Session 1 – Data Science Foundations | Quantum Machine Learning Track | Quantum AI Lab скачать в хорошем качестве

Session 1 – Data Science Foundations | Quantum Machine Learning Track | Quantum AI Lab

This track is part of the Quantum AI Lab organized by iQafé and Bibliotheca Alexandrina in collaboration with the Egypt Quantum Computing Community. Session 1 – Data Science Foundations In this session, we cover the essential mindset and tools every data scientist needs. You’ll learn: •⁠ ⁠What data really represents (observations, not just tables) •⁠ ⁠Types of data (nominal, ordinal, numerical, discrete, continuous) •⁠ ⁠Why EDA matters and how to handle missing values, duplicates, outliers, and noise •⁠ ⁠Core statistical intuition (mean, median, variance, skewness) •⁠ ⁠Visualization basics (histogram, boxplot, scatterplot, pairplot) •⁠ ⁠Feature engineering and scaling techniques •⁠ ⁠Introduction to Principal Component Analysis (PCA) for dimensionality reduction Hands-on includes the Iris dataset. A complete foundation to think clearly, analyze correctly, and model effectively. Track Coordinator: Eng. Abdelrahman Elsayed Quantum Software Engineer Intern Brightskies Director of Education Egypt Quantum Computing Community (EgQCC) MSc Student @Alexandria University Classical Machine Learning Instructor: Eng. Sama Samer AI Engineer | Machine Learning & Data-Driven Solutions Host: Eng. Menna Abd-ElGawad AI/ML Engineer at Fusion Minds AI MSc Student @AAST LinkedIn links: iQafé:   / iqafe   EgQCC:   / egypt-quantum-computing-community