📊 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.

My lessons are designed to take you from simply following code to genuinely understanding how data science works. We can cover the complete data science process, including data cleaning, exploratory data analysis, feature engineering, visualisation, statistics, machine learning, model evaluation and communicating results. Depending on your goals, lessons can include: Python, pandas, NumPy and scikit-learn Data cleaning and exploratory analysis Regression and classification Decision trees, random forests and boosting Clustering and dimensionality reduction Cross-validation and model evaluation Feature engineering and model interpretation Neural networks and deep learning foundations Bayesian modelling and PyMC Portfolio and interview preparation Support understanding university modules and projects I use diagrams, analogies and practical demonstrations to make difficult ideas easier to understand. We will normally begin with an intuitive explanation, look at the underlying logic or mathematics, and then implement the concept in Python. Lessons are personalised around your level. Complete beginners receive a structured learning path, while experienced students can focus on advanced topics, project guidance, debugging or interview preparation. You will be encouraged to explain ideas back to me, interpret results and make your own modelling decisions. My goal is not only to help you produce working code, but to help you become an independent and confident data scientist.

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.