DATA SCIENCE

This guide provides a comprehensive and practical introduction to Data Science , emphasizing a deep, conceptual understanding of core algorithms for building reliable and interpretable predictive models. The book focuses heavily on Statistical Learning and foundational Machine Learning techniques, particularly the family of tree-based approaches like the Decision Tree and Random Forest It covers the entire data science workflow, including Statistical Learning, Python for Data Science (NumPy, Pandas, Matplotlib) , Exploratory Data Analysis (EDA) , Statistical Experiments and Significance Testing (t-tests, ANOVA) , Regression Techniques (Linear Regression) , and Classification (Logistic Regression, Decision Trees, Random Forests). The author stresses the importance of model transparency and addressing the bias-variance trade-off.