Data Science overview

Data Science applies statistical analysis, machine learning, and domain expertise to extract insight and predictive models from data. It covers exploratory data analysis (pandas, R, Jupyter), feature engineering, classical ML (scikit-learn, XGBoost), deep learning (PyTorch, TensorFlow), experiment tracking, and communication of findings through visualizations and reports. Data Science is upstream of ML-Ops (productionizing models) and downstream of Data Engineering (building the pipelines that supply clean data). Python and R are the dominant languages; SQL is essential for data access.