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Since July 2021
Instructor since July 2021
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Discrete Mathematical Structures
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From 7.54 Fr /h
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Discrete mathematical structures
It is one of the courses for the first year of the faculties of computer science, artificial intelligence and faculties of engineering. The explanation includes the lessons Logic statements-truth tables-gates-dets-op- logical circuits-reducing circuit-logical gates-relations-functions
Location
location type icon
Online from Egypt
About Me
I explain in an easy and simplified manner, loving my specialization, and I enjoy teaching for students, and I do not make my lessons one-sided only, but I make students participate in thinking about issues and solutions, or even trying to reach a solution. In my teaching, I take into account the different levels of students, continuous follow-up through periodic tests and assignments, and the use of educational methods that are attractive to students, and an easy method far from complexity to entice students in what I teach.
Education
He holds a PhD in Mathematical Statistics - Cairo University - a master's degree in the same specialization and a BA in mathematics and statistics, Cairo University.
PH.D in Mathematical Statistics -Many courses from British Cancel in English Language up to advanced level
Experience / Qualifications
More than 19 years experiences in teaching Math.
I have taught mathematics for more than 19 years in many international and national schools in the American and British departments, as well as language schools. I have also taught online and through famous educational platforms in various countries and so far, such as Fahim, Al-Ustad, Anar, my courses, my school and Nafham.
Age
Adults (18-64 years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
The class is taught in
Arabic
English
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
Preparing the student to pass the quantitative aptitude test to qualify for admission to applied and practical colleges in the Kingdom of Saudi Arabia.
This applies to the test provided by the Ministry of Education in the Kingdom, whether the test is paper-based or computer-based
Read more
I explain the topics with solving problems from the previous exams in an easy way and make the student participate in the solution during the class and give a quiz after each class to the student with the homework as well. There is continuous follow-up to improve the student’s level after each class, weekly and monthly. I take into account all the levels of weak, medium, good, very good and excellent. If the class is group, I divide the students according to the level so as to assign each group an appropriate method and means of explanation.
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► UNIVERSITY, FOUNDATION, IB, A-LEVEL & INTERNATIONAL SCHOOL SUPPORT

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► STATISTICS, DATA ANALYTICS & AI SUPPORT

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► APPLIED STATISTICS WITH R
I support students in using R for statistical analysis, data cleaning, visualisation, hypothesis testing, regression models and interpretation of outputs. Students learn how to connect theory, calculation, code and real meaning step by step.

► QUANTITATIVE METHODS & RESEARCH STATISTICS
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► DATA ANALYTICS & DATA SCIENCE
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With a doctorate in mathematics and experience as a university lecturer, I have dedicated several years to teaching students in France and internationally. I support students of varying levels, emphasizing a clear, progressive, and comprehension-focused approach.
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1. Algebra and linear algebra
The foundation of success in mathematics. General algebra: groups, rings, fields and arithmetic.
Linear algebra: Vector spaces, reduction of endomorphisms (diagonalization, triangularization), duality and quadratic forms.
2. Topology and Functional Analysis
Metric spaces and topology: open and closed sets, compactness, convexity.
Normed vector spaces: convergence of sequences, continuity and equivalence of norms.
Banach & Hilbert spaces: study of completeness, orthogonal projections, Riesz theorems and Fourier series.
3. Numerical Analysis: The art of solving mathematical problems using algorithms and high-performance computing.
Solving nonlinear equations: bisection, Newton-Raphson and fixed-point methods.
Numerical integration: rectangle rule, trapezoidal rule and Simpson's rule.
Numerical matrix analysis: solving systems of equations (Gauss, LU, Cholesky), eigenvalue calculations and conditioning.
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This course offers a comprehensive exploration of Information Technology, data center infrastructure, guiding students through the entire lifecycle—from initial design and planning to day-to-day operations and long-term performance optimization. Students will learn the critical components of data center design, including site selection, power and cooling systems, space planning, networking, and physical security. The course also covers operational best practices, monitoring tools, energy efficiency strategies, disaster recovery planning, and emerging trends. By integrating technical, environmental, and management perspectives, students will gain the knowledge and skills required to build and maintain high-performance, cost-effective, and sustainable data center environments.
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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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► EXPERT STATISTICS, DATA ANALYTICS, MACHINE LEARNING & AI TUTOR FROM SWITZERLAND
► UNIVERSITY, FOUNDATION, IB, A-LEVEL & INTERNATIONAL SCHOOL SUPPORT

I completed my Master’s degree in Business Information Systems at a Swiss University of Applied Sciences, where my academic background strongly combined mathematics, statistics, data analysis, analytical thinking and problem-solving. This technical and data-oriented foundation shaped the way I teach today: clearly, logically and with a strong focus on real understanding.

