Practical Data Mining with AI for Social Scientists

Andrei Kirilenko has a degree in applied mathematics and a Ph.D. in computer science. He started his career developing computer models for evaluating climate change impacts on the natural environment and agriculture. Gradually, his teaching and research interests have shifted toward quantitative social science research. Since 2015, he has held the position of Associate Professor at the Department of Tourism, Hospitality, and Event Management at the University of Florida, USA, teaching courses on data mining for social sciences, Geographical Information Systems, statistics, and research methods. His research is centered on social media analysis, sustainability, and biodiversity. Dr. Kirilenko has served as an expert in the global assessments of the Intergovernmental Panel on Climate Change (IPCC) and the United Nations Environmental Program (UNEP) Global Environmental Assessments.

• 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.