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This textbook is dedicated to practitioners, graduate, and advanced undergraduate students who have interest in Data Science, Business analytics, and Statistical and Mathematical Modeling in different disciplines such as Finance, Geophysics, and Engineering. This book is designed to serve as a textbook for several courses in the aforementioned areas and a reference guide for practitioners in the industry.

The book has a strong theoretical background and several applications to specific practical problems. It contains numerous techniques applicable to modern data science and other disciplines. In today's world, many fields are confronted with increasingly large amounts of complex data. Financial, healthcare, and geophysical data sampled with high frequency is no exception. These staggering amounts of data pose special challenges to the world of finance and other disciplines such as healthcare and geophysics, as traditional models and information technology tools can be poorly suited to grapple with their size and complexity. Probabilistic modeling, mathematical modeling, and statistical data analysis attempt to discover order from apparent disorder; this textbook may serve as a guide to various new systematic approaches on how to implement these quantitative activities with complex data sets.

The textbook is split into five distinct parts. In the first part of this book, foundations of Data Science, we will discuss some fundamental mathematical and statistical concepts which form the basis for the study of data science. In the second part of the book, Data Science in Practice, we will present a brief introduction to R and Python programming and how to write algorithms. In addition, various techniques for data preprocessing, validations, and visualizations will be discussed. In the third part, Data Mining and Machine Learning techniques for Complex Data Sets and fourth part of the book, Advanced Models for Big Data Analytics and Complex Data Sets, we will provide exhaustive techniques for analyzing and predicting different types of complex data sets.

We conclude this book with a discussion of ethics in data science: With great power comes great responsibility.

The authors express their deepest gratitude to Wiley for making the publication a reality.

El Paso, TX and Mahwah, NJ, USA

September 2021

Maria Cristina MarianiOsei Kofi TweneboahMaria Pia Beccar‐Varela