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Since October 2025
Instructor since October 2025
Hands On Introduction to Data Analytics with Power BI Practical Sessions for Real-World Insight
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From 152 £ /h
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Power BI is one of the most powerful tools for turning raw data into meaningful insights.
In this class, you’ll learn how to connect data sources, clean and transform data, create interactive dashboards, and visualize insights that support smart business decisions.

Through practical, hands-on exercises, you’ll gain the skills to analyze trends, identify patterns, and communicate results effectively, whether you’re a student, business professional, or aspiring data analyst.
Extra information
Bring your own laptop with Power BI Desktop installed (free download from Microsoft Store).
Location
location type icon
Online from United Kingdom
About Me
Hi, I’m Okechukwu, a passionate educator and technology professional with over six years of experience across Data Analytics, Quality Assurance, Cloud Computing, and Software Development. I’m currently pursuing a Master’s in Artificial Intelligence at the University of Wolverhampton, where I focus on data science, machine learning, and intelligent automation.

I love helping learners build both technical competence and confidence. Whether you’re a beginner looking to understand coding concepts, a student aiming to master mathematics and logical reasoning, or a professional seeking to break into the data or tech field, I design lessons that are interactive, practical, and goal-oriented.

Beyond tech, I’m also an avid chess player, teaching the game not only as a sport but as a tool for strategic thinking, focus, and patience. My students enjoy learning through real-world examples, structured progress tracking, and a supportive learning environment tailored to their needs.
Education
Master’s in Artificial Intelligence (Ongoing)
University of Wolverhampton, United Kingdom
2025 – Present
Focus: Data Science, Machine Learning, and Intelligent Automation

Diploma in Data Science
National Institute of Information Technology (NIIT), India
2020 – 2021

B.Eng (Hons) in Computer Engineering
Michael Okpara University of Agriculture, Nigeria
2012 – 2017
Experience / Qualifications
Experience

Solutions Consultant – Sonicom (Remote, Abuja)
May 2022 – Present

Designed and implemented AI-powered, cloud-integrated platforms to automate business workflows.

Delivered data-driven solutions improving efficiency and decision-making.

Mentored junior engineers and provided technical guidance to cross-functional teams.

Solutions Architect – Industrial Arbitration Panel (IAP)
Oct 2024 – Oct 2025

Developed secure and scalable IT systems to streamline arbitration processes.

Led architecture design and system analysis aligned with business and regulatory requirements.

Senior QA Engineer & DevOps Specialist – HNG Tech
Jun 2024 – Nov 2024

Built QA automation frameworks using Selenium and Postman, cutting manual testing by 60%.

Deployed CI/CD pipelines to enhance deployment speed and reliability.

Product Development Lead & QA Engineer – Havosoft International Ltd.
Sep 2023 – Oct 2024

Managed multiple software projects using Agile methodologies.

Increased platform adoption by 25% through innovative feature development.

Chief Technology Officer (CTO) – Dotmac Technology Ltd.
Aug 2022 – Jul 2023

Directed a team of engineers maintaining 2,000+ client networks.

Reduced network downtime by 35% through proactive system monitoring.

Chess Tutor (Freelance)
2018 – Present

Teach beginners and intermediate players the fundamentals and strategies of chess.

Focus on improving decision-making, focus, and strategic reasoning.

Conduct both in-person and online lessons.

Qualifications

AWS Certified Cloud Practitioner (2022) – Cloud architecture & deployment.

Certified Software Tester (CST, 2024) – Functional, regression & automation testing.

Power BI Certified Analyst (2024) – Data visualization & business intelligence reporting.

Prompt Engineering with ChatGPT (2024) – Applied prompt design for LLM workflows.

HNG Product Testing Certificate (2024) – Agile QA, test case design & automation.

Senior QA Engineer & DevOps Specialist – Micro1 (2025)

B.Eng in Computer Engineering – Michael Okpara University of Agriculture.

Diploma in Data Science – NIIT India.
Age
Children (7-12 years old)
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
90 minutes
The class is taught in
English
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
Chess is more than just a game, it’s a science of strategy, patience, and decision-making.
In this class, I’ll guide you through the fundamentals of chess, from understanding the board and piece movements to mastering tactics, openings, and endgame strategies.

You’ll also learn how to analyze your moves, recognize opponent patterns, and develop a logical approach that strengthens your focus and critical thinking.

