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Full Description
This book is designed as a foundational textbook for upper-level undergraduate and graduate students from non-technical fields who want to acquire a basic understanding of data science and learn practical skills in data analysis. It distinguishes itself by combining theoretical knowledge with practical applications, bridged through extensive Python programming exercises. To accommodate social scientists' needs, the book emphasizes the analysis of textual data, especially those acquired from surveys and social media. For those without prior programming experience, the book provides instruction on using an AI-assisted Python programming tool, following the learn-by-doing methodology of acquiring new skills through experience. The overall learning goal of the book is to develop a conceptual understanding of data mining as well as the technical skills necessary for real-world data analysis.
Contents
Introduction to Data Mining. CRISP-DM Process.- Data Preprocessing.- Introduction to Data Mining Methods. Association Rules.- Decision Trees.- Clustering Techniques: K-means and DBSCAN.- Hierarchical Clustering.- Predictive Analytics and Supervised Learning. Classification.- Validation and Evaluation Methods.- Web Data Scraping.- Sentiment and Emotion Analysis.- Text Mining Essentials.- Topic Modeling: Latent Dirichlet Allocation.- Text Analysis with Large Language Models (LLMs).- Introduction to Social Network Analysis.- Understanding Data Storage and Databases.- Ethics and Explainable AI.
