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I am a Cambridge graduate in CS, with over 10 medals and national distinctions for competitive programming. For over 5 years, I have helped hundreds of students discover how easy and beautiful Computer Science is: - Assisted students in improving from a C to an A* grade. - Helped motivated students qualify for the National Informatics Olympiad. - Guided many achieve their target grades in A-Levels, even attaining the perfect score. - Prepared for IB/IA, A-Levels, GCSE, University Entry, or equivalent. - Assisted with specific projects at a professional level, including interview preparation. All my success in Computer Science is due to my teachers, who knew how to inspire my passion and turn any concept into an easy-to-understand story. That's why I decided to help others further to elucidate the secrets of Computer Science and to smile with relief in the exam hall when they notice that they know how to solve all the subjects perfectly. It's amazing how quickly a student can progress when the material is explained to them in their understanding. I have a highly flexible schedule and can adapt to accommodate your needs. If you have any questions about my teaching method, availability, or pricing, please don't hesitate to reach out. I am here to assist you and provide the support you need :).

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23Students

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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14Students

Python is the most in-demand programming language in the world right now — and one of the easiest to learn with the right guidance. Whether you've never written a line of code or you're a student who needs to pass a programming course, this is a practical, no-fluff introduction that gets you writing real code from session one. What we can cover depending on your goals: Python fundamentals: variables, loops, functions, data structures - Object-oriented programming (OOP) - Data manipulation with pandas and NumPy - Introduction to machine learning with scikit-learn - Database management with SQL - C and Java upon request - MATLAB and R available for engineering/science students Why learn with me? I'm not a student teaching on the side — I'm a professional engineer who uses Python daily for data analysis, modeling, and automation. I know exactly which concepts matter in the real world and which ones you can skip for now. Sessions are 100% personalized: I adapt the pace, the examples, and the exercises to your background and your goal — whether that's passing your university exam, building a project, or landing a job.

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10Students

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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6Students

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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2Students

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?