Whether you’re a beginner eager to learn or an intermediate player aiming to sharpen your game, this class provides structured lessons, real-match practice, and personal feedback to help you grow into a confident player.
Read more
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You will learn Systematic Reasoning & Logical Thinking which is a requirement for entering Computer Science program in many universities.
The book “Delftse Foundations of Computation” especially its second chapter will be the main source of our lesson, but other more in-depth books will be also covered if you want to improve even further on logical thinking.
The topics in our lesson include:
• Propositional Logic: Logical operators; Precedence rules; Logical equivalence; Implications in English; Exclusive or; Universal operators; Classifying propositions
• Boolean Algebra: Substitution laws
• Logic Circuits: Logic gates; Combining gates to create circuits; From circuits to propositions; Disjunctive Normal Form; Binary addition.
• Predicate Logic: Predicates; Quantifiers; Tarski’s world and formal structures;
• Deduction: Valid arguments and proofs; Proofs in predicate logic

If you have any additional questions before starting a class, please feel free to ask me. I am here to assist! :)
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Experienced and patient teacher of logic for computer science.

I have taught logic, formal languages and automata theory to undergraduates for six years. My tutoring is adapted to the student's level and goals. Whether you need to learn logic for your studies, or you would simply like to know more about the subject, I will be more than happy to help you improve your understanding and skills.

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The sciences presuppose a certain standard of rationality. An ability to distinguish between correct reasoning and claims that do not follow from the assumptions. In this class we study the basic principles of logic and apply mathematical techniques to the study thereof.
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A formal language is an abstraction of general characteristics of programming languages. Such a languages consists of a set of symbols together with some rules to determine whether a string made up out of those symbols is a member of the language.

Topics include:
Regular languages, context-free languages
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Over 10 years of experience in training and mentoring
Teaching method adapted to all levels
Hands-on approach based on concrete projects
Contagious passion for code and technological innovation

Whether you're a beginner looking to get started in programming or a professional looking to improve your skills, this course will open up exciting new opportunities in the world of software development and data science.
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✅ Cybersecurity: Ethical hacking, data protection, network security
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📌 Mentored students to achieve top grades in Cambridge ICT & Computer Science exams.
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📌 Conducted coding boot camps and cybersecurity workshops to enhance practical learning.
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AI is not a movie robot: Difference between fiction and reality.

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Where is it already present? Spell checkers, Netflix/YouTube suggestions, GPS, and voice assistants (Siri/Alexa).

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Ask him to write an administrative email or a complex letter.

Summarize a long newspaper article or document.

Plan a travel itinerary or find recipe ideas with what's left in the fridge.

AI for creativity and memory:

Generate images to illustrate a birthday card (Midjourney, DALL-E).

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Privacy protection:

Never give sensitive data (social security number, passwords, bank details) to an AI.

Knowing that everything we write to the AI is potentially used to train it.

Spotting "Deepfakes":

How to recognize a doctored image or video (details on the hands, strange reflections, slightly metallic voice).

Verify the information: the golden rule of cross-referencing sources.

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The environmental impact: The water and energy consumption of AI servers.

The future: Will AI replace us or assist us?
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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

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• 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
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• Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization

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• TensorFlow, Keras, or PyTorch depending on the learner’s project and working environment

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• 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
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• Transformer architecture, tokens, embeddings, attention mechanisms, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation
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• Reliability, hallucinations, bias, privacy, responsible use, and appropriate human validation

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

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• Code review, debugging, documentation, reproducibility, model comparison, and communication of results

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected.

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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.

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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 explain each idea before we click, then we build it together on a realistic dataset, and then you try it yourself. You get clear notes after each lesson. If you bring your own work file (with sensitive data removed), we can use it so you solve your real problem.

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Is your code not doing what you want it to? Are you learning Python but just copying and pasting without really understanding?

Loops, functions, algorithms, data structures... Behind these intimidating concepts lie simple, visual ideas. My goal: to help you understand them, not just apply them.

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- prepare for your exams (Brevet, Baccalaureate, midterms, entrance exams, NSI),
- make progress on your projects: lab assignments, scripts, games, digital projects...

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Message me to schedule your first lesson: let’s make the abstract clear!

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- Group lessons: 40 €/h

College/University:
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- Group lessons: 50 €/h
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**Learn Programming, Python, Artificial Intelligence & Data Analysis with Practical Projects!**

Are you looking to learn programming from scratch, improve your coding skills, or prepare for a future career in technology?

I'm George Zakary, a Computer and Data Science graduate from Alexandria University and a programming instructor with hands-on teaching experience in Python, Data Analysis, Cybersecurity, and Backend Development.

I offer personalized, interactive online lessons for students across Saudi Arabia, the UAE, Kuwait, Qatar, and other Gulf countries.

