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This teacher has a fast response time and rate, demonstrating a high quality of service to their students.
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Since October 2022
Instructor since October 2022
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2 repeat students
Trusted choice for 2 returning students
R Programming for Data Analysis – From Beginner to Confident Analyst
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From 13 $ /h
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This course is designed for students and professionals who want to learn how to analyze data using the R programming language. You will start with the basics of R, including variables, data types, and simple functions, and then move on to real-world data analysis skills such as data cleaning, visualization, and basic statistics.

By the end of the course, you will be able to work with datasets, create clear and professional graphs, and perform meaningful data analysis for projects, studies, or work.
Location
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Online from Egypt
About Me
لقد قمت بالتدريس لكثير من الطلبة بمختلف أعمارهم من الأطفال حتى طلاب الجامعة، وهذا ساعدني على فهم ما يحتاجه الطالب من خلال تعاملي معه و من خلال سنته الدراسية، أستطيع فهم ما يحتاجه الطالب حقًا بجانب الشرح المميز و التمرين الكثير حتي نستطيع الوصول لأعلى النتائج بإذن الله
Education
طالب بالسنة الأخيرة في كلية الحاسبات و علوم البيانات جامعة الإسكندرية، حاصل على مجموع تراكمي 3.88، و قد قمت بدراسة لغات برمجة مثل بايثون، جافا، R، و درست مواد متعلقة بالذكاء الاصطناعي (لقد كانت هذه أمتعهم)، درست أيضًا مواد متعلقة بالرياضايات و فروعها مثل التفاضل و التكامل و الرياضايات الخطية
Experience / Qualifications
مدرب روبوتات في مركز روبوكيد، أقوم بتعليم الأطفال و النشأ، ومساعدتهم في رحلتهم الشيقة في بناء الروبوت الذي يريدونه.
معلم برمجة أونلاين في مركز بناة العقل، أقوم بتعليم الأطفال لغة scratch، و القيام بصنع ألعاب من خلال بررنامج pictoblox.
مدرس لبعض المواد الجامعية(الرياضيات- التفاضل و التكامل) لطلاب في جامعات سعودية.
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
60 minutes
90 minutes
The class is taught in
English
Arabic
Reviews
Availability of a typical week
(GMT -05:00)
Chicago
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
Teaching the entire mathematics curriculum to students from the third grade to the third preparatory grade, while following up on the student’s level and working on the lessons with which he faces difficulty in understanding, with a focus on the solution to master any skill that the student must master, you will feel the necessary confidence before entering the exam.
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This fun and interactive course introduces kids and teens to the fundamentals of coding using Scratch, a visual programming language developed by MIT. Students will learn to create games, animations, and interactive stories while developing problem-solving, logical thinking, and creativity skills—all in a playful, drag-and-drop environment!
Who Should Join?
✔ Kids & teens curious about coding and game design
✔ Young learners who enjoy storytelling, art, or technology
✔ Future coders looking for a fun introduction to programming
Read more
Show more
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Engineer and senior professor of engineering sciences provides support courses in analog and digital electronics at all levels, engineering schools. having a scientific and technical knowledge, five years of experience in the field of teaching, teaching and a sense of listening and analysis, I am able to help pupils and students and train them in the chapters of which they have difficulties. for more info please contact me
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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.
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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.
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doctoral student in engineering sciences provides support courses in analog and digital electronics at any DEUG level and engineering schools. having scientific and technical knowledge, three years of experience in the field of teaching, pedagogy and a sense of listening and analysis, I am able to help pupils and students and train them in the chapters of which they are having difficulty. for more info please contact me
verified badge
While adults are still debating whether kids should use AI, they are already using it.
The question isn't "should they?" it's "how do we do it intelligently?"

In this course, your child will discover:
✓ What AI actually is: not magic, not mystery. How machines think, what they can do, what they can't
✓ How ChatGPT really works: not just "ask a question and get an answer," but why it responds that way, where it fails, when to trust it
✓ What LLMs are (Large Language Models): in language they understand, not tech jargon
✓ Create with AI: custom avatars, interactive stories, real projects using real tools
✓ Think critically about AI: Bias, privacy, creativity. What does AI do better than humans? What can't it do?
✓ Real-world applications: How AI transforms medicine, education, art, gaming, everyday life

Why this is different:
Most AI courses for kids teach "here's the tool, use it." I teach how to think about AI.
Your child will learn to see AI not as black magic or a solution to everything, but as a powerful tool with real limits.
And, more importantly: that they can control how they use it.

