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Since July 2026
Instructor since July 2026
Python for beginer to start any new project (Usefull for my class Become OT/IT,SCADA DCS engineer)
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From 28 € /h
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This hands-on training pathway is designed to help students kickstart any project, specifically tailored for OT labs and industrial applications. Starting from absolute scratch, students will build a strong foundation in Python programming through practical, industry-relevant concepts.

Curriculum Outline: |
01 - Python Environment Setup & Basics |
02 - Python Variables, Numbers, Bytes & Hex |
03 - Control Flow Logic Functions |
04 - Data Structures (Lists, Tuples, Dictionaries & Sets) |
05 - String Formatting, Comprehensions & Exception Handling |
06 - File IO, Pathlib & Context Managers |
07 - Object-Oriented Programming (Classes & OOP) |
08 - Standard Library, Modules & Networking Basics |

Assessment & Evaluation:
Students will take a mini-test after the completion of each module. Additionally, an Audit & Performance Evaluation report will be sent following the tests.
Duration:
5 days to 15 days (depending on the pace of the cohort)
Extra information
- Equipment: A laptop is required for hands-on exercises and practical work.
- Session Recording: Classes are recorded to create post-session summaries and key takeaways for easy revision.
- Feedback & Progress: Student reviews are gathered after each session to fine-tune the learning and objectives.
Location
location type icon
Online from Morocco
About Me
Results-driven Senior Solution Architect with over 10+ years of expertise in Operational Technology (OT), Industrial Control Systems (ICS), SCADA, and IIoT ecosystems. Proven track record of designing, modernizing, and governing complex OT architecture for industrial environments while bridging the gap between IT enterprise standards and plant-floor operational requirements. Recognized for developing reference architectures, design patterns, and strategic technology roadmaps using TOGAF and ISA/IEC frameworks. Adept at vendor integrations like (Areva, Schneider Electric, Siemens , Alstom , Abb ) and leading cross-functional teams toward successful digital transformations.
Education
Bachelor of Engineering / Computer Science : Oran University 1 Ahmed Ben Bela 2002
SNMP Protocol developper
Design a monitoring system for the compus
+ Monitoring of Servers
+ Monitoring of Sun Microsystem Workstation
+ Monitoring of Switch and router 3com and Cisco
+ Integration on IDS
Experience / Qualifications
- Network Administrator & Application Developer (1 year)
- Senior SCADA System Engineer (5 years)
- Senior DCS System Engineer (2 years)
- Senior Technical Sales & Proposals Engineer SCADA & DCS (4 years)
- Business Analyst Specialist - GIS (1 year)
- OT/IT Consultant
Age
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Duration
60 minutes
The class is taught in
English
French
Arabic
Availability of a typical week
(GMT -04:00)
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
Master Industrial Control Systems (SCADA, DCS, IIoT) and automation through tailored, hands-on coaching based on real-world industrial projects! With over 10 years of international engineering, solution architecture, and technical business development experience working with major industry vendors

I offer practical courses designed for engineering students, university undergraduates, and professionals looking to upskill.

The pathway will be in 7 days to cover all basics in OT environnement :
Day 1 - Virtual Environment Preparation for OT projects
- Install Hypervisor on your workstation (A virtual machine).
- Create a Linux VM (Fedora Server).
- Configure 2 networks on the VM: one in NAT (internet access) and one in Host-Only Network (to isolate lab traffic).
- Install basic tools for OT
Day 2: Modbus PLC Simulation (Add 2 Server and test script client to connect)
- Implement Modbus PLC simulators and architecture overview
- Create PLC simulator scripts in src/plc-simulators/
- Add validation test script for Modbus connectivity
- Update Day 2 guide with detailed implementation steps and compatibility notes
Day 3: NGINX Load Balancer Configuration (Round Robin)
- Understand NGINX Stream Module
- Configure NGINX
- Verify and Load the Module
- Troubleshooting NGINX (Activate load balancing in layer 4 protocol, Set permission)
- Step-by-Step Load Balancer Validation
- Test Load Balancing (Round-Robin)
- Test Failover (Resilience)
Day 4 - Creation of the traffic generator (SCADA Client)
- TBD
Day 5 - Traffic capture and measurement with TShark
TBD
Day 6 - Advanced analysis and overload simulation
TBD
Day 7 - Grafana
-TBD
What we can cover together based on your goals:
Read more
- Modbus Protocol Introduction
- Modbus Frame Structure & Byte Analysis
- Modbus Exception Handling & Error Codes