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- **Python Programming:** From basic concepts to functions, loops, data structures, and Object-Oriented Programming (OOP).
- **Artificial Intelligence:** Introduction to AI, Machine Learning fundamentals, and practical applications.
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**Why choose my lessons?**

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Every lesson is adapted to your age, experience, learning pace, and personal goals.

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**Lessons are available in Arabic or English, with clear explanations tailored to Arabic-speaking students.**

**للطلاب في السعودية والإمارات والكويت وقطر وجميع دول الخليج:**

أقدم دروس برمجة أونلاين باللغة العربية بأسلوب بسيط وعملي، سواء كنت مبتدئًا تمامًا أو ترغب في تطوير مستواك.

سنتعلم خطوة بخطوة من خلال التطبيق العملي والمشاريع الحقيقية، مع خطة تعليمية تناسب مستواك وهدفك.

**Start your programming journey today!**
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arrow icon previousarrow icon next
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As a Franco-Belgian management teacher, I give Excel lessons with passion!
Whether remotely or face-to-face, I offer many examples and exercises to accompany you.
I travel without problem throughout the region of Brussels and its surroundings, for lessons of at least 2 hours. For France, courses are only given remotely.

Here are some key words that will be covered in my classes:
Scenario analysis, Year, Rounding, Today, Bdnb, Bdnbval, Bdsum, Search, Column, Copy/paste in values, Copy/paste with transposition, Consolidation, Date, Datedif, Determat, Dollar, Right, Righterg, Equiv, Esterror, Estna, Frequency, Filter (simple and advanced), Format of cells, Left, Large.Value, Printing of documents, Index, Indirect, Inversemat, Day, Weekday, Line, Matrix, Max, Maxa, Max.Si, Min , Mina, Mina.If, Formatting of cells and ranges, Month, Average, Average.If, Nb, Nb.If, Nbval, Naming of cells and ranges, No, Small.value, Product, Productmat, Protection of cells, Lookup (Lookup), Lookupv (VLookup), Lookuph (HLookup), If (If), If.Not.Disp, If.Conditions, Iferror, Sum, Sumproduct, Sum.If, Sum.If.Set, Substitute , Pivot tables, Sorting, Cell locking

Do not hesitate to contact me to organize your lessons according to your needs and availability. Together, we will develop your Excel skills in an efficient and personalized way.
verified badge
If you’ve ever felt that science and math are difficult, it’s probably because no one showed you how to think like a problem solver.
In my classes, you’ll learn not just formulas or code but how to truly understand concepts, apply them, and build strong logical intuition.

I teach:
• 🔢 Mathematics: From algebra and calculus to applied problem-solving for real-world use.
• 💻 Computer Science: Coding fundamentals (Python, C++), algorithms, and logical thinking for beginners and intermediate learners.
• ⚛️ Physics: Mechanics, thermodynamics, and practical examples that make abstract ideas simple and visual.

As a Software Engineer and Master’s student in Engineering at Nagoya University, I bring both academic knowledge and hands-on experience from real projects. My teaching approach is interactive, visual, and deeply focused on understanding over memorization.

Let’s turn complex problems into clear, step-by-step insights — and make learning something you genuinely enjoy.
verified badge
You will learn Systematic Reasoning & Logical Thinking which is a requirement for entering Computer Science program in many universities.
The book “Delftse Foundations of Computation” especially its second chapter will be the main source of our lesson, but other more in-depth books will be also covered if you want to improve even further on logical thinking.
The topics in our lesson include:
• Propositional Logic: Logical operators; Precedence rules; Logical equivalence; Implications in English; Exclusive or; Universal operators; Classifying propositions
• Boolean Algebra: Substitution laws
• Logic Circuits: Logic gates; Combining gates to create circuits; From circuits to propositions; Disjunctive Normal Form; Binary addition.
• Predicate Logic: Predicates; Quantifiers; Tarski’s world and formal structures;
• Deduction: Valid arguments and proofs; Proofs in predicate logic

If you have any additional questions before starting a class, please feel free to ask me. I am here to assist! :)
verified badge
Experienced and patient teacher of logic for computer science.

I have taught logic, formal languages and automata theory to undergraduates for six years. My tutoring is adapted to the student's level and goals. Whether you need to learn logic for your studies, or you would simply like to know more about the subject, I will be more than happy to help you improve your understanding and skills.

