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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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4 repeat students
Trusted choice for 4 returning students
R Programming for Data Analysis – From Beginner to Confident Analyst
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From 5.4 C$ /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 -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
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
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.
Read more
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Vincent
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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Raouf
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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Ammar
Artificial Intelligence and programming become much easier when you understand the reasoning behind the algorithms—not simply memorize Python syntax or use AI tools as a black box.

I am a PhD-qualified engineer, university professor, researcher, programmer, and multidisciplinary tutor with more than 30 years of experience across teaching, technical training, engineering, information systems, quantitative analysis, research, data mining, programming, and intelligent knowledge-based systems.

This class provides a structured and personalized pathway for beginners, school and university students, researchers, engineers, professionals, career changers, and adult learners. Depending on your goals, we can focus on Python programming, computational problem solving, automation, machine learning, artificial intelligence, or a coherent progression connecting them.

PYTHON PROGRAMMING & COMPUTATIONAL THINKING
• Python installation and development environments
• Variables, data types, operators, and expressions
• Input, output, and program flow
• Conditional statements and decision making
• For loops and while loops
• Functions, parameters, return values, and scope
• Strings and text processing
• Lists, tuples, sets, and dictionaries
• File handling and data input/output
• Error handling and exceptions
• Modules, packages, and reusable code
• Object-oriented programming
• Algorithms and computational problem solving
• Debugging and systematic error correction
• Code organization, readability, and good programming practices

SCIENTIFIC COMPUTING, DATA & AUTOMATION
• NumPy for numerical computing
• pandas for structured data manipulation
• matplotlib for visualization
• Scientific and engineering calculations
• Automation of repetitive tasks
• Data processing workflows
• Working with files and external data
• Introduction to APIs when relevant
• Python and SQL workflows
• Research and quantitative applications
• Project development from idea to working solution

MACHINE LEARNING
• Foundations of machine learning
• Supervised and unsupervised learning
• Regression and classification
• Clustering and pattern discovery
• Decision trees and rule-based approaches
• Feature selection and data preparation
• Training, validation, and testing
• Model evaluation and performance metrics
• Overfitting and underfitting
• Bias, variance, and generalization
• Model comparison and interpretation
• Predictive modelling and data mining
• Neural-network foundations

ARTIFICIAL INTELLIGENCE & INTELLIGENT SYSTEMS
• Foundations and major branches of Artificial Intelligence
• How intelligent systems represent, classify, predict, and support decisions
• Knowledge representation concepts
• Ontologies and structured knowledge
• Rule-based reasoning and expert-system foundations
• Intelligent decision-support systems
• Generative AI and large language model concepts
• Prompt design and effective AI-assisted workflows
• AI limitations and hallucinations
• Bias, privacy, ethics, and responsible AI
• Applications in engineering, research, business, education, and professional decision making

Depending on your goals, practical work may involve Python, NumPy, pandas, matplotlib, relevant machine-learning libraries, Weka, SPSS Modeler, structured datasets, or modern generative-AI tools.

My teaching approach follows a clear progression:
understand the problem → design the logic → represent and prepare the data → write or select the method → test it → evaluate the output → debug or improve it → interpret the result → apply it responsibly

I do not simply provide finished code, demonstrate isolated commands, or recommend an AI model because it is popular. I help you understand why a method works, what assumptions it makes, when it should be used, how to evaluate its results, where it may fail, and how to improve the solution.

We can work with your course syllabus, programming exercises, existing code, error messages, dataset, research problem, AI project, automation task, engineering application, model output, or professional use case.

Whether you are writing your first Python program, preparing for a university course, debugging a project, automating a professional task, learning machine learning, or exploring advanced AI applications, I will adapt the sessions to your level, objectives, and pace.

My goal is to help you become an independent computational problem solver who can understand, build, evaluate, and apply intelligent solutions—not merely copy code or use AI tools without understanding them.
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Contact Mohamed
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Vincent
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.
verified badge
Raouf
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?
verified badge
Ammar
Artificial Intelligence and programming become much easier when you understand the reasoning behind the algorithms—not simply memorize Python syntax or use AI tools as a black box.

I am a PhD-qualified engineer, university professor, researcher, programmer, and multidisciplinary tutor with more than 30 years of experience across teaching, technical training, engineering, information systems, quantitative analysis, research, data mining, programming, and intelligent knowledge-based systems.

This class provides a structured and personalized pathway for beginners, school and university students, researchers, engineers, professionals, career changers, and adult learners. Depending on your goals, we can focus on Python programming, computational problem solving, automation, machine learning, artificial intelligence, or a coherent progression connecting them.

PYTHON PROGRAMMING & COMPUTATIONAL THINKING
• Python installation and development environments
• Variables, data types, operators, and expressions
• Input, output, and program flow
• Conditional statements and decision making
• For loops and while loops
• Functions, parameters, return values, and scope
• Strings and text processing
• Lists, tuples, sets, and dictionaries
• File handling and data input/output
• Error handling and exceptions
• Modules, packages, and reusable code
• Object-oriented programming
• Algorithms and computational problem solving
• Debugging and systematic error correction
• Code organization, readability, and good programming practices

SCIENTIFIC COMPUTING, DATA & AUTOMATION
• NumPy for numerical computing
• pandas for structured data manipulation
• matplotlib for visualization
• Scientific and engineering calculations
• Automation of repetitive tasks
• Data processing workflows
• Working with files and external data
• Introduction to APIs when relevant
• Python and SQL workflows
• Research and quantitative applications
• Project development from idea to working solution

MACHINE LEARNING
• Foundations of machine learning
• Supervised and unsupervised learning
• Regression and classification
• Clustering and pattern discovery
• Decision trees and rule-based approaches
• Feature selection and data preparation
• Training, validation, and testing
• Model evaluation and performance metrics
• Overfitting and underfitting
• Bias, variance, and generalization
• Model comparison and interpretation
• Predictive modelling and data mining
• Neural-network foundations

ARTIFICIAL INTELLIGENCE & INTELLIGENT SYSTEMS
• Foundations and major branches of Artificial Intelligence
• How intelligent systems represent, classify, predict, and support decisions
• Knowledge representation concepts
• Ontologies and structured knowledge
• Rule-based reasoning and expert-system foundations
• Intelligent decision-support systems
• Generative AI and large language model concepts
• Prompt design and effective AI-assisted workflows
• AI limitations and hallucinations
• Bias, privacy, ethics, and responsible AI
• Applications in engineering, research, business, education, and professional decision making

Depending on your goals, practical work may involve Python, NumPy, pandas, matplotlib, relevant machine-learning libraries, Weka, SPSS Modeler, structured datasets, or modern generative-AI tools.

My teaching approach follows a clear progression:
understand the problem → design the logic → represent and prepare the data → write or select the method → test it → evaluate the output → debug or improve it → interpret the result → apply it responsibly

I do not simply provide finished code, demonstrate isolated commands, or recommend an AI model because it is popular. I help you understand why a method works, what assumptions it makes, when it should be used, how to evaluate its results, where it may fail, and how to improve the solution.

We can work with your course syllabus, programming exercises, existing code, error messages, dataset, research problem, AI project, automation task, engineering application, model output, or professional use case.

Whether you are writing your first Python program, preparing for a university course, debugging a project, automating a professional task, learning machine learning, or exploring advanced AI applications, I will adapt the sessions to your level, objectives, and pace.

My goal is to help you become an independent computational problem solver who can understand, build, evaluate, and apply intelligent solutions—not merely copy code or use AI tools without understanding them.
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Contact Mohamed