Studying Data Science

A practical guide to data analysis, revealing the full cycle of working with information: from collection and processing to building machine learning models. It covers working with pandas and SQL tools, methods for identifying patterns and cleaning data. Various sources of information are described, including textual, binary, and web data. Statistical methods are detailed: confidence intervals, hypothesis testing, feature engineering. Practical examples in Python from different fields are provided: analysis of transportation systems, environmental research, veterinary analytics. The concluding sections are dedicated to logistic regression and model optimization applied to text classification tasks.