What they take home:
Real projects they created (custom avatar, interactive app, analysis of a real AI case study). A genuine understanding of how it works. And the ability to use AI responsibly and creatively.

Format: Online | 60–90 min sessions | Flexible, adapted to their age and pace

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!
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! :)
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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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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
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 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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Experienced Teacher – Mathematics, Statistics & Computer Science

With more than 10 years of experience, I support pupils and students from middle school to university, as well as candidates for competitive exams (BCE, ECRICOME, TAGE MAGE, IAE MESSAGE, etc.).

Courses offered:

Mathematics (academic support, in-depth study, exam/competition preparation)
Applied Statistics & Probability
Computer science: Python, SQL, VBA, Data Mining
Methodological coaching (organization, efficiency, self-confidence)

Method: progressive and personalized approach, based on a diagnostic assessment, targeted exercises and regular monitoring to ensure lasting progress.

In-person or online teaching – individual or small group lessons.

Strengths:
More than 10 years of experience
Concrete results (students admitted to the best schools)
Clear, structured and motivating teaching

Prices adapted according to the level and the package chosen.
First free exchange to assess your needs.

Achieve your goals with quality support
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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

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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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Teaching python to beginners!

In these classes, you will learn the basics of python programming, functions, lists, sets, and much more with practice assignments and assessments...

I have experience in teaching python to university peers.
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Contact Mohamed
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Engineer and senior professor of engineering sciences provides support courses in analog and digital electronics at all levels, engineering schools. having a scientific and technical knowledge, five years of experience in the field of teaching, teaching and a sense of listening and analysis, I am able to help pupils and students and train them in the chapters of which they have difficulties. for more info please contact me
verified badge
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
doctoral student in engineering sciences provides support courses in analog and digital electronics at any DEUG level and engineering schools. having scientific and technical knowledge, three years of experience in the field of teaching, pedagogy and a sense of listening and analysis, I am able to help pupils and students and train them in the chapters of which they are having difficulty. for more info please contact me
verified badge
While adults are still debating whether kids should use AI, they are already using it.
The question isn't "should they?" it's "how do we do it intelligently?"

In this course, your child will discover:
✓ What AI actually is: not magic, not mystery. How machines think, what they can do, what they can't
✓ How ChatGPT really works: not just "ask a question and get an answer," but why it responds that way, where it fails, when to trust it
✓ What LLMs are (Large Language Models): in language they understand, not tech jargon
✓ Create with AI: custom avatars, interactive stories, real projects using real tools
✓ Think critically about AI: Bias, privacy, creativity. What does AI do better than humans? What can't it do?
✓ Real-world applications: How AI transforms medicine, education, art, gaming, everyday life

Why this is different:
Most AI courses for kids teach "here's the tool, use it." I teach how to think about AI.
Your child will learn to see AI not as black magic or a solution to everything, but as a powerful tool with real limits.
And, more importantly: that they can control how they use it.

What they take home:
Real projects they created (custom avatar, interactive app, analysis of a real AI case study). A genuine understanding of how it works. And the ability to use AI responsibly and creatively.

Format: Online | 60–90 min sessions | Flexible, adapted to their age and pace

For curious kids asking "How does ChatGPT actually know things?"
verified badge
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!
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
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.
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
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 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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Experienced Teacher – Mathematics, Statistics & Computer Science

With more than 10 years of experience, I support pupils and students from middle school to university, as well as candidates for competitive exams (BCE, ECRICOME, TAGE MAGE, IAE MESSAGE, etc.).

Courses offered:

Mathematics (academic support, in-depth study, exam/competition preparation)
Applied Statistics & Probability
Computer science: Python, SQL, VBA, Data Mining
Methodological coaching (organization, efficiency, self-confidence)

Method: progressive and personalized approach, based on a diagnostic assessment, targeted exercises and regular monitoring to ensure lasting progress.

In-person or online teaching – individual or small group lessons.

Strengths:
More than 10 years of experience
Concrete results (students admitted to the best schools)
Clear, structured and motivating teaching

Prices adapted according to the level and the package chosen.
First free exchange to assess your needs.

Achieve your goals with quality support
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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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Teaching python to beginners!

In these classes, you will learn the basics of python programming, functions, lists, sets, and much more with practice assignments and assessments...

I have experience in teaching python to university peers.
Good-fit Instructor Guarantee
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Contact Mohamed