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Python is a Programming Language widely used with huge capabilities on building projects of many different kinds but the most important advantage is that it is easy to learn and it can be a good start for someone to embrace Computer Science and improve their logical thinking.

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MsC in Engineering with top marks and research assistant of Econometrics for Italian top University. Business Expert in Risk Management. Academic Research in Quantitative Finance and Algorithmic Trading. Common discipline covered: Econometrics (with applications in R, Stata, SPSS, Eviews, Gretl), Statistics, Financial Mathematics, Quantitative Support for Master Degree Thesis (from Regressions to all statistical applications), Risk Management, Mathematics, Computer Science I help with assignments, exams, presentations, advanced research, dissertations, big programming projects and general skill enhancement. Proficient in all major statistical packages: R, SPSS, Stata, Matlab, EViews, Gretl Technical Skills (application and often implementation from scratch): 1) Econometrics: Multivariate Regression, Discrete variable models (i.e. Logit), Time series models (i.e. AR/MA, ARCH/GARCH), Vector AutoRegressive model (VAR), Cointegration (Engle-Granger, VECM), Long-memory process (Fractional Integration), Regime switching models (Hamilton Filter), Kalman Filter, Unobserved Components ARIMA model, Beveridge-Nelson decomposition (Hansen's approach), Copula methods, Metropolis-Hastings algorithm, Black-Litterman model (Meucci's approach), Hierarchical Risk Parity 2) Quantitative Trading (Mid-High Frequency Trading): Stat Arb & Pairs Trading models, Order Imbalance & Order Replenishment effects on intraday returns, Optimal Setup of Entry-Exit Trading Triggers for Quant Trading Strategies, Stat Arb Bertram Model, Data sampling rules for non equally-spaced data (time vs. volume clock for high freq data), Bid-Ask Bounce Bias & Sahalia Method for Microstructure Noise Estimation & Test, Hayashi-Yoshida Lead-Lag Index, D'Aspremont Method for Mean Rev Portfolios, Market Fragmentation in Financial Markets, High-Low prices & Pivot Points trading rule, Trend Following Strategy, Avellaneda-Stoikov Model for Optimal Trading Execution 3) Risk Management: P&L production & analysis for energy trading, VaR & Profit at Risk for energy trading, Merton approach for Credit VaR with/without credit rating migrations, EVT & Copula-based VaR, Stress Test models, Structured Credit Models for Regulatory Risk-Transfer, Additional Value Adjustments for Balance Sheet, Risk Aggregation, Model Risk, Interpolation Methods for multi-year PD Term Structure, Methods for Semidefinite-Positive Corr Matrix Adjustment 4) Financial Mathematics: Longstaff-Schwartz, HJM model (Glasserman's scheme), Greeks with Finite Difference Method, CPPI Products & Cushion Multiplier Setup 5) Machine Learning: Support Vector Machine, Decision Tree, Principal Component Analysis & Regression, XGBoost, Random Forest

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As a software engineer who works in bank and e-commerce companies in Asia and Europe with teaching experience near 3 years to children and adults, I know the most efficient learning way for a person. Also, how to reach the most relevant job position at the right time.

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19Students

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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6Students

Python is one of the best, if not the best, language to start learning programming. It is also one of the most widely used languages today, especially in cutting-edge areas such as machine learning. This popularity means that Python is constantly evolving. It offers a wide range of tools and libraries, which are free and very varied. As an aeronautical engineer, I like to share my knowledge and derive satisfaction from it by teaching and motivating others. I'm used to working with people of different ages. I believe in the importance of segmenting learning, visualizing progress, setting concrete goals and practicing regularly. Beyond these general principles, there is no magic rule or method. Some approaches work with some students but not with others. Adaptation to individual needs is therefore the main objective of private lessons. So I will do my best to find what motivates and helps my student.

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1Students

I offer one-to-one Programming tuition in Python, C, and C++, for GCSE Computer Science, A-Level Computing, and university students studying engineering, computer science, or related subjects. Lessons are available online or in person around Birmingham. What I cover: Python for beginners and intermediate learners C and C++ programming GCSE and A-Level Computer Science (all exam boards) University coursework support, debugging help, and project guidance Core concepts: variables, loops, functions, data structures, object-oriented programming, file handling, basic algorithms How I teach: I start by understanding exactly where you are — whether that's "I've never coded before" or "I'm stuck on a specific assignment." Then I build lessons around small, practical examples you can actually run and modify yourself. I'm patient with errors (everyone gets them), and I make sure you understand the why behind the code, not just how to copy it. For university students, I can also help with debugging, code reviews, and explaining tricky concepts in plain English. If you or your child is preparing for exams, working on coursework, or just wants to finally feel comfortable with coding, I'd love to help.

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2Students

These courses are part of a structured and progressive training in Object-Oriented Programming (OOP) with JavaScript, designed for beginner or intermediate developers who want to understand in depth how the language works, write clearer, more maintainable code and prepare themselves calmly for modern frameworks like React ⚛️. Object-Oriented Programming is often perceived as complex or abstract. My goal is simple: to make it logical, concrete, and immediately applicable. 🎯 Training Objectives Upon completion of this training, you will be able to: Understanding what Object-Oriented Programming really is (and when to use it) Create and manipulate objects in JavaScript in a clean and efficient way Use ES6 classes, constructors, and methods with confidence Mastering this, the prototype, and the instantiation logic Apply encapsulation, inheritance, and polymorphism without confusion Avoiding common mistakes made by OOP beginners Structure your JavaScript code like a professional developer 📖 Training Plan – Object-Oriented Programming in JavaScript 1. Introduction to Object-Oriented Programming 🧠 Understanding the concept, objectives and benefits of OOP. 2. Procedural Programming vs. OOP Why unstructured code quickly becomes unmanageable. 3. Objects in JavaScript Properties, methods and representation of the real world. 4. The keyword this Understanding the execution context (often poorly understood). 5. Limitations of simple objects Why duplicating code is a bad idea. 6. Constructive functions Create multiple objects from the same model. 7. The keyword new What it's actually doing under the hood. 8. The prototype Sharing methods and memory optimization. 9. ES6 Classes Modern syntax and best practices. 10. The builder Proper initialization of objects. 11. Data Encapsulation Protect the internal state of objects. 12. Inheritance between classes Reusing code intelligently. 13. The keyword super Communication between parent and child in the classroom. 14. Polymorphism The same behavior, several forms. 15. Composition vs. Inheritance Choosing the right architecture. 16. Best practices in OOP Write readable, scalable, and maintainable code. 17. Common mistakes made by beginners Pitfalls to absolutely avoid. 18. Guided practical exercise Creation of a concrete class (product, user, etc.). 19. Assessment Quiz (Multiple Choice Questions) To validate the actual understanding of the concepts. 🛠️ Teaching method: Understand before writing This training program is based on a progressive and pragmatic approach: Clear and illustrated explanations Concrete examples from real projects Simple but effective exercises Constant questioning to avoid rote learning Adaptation to the learner's level and pace Here, we don't "recite OOP" — we understand it. 🚀 Learner's result At the end of the training, you will not only know how to write a JavaScript class. You will know: 1- Why does it exist? 2- When to use it 3- and when not to use it You will leave with: a solid understanding of OOP a cleaner and more professional code an ideal foundation for learning React, Node.js or any other modern framework