This comprehensive and timely methodological book introduces several novel topics under the overarching sections of advanced learning analytics (LA), artificial intelligence (AI), precision education, and complex systems. These topics are presented using accessible language, beginning with introductory chapters that cover the fundamentals of each section, followed by step-by-step tutorials featuring code and datasets for various methods within each area. Although the title refers to "advanced LA," the book is written for the broader educational research community and is of interest to quantitative researchers from diverse backgrounds. The first section focuses on Explainable AI and machine learning (ML), with an introduction to the methods, their applications, and tutorials. The second section outlines the foundational concepts of LLMs, their potential applications, and related methodologies, with a tutorial on using LLMs in various analytical tasks. The third section focuses on complex systems, which have become integral to many disciplines and have enabled breakthroughs in modeling intractable problems. Here, three chapters cover Transition Network Analysis (TNA), which fills a critical gap in modeling the temporal unfolding of learning processes over time from a complex systems perspective. The final section addresses precision education, with a particular emphasis on person-centered and person-specific (idiographic) methodologies.