Logic
The sciences presuppose a certain standard of rationality. An ability to distinguish between correct reasoning and claims that do not follow from the assumptions. In this class we study the basic principles of logic and apply mathematical techniques to the study thereof.
Topics include:
Propositional and Predicate Logic
Syntax and semantics
Natural deduction
Semantic tableaux
Correctness and soundness
Completeness

Formal languages and automata
A formal language is an abstraction of general characteristics of programming languages. Such a languages consists of a set of symbols together with some rules to determine whether a string made up out of those symbols is a member of the language.

Topics include:
Regular languages, context-free languages
Finite automata, pushdown automata, Turing machines
Regular expressions
Regular grammar, context-sensitive grammar
Pumping lemmas for regular and context-free languages
verified badge
Discover the power of Python with an experienced computer trainer!
I'm Hamza, a passionate developer and seasoned mentor with extensive experience teaching programming. My unique teaching approach will help you quickly acquire solid Python skills while discovering its real-world applications in the professional world.
What you will learn:

Python Fundamentals (variables, control structures, functions)
Advanced Object-Oriented Programming
Data Analysis and Visualization with Pandas and Matplotlib
Developing graphical interfaces with PyQt
Practical projects inspired by real business cases

Why choose me?

Over 10 years of experience in training and mentoring
Teaching method adapted to all levels
Hands-on approach based on concrete projects
Contagious passion for code and technological innovation

Whether you're a beginner looking to get started in programming or a professional looking to improve your skills, this course will open up exciting new opportunities in the world of software development and data science.
Join me for a captivating journey into the heart of the most versatile and in-demand programming language on the market!
Book now and take the first step towards mastering Python. Your career as a Python developer starts here!
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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.
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With over seven years of experience in teaching Computer Science & Information Technology (ICT), I have developed a strong expertise in delivering high-quality education across multiple internationally recognized curricula, including Cambridge IGCSE, GCSE, A-Levels, O-Levels, and Checkpoint. My passion lies in equipping students with coding, cybersecurity, and digital literacy skills, ensuring they are well-prepared for the evolving demands of the digital world.

Expertise & Teaching Areas:
✅ Programming & Software Development: Python, Java, C++
✅ Cybersecurity: Ethical hacking, data protection, network security
✅ Digital Literacy: ICT applications, online safety, cloud computing
✅ Data Science & AI: Data analysis, machine learning fundamentals
✅ Web Development: HTML, CSS, JavaScript

Curriculum & Pedagogical Experience:
🔹 Cambridge IGCSE & GCSE ICT & Computer Science – Teaching core and extended syllabi, focusing on programming logic, databases, and networking.
🔹 Cambridge A-Levels & O-Levels Computer Science – Preparing students for advanced computing concepts, problem-solving, and algorithm development.
🔹 Cambridge Checkpoint ICT – Building foundational skills in digital technology and computer applications.

Professional Impact:
📌 Mentored students to achieve top grades in Cambridge ICT & Computer Science exams.
📌 Developed interactive lesson plans integrating real-world applications of technology.
📌 Conducted coding boot camps and cybersecurity workshops to enhance practical learning.
📌 Guided students in project-based learning, including app development and website design.

With a strong commitment to student-centered learning and technological innovation, I am dedicated to shaping future tech leaders and empowering learners with skills relevant to careers in technology, data science, and software development.
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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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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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Objective: To understand AI without fear, to use it to simplify one's life, to know how to identify digital traps, and to use Word, Excel, etc. without difficulty.

1: Demystifying AI (What exactly is it?)
AI is not a movie robot: Difference between fiction and reality.

How it works (simply): The image of the "giant library": AI has read billions of books and uses them to predict the continuation of a sentence or create an image.

Where is it already present? Spell checkers, Netflix/YouTube suggestions, GPS, and voice assistants (Siri/Alexa).

2: Using AI to make life easier
Conversing with AI (ChatGPT, Claude, Gemini):

Ask him to write an administrative email or a complex letter.

Summarize a long newspaper article or document.

Plan a travel itinerary or find recipe ideas with what's left in the fridge.

AI for creativity and memory:

Generate images to illustrate a birthday card (Midjourney, DALL-E).

Using AI to restore or colorize old family photos.

3: Learning to "talk" to AI (The Art of the Prompt)
The context method: Why "Give me a cake recipe" is less effective than "I am allergic to gluten and I am hosting 4 people, give me a simple chocolate cake recipe".

The expert's role: Learning to tell AI "Act like a travel guide" or "Act like an expert gardener".

4: Precautions and Critical Thinking (The Survival Guide)
"Hallucinations": Understand that AI can make false claims with complete certainty (never take medical or legal advice from AI without verification).

Privacy protection:

Never give sensitive data (social security number, passwords, bank details) to an AI.