For many years, I have successfully supported students in Statistics, Data Analytics, Machine Learning and AI. My main focus is especially on Statistics — from basic descriptive statistics to advanced statistical methods, hypothesis testing, regression, probability distributions and interpretation of results.

I mainly use R for statistical analysis, data handling, visualisation and practical exercises. My goal is not only to help students calculate results, but to make sure they understand what the results mean and how to explain them correctly.

► STATISTICS, DATA ANALYTICS & AI SUPPORT

► STATISTICS & PROBABILITY
I help students understand descriptive statistics, probability, random variables, distributions, sampling, confidence intervals, hypothesis testing, p-values, correlation, regression and statistical interpretation. My lessons focus on explaining the logic behind each method, not just applying formulas.

► APPLIED STATISTICS WITH R
I support students in using R for statistical analysis, data cleaning, visualisation, hypothesis testing, regression models and interpretation of outputs. Students learn how to connect theory, calculation, code and real meaning step by step.

► QUANTITATIVE METHODS & RESEARCH STATISTICS
I help students with statistical methods used in business, economics, psychology, social sciences, science and university research. This includes choosing the correct test, understanding assumptions, interpreting results and presenting findings clearly.

► DATA ANALYTICS & DATA SCIENCE
I support students with data preparation, exploratory data analysis, visualisation, dashboards, summary statistics and practical interpretation. The focus is always on understanding the data and drawing meaningful conclusions.

► MACHINE LEARNING & AI FOUNDATIONS
For students working with modern data topics, I also provide support in the foundations of Machine Learning and AI, including regression, classification, clustering, model evaluation and practical applications. These topics are explained from a statistical point of view, so students understand the logic behind the models.

► UNIVERSITY, FOUNDATION & INTERNATIONAL COURSES
I support students in Statistics, Data Analytics, Business Analytics, Quantitative Methods, Econometrics, Research Methods and technical modules. I help with exam preparation, assignments, projects and practical data analysis tasks.

► HOW I TEACH

► I FOCUS ON REAL STATISTICAL UNDERSTANDING.
Statistics becomes much easier when students understand why a method is used, what the result means and how to interpret it correctly.

► I EXPLAIN FORMULAS STEP BY STEP.
Difficult formulas, tests and models are broken down into simple, logical parts so students can follow the reasoning clearly.

► I CONNECT THEORY WITH R PRACTICE.
Students learn not only the statistical theory, but also how to apply it in R, read the output and explain the result in proper academic language.

► I HELP STUDENTS CHOOSE THE RIGHT METHOD.
Many students struggle with deciding whether to use a t-test, chi-square test, ANOVA, regression or another method. I teach students how to recognise the correct approach from the question or dataset.

► I TRAIN INTERPRETATION AND EXAM TECHNIQUE.
Students learn how to structure statistical answers, write clear conclusions, explain p-values, interpret confidence intervals and present results professionally.

► I ADAPT EVERY LESSON TO THE STUDENT.
Some students need help with theory, others with R coding, assignments, research projects or exam preparation. I adjust every lesson to the student’s exact course, level and goals.

► YEARS OF EXPERIENCE WITH STATISTICS, DATA & UNIVERSITY STUDENTS

Over the years, I have successfully supported students from demanding academic programmes, helping them strengthen their statistical understanding, improve their analytical thinking and achieve excellent progress in Statistics, Data Analytics, Machine Learning and AI.

