The increasingly popular field of AI/ML Data Science requires a holistic understanding, spanning everything from core machine algorithms techniques to complex data handling and insight extraction. This guide presents a practical exploration, addressing key principles such as supervised and unsupervised learning, attribute engineering, model selecti