📊 Introduction to Data Science with Python Full Title: Statistics, Data Visualization, and Machine Learning for Beginners (100% online course – for students, professionals in retraining, or curious data enthusiasts) Data science is now at the heart of the most innovative professions and strategic decisions in all sectors. However, when you're just starting out, you can quickly feel overwhelmed by technical jargon, Python libraries, or statistical models. With this course, my goal is to make this exciting discipline understandable and accessible to everyone, even without advanced mathematical training or computer science background. I offer step-by-step support based on practical experience, concrete projects, and a supportive teaching approach. You'll learn how to manipulate data, extract information from it, and create your first machine learning models with ease. 🎯 Course objectives Discover the basic tools of Data Science with Python Understand and apply the fundamental concepts of exploratory statistics Know how to manipulate, clean, visualize and interpret real data sets Carry out initial predictive modeling (linear regression, classification) 📚 Course content ✔ Fundamental libraries in Data Science – pandas: reading, cleaning and transforming data – numpy: mathematical operations and array manipulation – matplotlib & seaborn: clear and aesthetic data visualization – Getting Started with Scikit-Learn for Machine Learning ✔ Data cleaning and analysis – CSV file import and data mining – Management of missing values and duplicates – Creation of variables, filtering, groupings – Visualization: histograms, curves, heatmaps, boxplots... ✔ Introduction to Machine Learning – Understand how linear and logistic regression work – First classification models (KNN, simple decision trees) – Data separation (training/test set), single cross-validation – Interpretation of results and improvement of the model 🧭 How the sessions work 1️⃣ Assessment of the student's objectives: discovery, professional project, preparation for training, etc. 2️⃣ Personalized progression plan, adapted to the starting level. 3️⃣ Alternation of visual theory and intensive practice on real data sets (health, sports, finance, etc.). 4️⃣ Practical mini-projects at each stage: analyzing survey results, predicting simple results, automating analyses. 5️⃣ Explanation of errors encountered, individualized educational monitoring. 6️⃣ Regular assessment, with reinforcement of key points as needed. 🌐 100% online courses – accessible teaching methods Classes via Zoom, Google Meet, or the tool of your choice Live screen sharing, work on interactive notebook (Jupyter or Google Colab) PDF supports + commented code provided after each session Possibility of intensive coaching for training or an interview Flexible hours, adapted to the time zone of the Gulf countries and your availability 👨‍🎓 For whom? Complete beginners in Data Science and Python Students wishing to enrich their profile with practical skills Professionals retraining for data professions Anyone curious about understanding the world through data! This course has been designed so that each participant can progress at their own pace, develop their analytical logic and discover the pleasure of "making the data speak". Feel free to contact me to discuss your goals and build a customized program together. I would be delighted to accompany you on this wonderful adventure that is data science.

This lesson builds a solid foundation in numbers and variables by helping students understand how numbers are represented, how variables are used to express unknown values, and how they relate to real-world situations. Through clear explanations, guided examples, and practice activities, students develop algebraic thinking, logical reasoning, and confidence needed for advanced mathematics.

Are you an engineering or mathematics (Bachelor's/Master's) student who finds numerical analysis too abstract or difficult to apply? This course provides a rigorous yet practical introduction to scientific computing, showing how numerical methods are used to solve real engineering and scientific problems. You'll first learn how numerical computations behave by studying floating-point arithmetic, rounding and truncation errors, conditioning, stability, and error propagation. These concepts are essential for understanding why numerical algorithms succeed—or fail. Next, you'll learn how to solve nonlinear equations using the bisection method, fixed-point iteration, and Newton's method, before moving on to linear systems through both direct methods (Gaussian elimination, LU decomposition, Cholesky factorization) and iterative methods (Jacobi, Gauss-Seidel, and relaxation methods). The second half of the course covers polynomial interpolation and approximation (Lagrange, Newton, Hermite, least-squares approximation, and splines), numerical differentiation and integration, and numerical methods for ordinary differential equations, including Euler's method, Runge-Kutta methods, and finite difference techniques. Every algorithm is implemented step by step in Matlab so that you not only understand the mathematics behind it but also learn how to apply it to practical engineering problems. Throughout the course, we compare the accuracy, convergence, stability, and computational efficiency of the different numerical methods. This course is ideal for students preparing for university exams, engineering projects, scientific computing courses, or anyone wishing to build solid foundations in numerical analysis. Prerequisites: A good understanding of single-variable calculus, basic linear algebra, and elementary programming concepts is recommended.

The lesson is tailored to your wishes. I can explain the theory from the *Getal & Ruimte* method (or *Moderne Mathematics*). We can go through the exercises you want to review, or simply the exercises in the book. We can review tests or prepare for exams. Together, we will strive for mathematics that you understand.

- Python Programming is currently the most utilized tool in the data analysis world. Its ease-of-use and wide variety of packages/libraries makes it a most valuable skill to have for a student wanting to get started in the world of data analysis and Object Oriented Programming. - This course is geared towards beginners and intermediates who are interested in a hands-on approach to learning without neglecting the scientific background of the covered material, which will include the following: * Introduction to Python Programming * Understanding of OOP concepts and use-cases * Crash course in statistical analysis concepts * The usage of specified analysis libraries like Pandas and Numpy * Introduction to Data science * Neural Networks

Learn Excel in a practical way, focused on real-world work situations. This isn't a theoretical course: from the very first class, you'll work with applied exercises that will help you gain confidence and improve your professional profile. It is aimed at people who want to start from scratch or improve their level of using Excel in their work, studies or personal projects. In classes you will learn to: Master Excel from basic to intermediate/advanced level Use key formulas such as SUM, IF, VLOOKUP/XLOOKUP Create pivot tables to analyze information Clean and organize data efficiently Design clear reports and basic dashboards Applying Excel to real-world business cases The classes are personalized according to your level and goals. We can work with your own files or with practical exercises that simulate real-life situations. My approach is for you to learn quickly, understand what you're doing, and be able to apply it immediately. I'm an Electronics Engineer with experience in data analysis and marketing analytics. I've worked with tools like Excel, Power BI, and RapidMiner on real-world projects, so I can teach you not only "how" but also "why" to use Excel in the workplace.