How can data be transformed into robust solutions for real-world problems? Many people learn Data Science as a collection of technical tools: cleaning data, training models, calculating metrics, and generating predictions. Yet successful Data Science projects rarely fail because of the code. More often, they fail because the underlying problem has not been properly understood. This book presents Data Science as a systematic problem-solving process with Python. At its core is the question of how a problem can be transformed step by step into reliable insights, traceable decisions, and responsible solutions. In this book, you will learn:

• how to structure Data Science projects from problem definition to implementation
• why data quality is often more important than the choice of model
• how to systematically examine, clean, and prepare raw data
• how to develop features and evaluate them critically
• how to use baselines, training data, and model comparisons effectively
• how to interpret metrics in the context of real-world decisions
• how to identify risks, uncertainty, and data leakage at an early stage
• how to document, version, and monitor models in production
• how to communicate results clearly and translate them into actionable recommendations
• how to use Python, pandas, Scikit-Learn, and MLOps tools within a traceable development process

• want to learn Data Science systematically and practically
• want to analyze data with Python and develop Machine Learning models
• want to understand how successful Data Science projects actually work
• want not only to train models, but also to evaluate and use them responsibly
• want to document and communicate data, methods, and decisions in a traceable way