Knowing that everything we write to the AI is potentially used to train it.

Spotting "Deepfakes":

How to recognize a doctored image or video (details on the hands, strange reflections, slightly metallic voice).

Verify the information: the golden rule of cross-referencing sources.

5: Ethics and Impacts (To go further)
Copyright: Who owns an image created by AI?

The environmental impact: The water and energy consumption of AI servers.

The future: Will AI replace us or assist us?
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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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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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Spending hours every month cleaning the same Excel reports? Or preparing for the PL-300 (Microsoft Power BI Data Analyst) exam and not sure where to start? I teach working professionals and career changers how to use Power BI and Excel the way they're used at work, step by step and in plain language.

What you'll learn (tailored to your goal):
• Power Query: clean messy data (extra spaces, wrong data types, duplicates, text numbers) and set it up so next month is just "Refresh"
• Data modelling: star schema, relationships and a proper Date table
• DAX: measures, CALCULATE and time intelligence, explained so you understand why, not just copy formulas
• Dashboards: clear visuals, slicers, drill-down and Actual vs Target reports
• Power BI Service: publishing, refresh and sharing
• PL-300 exam practice: exam-style questions with explanations

How I teach:
I explain each idea before we click, then we build it together on a realistic dataset, and then you try it yourself. You get clear notes after each lesson. If you bring your own work file (with sensitive data removed), we can use it so you solve your real problem.

About me: Data Science Master's student and BI analyst. I built Power BI and Excel reporting for companies, and recently automated a finance team's monthly SAP report with Power Query so it now updates with one click.
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Is your code not doing what you want it to? Are you learning Python but just copying and pasting without really understanding?

Loops, functions, algorithms, data structures... Behind these intimidating concepts lie simple, visual ideas. My goal: to help you understand them, not just apply them.

These tools may seem difficult at first, but they make perfect sense once you visualize them in practice: seeing a loop run, a variable change, a list fill up. That’s the level I’d like to help you reach, using diagrams, analogies, and programs we’ll build together, line by line.

I offer computer science lessons (Python and other languages), from middle school through college, to help you:
- overcome obstacles and regain confidence,
- prepare for your exams (Brevet, Baccalaureate, midterms, entrance exams, NSI),
- make progress on your projects: lab assignments, scripts, games, digital projects...

Each lesson is tailored to your needs: we’ll go over the tricky parts, code together, and I’ll teach you methods to become self-sufficient.

Message me to schedule your first lesson: let’s make the abstract clear!

High School/Middle School:
- Private lessons: 25 €/h
- Group lessons: 40 €/h

College/University:
- Private lessons: 35 €/h
- Group lessons: 50 €/h
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**Learn Programming, Python, Artificial Intelligence & Data Analysis with Practical Projects!**

Are you looking to learn programming from scratch, improve your coding skills, or prepare for a future career in technology?

I'm George Zakary, a Computer and Data Science graduate from Alexandria University and a programming instructor with hands-on teaching experience in Python, Data Analysis, Cybersecurity, and Backend Development.

I offer personalized, interactive online lessons for students across Saudi Arabia, the UAE, Kuwait, Qatar, and other Gulf countries.

**What can you learn with me?**

- **Python Programming:** From basic concepts to functions, loops, data structures, and Object-Oriented Programming (OOP).
- **Artificial Intelligence:** Introduction to AI, Machine Learning fundamentals, and practical applications.
- **Data Analysis:** Learn to work with data using Python, Pandas, and practical examples.
- **AI Automation:** Explore automation concepts and tools such as n8n.
- **Backend Development:** Build web applications using Python and Flask.
- **Cybersecurity Fundamentals:** Understand essential security concepts and safe computing practices.

**Why choose my lessons?**

My teaching approach focuses on simplifying complex technical concepts through real-world examples, live coding, practical exercises, and hands-on projects.

Every lesson is adapted to your age, experience, learning pace, and personal goals.

Whether you are a complete beginner, a school or university student, or a professional looking to develop new technical skills, I will help you learn step by step.

**Lessons are available in Arabic or English, with clear explanations tailored to Arabic-speaking students.**

**للطلاب في السعودية والإمارات والكويت وقطر وجميع دول الخليج:**

أقدم دروس برمجة أونلاين باللغة العربية بأسلوب بسيط وعملي، سواء كنت مبتدئًا تمامًا أو ترغب في تطوير مستواك.

سنتعلم خطوة بخطوة من خلال التطبيق العملي والمشاريع الحقيقية، مع خطة تعليمية تناسب مستواك وهدفك.

**Start your programming journey today!**
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