Applied Financial & Business Analytics

Applied Financial and Business Analytics is a comprehensive guide to modern financial and business decision-making in an environment increasingly shaped by artificial intelligence, data technologies, and automation. Rather than focusing on isolated software tools, the book develops the broader professional capability to define the right problem, evaluate data critically, select appropriate methods, interpret results responsibly, and translate evidence into effective business decisions.

The book integrates financial reasoning, quantitative analysis, data technology, and managerial decision-making into a unified framework. Its coverage includes Excel-based financial analysis and modeling, descriptive statistics, probability distributions, statistical inference, regression analysis, time-series forecasting, SQL, data mining, data visualization, risk analysis, Monte Carlo simulation, optimization, linear programming, artificial intelligence, business intelligence, big data, Python programming, machine learning, and R.

Its value lies not simply in the breadth of topics, but in the way they are connected through a complete analytical workflow. Readers learn how to frame business problems, prepare and evaluate data, build and test models, interpret evidence, assess risk, communicate findings, and convert analysis into practical action. Learning objectives, worked examples, financial and business cases, skills assessments, exercises, and capstone projects help bridge the gap between academic study and professional practice.

The interdisciplinary strength of the book is reinforced by the backgrounds of its three authors. Peter Lou, CFA, brings more than 25 years of Wall Street fintech experience, including senior roles at Wells Fargo, Bank of Tokyo-Mitsubishi UFJ, and Ernst & Young, together with extensive work in artificial intelligence, data analytics, and executive education.

John Jiang, Ph.D., contributes experience spanning high-energy and particle physics, artificial intelligence, enterprise technology architecture, and large-scale digital transformation. He has worked as a senior researcher at a U.S. Department of Energy national laboratory and has held major technology leadership positions at Lockheed Martin, Hitachi America, and IBM.

Christine Lou brings the perspective of modern software engineering and data science. A computer science graduate with honors from the University of California, Berkeley, she is a senior software engineer at a Fortune 500 company headquartered in Silicon Valley and has teaching experience in Python, data science, and computer science.

Together, the authors combine expertise in finance, scientific research, artificial intelligence, enterprise architecture, software engineering, and higher education. This gives the book a rare balance of academic rigor, technical breadth, and practical relevance.

The book also emphasizes a central principle of responsible analytics: models do not replace judgment. High-quality analysis requires attention to data quality, assumptions, transparency, privacy, explainability, fairness, governance, and professional accountability.

Designed for undergraduate and graduate students, instructors, financial professionals, business analysts, managers, and independent learners, Applied Financial and Business Analytics provides a structured pathway from foundational quantitative analysis to AI, machine learning, and modern business intelligence.

Ultimately, the book is designed to help readers move from data to insight, from insight to decision, and from decision to measurable business impact.