This introductory chapter explores the role of data mining within the emerging field of digital social science, where traditional disciplines like sociology integrate with big data and machine learning to analyze complex social phenomena. Data mining bridges raw data and actionable knowledge, enabling informed decision-making by utilizing large and diverse data sources. The chapter highlights the Cross-Industry Standard Process for Data Mining (CRISP-DM), a structured, iterative methodology comprising six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The practical part of the chapter introduces the Anaconda Cloud platform, a versatile repository and collaboration hub for data science and machine learning projects. A practical exercise guides readers to use an AI assistant to develop Python code that analyzes factors influencing survival in the Titanic disaster. This hands-on approach reinforces the application of data mining concepts in real-world scenarios.