while, forEach, manipulate arrays like a pro.

10. DOM Manipulation & Closures 🌐✨
Edit HTML/CSS from JS, add events, understand closures for advanced interactions.
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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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Private Programming Lessons for you / your family / your company employees
Programming Tutor – IGCSE & Computer Science Subjects
Deeper understanding, stronger results

• Lecturer at the American University AUC
• Over 20 years of experience in training students for government employees, oil companies (BP), food companies (Nestle), banks (CIB), and telecommunications companies (Vodafone).

• Teaching curricula, syllabuses, courses:
o IGCSE (Computer Science 0478, ICT 0417)
o Programming and computer courses for all educational levels (from primary to university)
o Microsoft Windows, Word, Excel, PowerPoint, Outlook, MS-Project
o Programming, C, C++, VB.NET, C#, Python, Database, SQL, MQL, VBA
o HTML, CSS, JavaScript, Angular
o Different database systems
o Data analysis using Excel
o Computer and Information Colleges Curricula
o Using artificial intelligence in life and work

• Master office applications to improve your job performance.
• Prepare yourself to work as a Front-End / Back-End / Full Stack Developer
• Theoretical and practical training for market requirements
• Don't miss out on technology. Lessons are designed for the elderly, in a simple and understandable way (use of computers and their programs, use of mobile phones, dealing with the Internet and social media).
• Lessons are available in person or online.
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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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Learn the fundamentals of ethical hacking and web application security through practical, controlled exercises.

Topics include:

Introduction to cybersecurity and ethical hacking.

HTTP requests, responses, headers, cookies, and sessions.

Web application security fundamentals.

Burp Suite: intercepting, inspecting, and analyzing HTTP traffic.

Introduction to common web vulnerabilities, including SQL injection and XSS.

Authentication security and password protection.

Understanding credential stuffing and automated attacks.

Introduction to OpenBullet and SilverBullet from a defensive security perspective.

Identifying suspicious automation and protecting web applications.

Hands-on exercises using intentionally vulnerable applications and authorized labs.

All practical exercises are conducted in authorized environments, with an emphasis on responsible security testing and defensive knowledge.
Good-fit Instructor Guarantee
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