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Microsoft Excel is very powerful data analysis software. It is a practical solution in the short to medium and long term to automate your calculations, to have a global and detailed overview on your activities, and to analyze your data. As an accountant, marketer, commercial agent, secretary, merchant, salesperson or company manager, a good mastery of this software will improve your efficiency, your competitiveness, and will save you a lot of time and money. Whatever your field of activity, this software is designed to help you. During this training you will learn: - best practices, functionalities and tools; - functions and their use; - handling of Dynamic Cross Tables, dynamic graphics, - the design of dashboards, - and you will acquire reflexes that will be useful for your entire career. Duration of training: 1 month Number of hours: 24 hours I am expecting many of you because we have a lot to share.

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Preparation course for the official Probatory Baccalaureate and BEPC exam also intermediate class on all scientific subjects and in computer bonus. Knowledge in outdoor classes is a plus but otherwise a brief summary of the course will be made before starting the lesson

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Digital suites courses I - General A numeric sequence is an application from N to R. • Bounded sequence A sequence (Un) is bounded if there exists a real A such that, for all n, Un ≤ A. We say that A is an upper bound of the series. A sequence (Un) is reduced if there exists a real number B such that, for all n, B ≤ one. One says that B is a lower bound of the sequence. A sequence is said to be bounded if it is both increased and reduced, that is to say if it exists M such that | Un | ≤ M for all n. • Convergent suite The sequence (Un) is convergent towards l ∈ R if: ∀ε> 0 ∃n0 ∈ N ∀n ≥ n0 | un − l | ≤ ε. A sequence which is not convergent is said to be divergent. When it exists, the limit of a sequence is unique. The deletion of a finite number of terms does not modify the nature of the sequence, nor its possible limit. Any convergent sequence is bounded. An unbounded sequence cannot therefore be convergent. • Infinite limits We say that the following (un) diverges Towards + ∞ if: ∀A> 0 ∃n0∈N ∀n ≥ n0 Un≥A Towards −∞ if: ∀A> 0 ∃n0∈N ∀n≤ n0 Un≤A. • Known limitations For k> 1, α> 0, β> 0 II Operations on suites • Algebraic operations If (un) and (vn) converge towards l and l ', then the sequences (un + vn), (λun) and (unvn) respectively converge towards l + l', ll and ll '. If (un) tends to 0 and if (vn) is bounded, then the sequence (unvn) tends to 0. • Order relation If (un) and (vn) are convergent sequences such that we have a ≤ vn for n≥n0, then we have: Attention, no analogous theorem for strict inequalities. • Framing theorem If, from a certain rank, un ≤xn≤ vn and if (un) and (vn) converge towards the same limit l, then the sequence (xn) is convergent towards l. III monotonous suites • Definitions The sequence (un) is increasing if un + 1≥un for all n; decreasing if un + 1≤un for all n; stationary if un + 1 = one for all n. • Convergence Any sequence of increasing and increasing reals converges. Any decreasing and underestimating sequence of reals converges. If a sequence is increasing and not bounded, it diverges towards + ∞. • Adjacent suites The sequences (un) and (vn) are adjacent if: (a) is increasing; (vn) is decreasing; If two sequences are adjacent, they converge and have the same limit. If (un) increasing, (vn) decreasing and un≤vn for all n, then they converge to l1 and l2. It remains to show that l1 = l2 so that they are adjacent. IV Extracted suites • Definition and properties - The sequence (vn) is said to be extracted from the sequence (un) if there exists a map φ of N in N, strictly increasing, such that vn = uφ (n). We also say that (vn) is a subsequence of (un). - If (un) converges to l, any subsequence also converges to l. If sequences extracted from (un) all converge to the same limit l, we can conclude that (un) converges to l if all un is a term of one of the extracted sequences studied. For example, if (u2n) and (u2n + 1) converge to l, then (un) converges to l. • Bolzano-Weierstrass theorem From any bounded sequence of reals, we can extract a convergent subsequence. V Suites de Cauchy • Definition A sequence (un) is Cauchy if, for any positive ε, there exists a natural integer n0 for which, whatever the integers p and q greater than or equal to n0, we have | up − uq | <ε. Be careful, p and q are not related. • Property A sequence of real numbers, or of complexes, converges if, and only if, it is Cauchy SPECIAL SUITES I Arithmetic and geometric sequences • Arithmetic sequences A sequence (un) is arithmetic of reason r if: ∀ n∈N un + 1 = un + r General term: un = u0 + nr. Sum of the first n terms: • Geometric sequences A sequence (un) is geometric of reason q ≠ 0 if: ∀ n∈N un + 1 = qun. General term: un = u0qn Sum of the first n terms: II Recurring suites • Linear recurrent sequences of order 2: - Such a sequence is determined by a relation of the type: (1) ∀ n∈N aUn + 2 + bUn + 1 + cUn = 0 with a ≠ 0 and c ≠ 0 and knowledge of the first two terms u0 and u1. The set of real sequences which satisfy the relation (1) is a vector space of dimension 2. We seek a basis by solving the characteristic equation: ar2 + br + c = 0 (E) - Complex cases a, b, c If ∆ ≠ 0, (E) has two distinct roots r1 and r2. Any sequence satisfying (1) is then like : where K1 and K2 are constants which we then express as a function of u0 and u1. If ∆ = 0, (E) has a double root r0 = (- b) / 2a. Any sequence satisfying (1) is then type: - Case a, b, c real If ∆> 0 or ∆ = 0, the form of the solutions is not modified. If ∆ <0, (E) has two conjugate complex roots r1 = α + iβ and r2 = α − iβ that we write in trigonometric form r1 = ρeiθ and r2 = ρe-iθ Any sequence satisfying (1) is then of the type: • Recurrent sequences un + 1 = f (un) - To study such a sequence, we first determine an interval I containing all the following values. - Possible limit If (un) converges to l and if f is continuous to l, then f (l) = l. - Increasing case f If f is increasing over I, then the sequence (un) is monotonic. The comparison of u0 and u1 makes it possible to know if it is increasing or decreasing. - Decreasing case f If f is decreasing over I, then the sequences (u2n) and (u2n + 1) are monotonic and of contrary Made by LEON