Each module in this series is structured with core learning content followed by two mandatory practical components:
Challenge: A hands-on troubleshooting or design scenario to test your practical skills.
Audit: A checklist and verification quiz to ensure full mastery before moving to the next section.
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fluid mechanics thermodynamics physics, thermodynamics 1 and 2, thermo b, calculus, turbo diesel engines, calculus, integration, control and design of Fluid Turbo Machine and electrical circuits MATLAB thermodynamics fluid .aerodynamics heat transfer PLC mathematics matlab control power plant air conditioning system and refrigeration ice circuit ,thermo b vibration theory of machine robots Aerospace engineering aeronautics
CFD fluent ansis gambit solidworks electric machines stress analysis design
thermodynamics b fluid advanced mathematics aerodynamics heat and mass transfer PLC mathematics matlab control power plant air conditioning system numerical methods desalination hydraulic circuit and refrigeration nuclear energy calculus Saudi Aramco tests turbo machine Quadcopter Drones
FE course projects and more for mechanical engineering, electrical engineering civil
engineering industrial engineering mechatronics engineering

Some of the universities we teach are
Princess Nourah bint Abdulrahman University - PNU - Saudi ArabiaKING ABDULAZIZ UNIVERSITY - Saudi ArabiaDammam University (Imam Abdul Rahman bin Faisal) - (imam abdulrahman bin faisal university)University of Dammam - Saudi ArabiaKing Faisal University - King Faisal University - KuwaitUniversity of Kuwait - kuwait university - KuwaitKuwait College of Science & Technology - KuwaitEmirates College of Technology - Emirates College of Technology - United Arab EmiratesYanbu University College - Saudi ArabiaNYIT - New York Institute of Technology (NYIT) - United Arab EmiratesKing Fahd University of Petroleum and Minerals (KFUPM) - Saudi ArabiaThe Petroleum Institute (PI) - United Arab EmiratesThe American University of Kuwait - American University of Kuwait - KuwaitAmerican University of the Middle East - American University of the Middle East - KuwaitCanadian University Dubai (CUD) - United Arab EmiratesSultan Qaboos University - Sultan Qaboos University - Sultanate of OmanHigher College of Technology - Sultanate of OmanGerman University of Technology in Oman - GERMAN UNIVARCITY OF TECHNOLOGY IN OMAN - Sultanate of OmanUniversity of Nizwa - University of Nizwa - Sultanate of Oman
Kuwait University Hafr Al-Batin University Princess Nora Bint Abdul Rahman University King Abdulaziz University Jubail Industrial College Jubail University College Dammam University King Faisal University Kuwait College of Science and Technology Emirates College of Technology Yanbu University College Saudi Arabia New York Institute of Technology Petroleum Institute Saudi Aramco Exams King Fahd University of Petroleum and Minerals The British University, The American University in Kuwait, The American University of the Middle East, Kuwait
Canadian University, Dubai, Sultan Qaboos University, Sultanate of Oman, Higher College of Technology, Sultanate of Oman
The Russian University The German University The German University The University of Nizwa Qatar University Northwestern University in Qatar - Northwestern University in Qatar Texas A&M University at Qatar - QatarGeorgetown University in Qatar - QatarVirginia Commonwealth University in Qatar - Virginia Commonwealth University - Qatar - College of the North Atlantic Qatar - Qatar
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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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Discover programming lessons suitable for children! With a fun and educational approach, my lessons allow young minds to dive into the fascinating world of programming. Provide your children with an enriching learning opportunity in a fun and stimulating environment.
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I adapt to the profile of each intern or student according to their objective and level.
The goal is not to work endless hours, but to show how to be able to work alone or partially independently in a short time.
It is always a pleasure for me to be recognized in the street 15 years after classes, and to be thanked for having provided a real service to the person concerned.
This activity is a passion of mine that I have been pursuing since I was 16 years old.
I hope to be able to share it with you to prepare effectively, by significantly reducing your legitimate apprehension of exams.
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🐍 Python Course – Learn to code and create your projects!

This course is for anyone who wants to:

✅ Learn Python from the beginning
✅ Strengthen their programming skills

📚 On the program:

Variables

Loops

Functions

Data structures

Practical projects for implementation

💡 How does the course work?

Clear explanations to understand the programming logic

Targeted exercises adapted to your level

Concrete projects to create your own applications

🎯 My goal:

Helping you understand the logic behind the code

Progress at your own pace

Create your own projects in Python and gain independence
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This computer science support course is designed for students and learners wishing to strengthen their foundations or improve their level in computer science and programming.
I support participants in a pedagogical and progressive manner, adapting to their level and objectives (university courses, training, practical work, exams, projects).
The goal is to understand, practice and gain autonomy through clear explanations and concrete examples.
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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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This cohort is designed for young people who want to learn in an affordable, flexible, and enjoyable way without having to dedicate a huge amount of time each week or even just extra support.

This beginner-friendly course introduces students to the world of computers and computer science through simple explanation.

Students will learn how computers work, including hardware, software, memory, storage, data, and how a computer processes information. They will then explore how applications are used to create and organize information, with practical experience using tools such as Microsoft Word, PowerPoint, and Excel.

As the course progresses, students will be introduced to important computer science concepts including binary numbers, algorithms, programming, databases, networks, the Internet, and cybersecurity.

By the end of the course, students will have a good foundation in computer science and improved digital skills.
Good-fit Instructor Guarantee
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