► ONLINE LESSONS

► Interactive whiteboard
► Clear digital notes
► Step-by-step statistical explanations
► R support for data analysis
► Exam preparation
► Assignment and project guidance
► Practical examples with real datasets
► Focused one-to-one support from Switzerland

► MY GOAL

My goal is not only to help students pass exams or complete assignments, but to help them truly understand Statistics. With the right guidance, statistical methods become logical, practical and much easier to apply.

► SUBJECTS: Statistics, Probability, Data Analytics, Data Science, Machine Learning, AI, Quantitative Methods, Research Methods, Econometrics
► MAIN TOOL: R
► LEVELS: International School, IB, A-Level, Foundation Courses, University Modules, Professional Training
► FORMAT: Online tutoring from Switzerland
► FOCUS: Statistical understanding, R practice, interpretation, exam preparation, assignments, projects and long-term analytical confidence.
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Session 1: Revolutionizing your Scientific Writing with LaTeX & AI
Duration: 2 Hours | Level: Beginner | Tools: Overleaf + AI**

First Hour: Foundations and Cloud Environment (60 min)

1. Introduction to LaTeX Philosophy (15 min)

- The "WYSIWYM" concept:** Explain the difference between Word (*What You See Is What You Get*) and LaTeX (*What You See Is What You Mean*). Why content takes precedence over form.
- Key advantages:** Unrivaled typographic quality, automatic reference management, stability on long documents (theses), and free of charge.
- The structure of a file:** Distinction between the **preamble** (the brain: settings and packages) and the **body of the document** (the heart: text).

2. Immersion in Overleaf (25 min)

- Configuration:** Creation of an account and first project "Blank Project".
- Exploring the interface:** The file panel (left), the code editor (middle) and the PDF preview (right).
- Real-time collaboration:** How to share a project and leave comments (like on Google Docs).
- History and versions:** How to revert to a previous version in case of a compilation error.

3. Practical Workshop: My First Document (20 min)

* Writing basic commands: `\documentclass`, `\usepackage[french]{babel}`, `\title`, `\author`.
* Compilation of the document and observation of the result.
* Structuring: Use of `\section` and `\subsection`.

Second Hour: Mathematics and the Magic of AI (60 min)

4. The Power of Mathematics (20 min)

- Mathematical modes:** Difference between the text (`$...$`) and the centered block (`\[...\]`).
- Essential syntax:** Fractions `\frac{}{}`, exponents `^`, indices `_`, and roots `\sqrt{}`.
- Introduction to AMS packages: Why amsmath and amssymb are essential for professional rendering.

5. From hand to screen: AI at the service of LaTeX (30 min)

- Presentation of OCR tools:** Use of **Mathpix Snip** (the leader) or models like Gemini/ChatGPT to transform a photo into code.
- Concrete demonstration:
1. Take a picture of a complex handwritten formula (e.g., an integral with matrices).
2. Use AI to generate the corresponding LaTeX code.
3. Correction and insertion: Learn to check the AI-generated code before copying and pasting it into Overleaf.

6. Conclusion and Q&A (10 min)

* Summary of achievements.
* Resources for further exploration
* Definition of the exercise for the next session.
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With a doctorate in mathematics and experience as a university lecturer, I have dedicated several years to teaching students in France and internationally. I support students of varying levels, emphasizing a clear, progressive, and comprehension-focused approach.
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1. Algebra and linear algebra
The foundation of success in mathematics. General algebra: groups, rings, fields and arithmetic.
Linear algebra: Vector spaces, reduction of endomorphisms (diagonalization, triangularization), duality and quadratic forms.
2. Topology and Functional Analysis
Metric spaces and topology: open and closed sets, compactness, convexity.
Normed vector spaces: convergence of sequences, continuity and equivalence of norms.
Banach & Hilbert spaces: study of completeness, orthogonal projections, Riesz theorems and Fourier series.
3. Numerical Analysis: The art of solving mathematical problems using algorithms and high-performance computing.
Solving nonlinear equations: bisection, Newton-Raphson and fixed-point methods.
Numerical integration: rectangle rule, trapezoidal rule and Simpson's rule.
Numerical matrix analysis: solving systems of equations (Gauss, LU, Cholesky), eigenvalue calculations and conditioning.
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