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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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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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I offer one-to-one Machine Learning and AI tuition for university students, postgraduates, working professionals, and serious self-learners. Lessons are available online or in person around Birmingham. What I cover: Python for data science and ML (NumPy, Pandas, Scikit-learn) Deep learning with TensorFlow and Keras Core ML concepts: regression, classification, clustering, neural networks, CNNs Computer vision and image classification (my published research area) University coursework support, dissertation help, project guidance Help with Kaggle competitions and personal portfolio projects How I teach: I focus on understanding, not memorisation. We work through real datasets and real problems — not toy examples — so you can actually apply what you learn. I'll help you build a model from scratch, debug it when it doesn't work, and explain the maths behind why it does or doesn't perform well. For university students, I can also help with assignments, dissertations, and final-year projects. Whether you're just starting out, stuck on a coursework project, or trying to break into ML professionally, I can meet you wherever you are and help you move forward.

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This comprehensive course is designed to take absolute beginners with no prior coding experience to confident Python programmers. Through hands-on projects, real-world examples, and interactive exercises, students will master Python fundamentals and apply them to build applications, automate tasks, analyze data, and even explore game development. What You’ll Learn: 🐍 Python Basics: Syntax, variables, data types, and operators Input/output, loops (for, while), and conditionals (if-else) 📚 Core Programming Concepts: Functions, modules, and error handling Lists, tuples, dictionaries, and sets 🎮 Real-World Applications: File handling (read/write files) Web scraping (intro to BeautifulSoup/Requests) Basic game development (with Pygame) 🔢 Next-Level Skills (Towards "Hero" Level): Object-Oriented Programming (OOP) – Classes & Objects Working with APIs (fetching data from the web Intro to data analysis (Pandas & Matplotlib basics) Mini-capstone project to showcase skills

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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.