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Foundations of MATLAB Programming for Behavioral Sciences
With Applications
, Northwestern University, Illinois
Coming soon
Online ISBN: 9781009350303
Expected online publication date:
October 2025
- Textbook
This textbook introduces the fundamentals of MATLAB for behavioral sciences in a concise and accessible way. Written for those with or without computer programming experience, it works progressively from fundamentals to applied topics, culminating in in-depth projects. Part I covers programming basics, ensuring a firm foundation of knowledge moving forward. Difficult topics, such as data structures and program flow, are then explained with examples from the behavioral sciences. Part II introduces projects for students to apply their learning directly to real-world problems in computational modelling, data analysis, and experiment design, with an exploration of Psychtoolbox. Accompanied by online code and datasets, extension materials, and additional projects, with test banks, lecture slides, and a manual for instructors, this textbook represents a complete toolbox for both students and instructors.
This textbook introduces the fundamentals of MATLAB for behavioral sciences in a concise and accessible way. Written for those with or without computer programming experience, it works progressively from fundamentals to applied topics, culminating in in-depth projects. Part I covers programming basics, ensuring a firm foundation of knowledge moving forward. Difficult topics, such as data structures and program flow, are then explained with examples from the behavioral sciences. Part II introduces projects for students to apply their learning directly to real-world problems in computational modelling, data analysis, and experiment design, with an exploration of Psychtoolbox. Accompanied by online code and datasets, extension materials, and additional projects, with test banks, lecture slides, and a manual for instructors, this textbook represents a complete toolbox for both students and instructors.
Mathematical Methods in Data Science
Bridging Theory and Applications with Python
, University of Wisconsin, Madison
Coming soon
Online ISBN: 9781009509435
Expected online publication date:
October 2025
- Textbook
Bridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science. It covers essential topics in linear algebra, calculus and optimization, and probability and statistics, demonstrating their relevance in the context of data analysis. Key application topics include clustering, regression, classification, dimensionality reduction, network analysis, and neural networks. What sets this text apart is its focus on hands-on learning. Each chapter combines mathematical insights with practical examples, using Python to implement algorithms and solve problems. Self-assessment quizzes, warm-up exercises and theoretical problems foster both mathematical understanding and computational skills. Designed for advanced undergraduate students and beginning graduate students, this textbook serves as both an invitation to data science for mathematics majors and as a deeper excursion into mathematics for data science students.
Bridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science. It covers essential topics in linear algebra, calculus and optimization, and probability and statistics, demonstrating their relevance in the context of data analysis. Key application topics include clustering, regression, classification, dimensionality reduction, network analysis, and neural networks. What sets this text apart is its focus on hands-on learning. Each chapter combines mathematical insights with practical examples, using Python to implement algorithms and solve problems. Self-assessment quizzes, warm-up exercises and theoretical problems foster both mathematical understanding and computational skills. Designed for advanced undergraduate students and beginning graduate students, this textbook serves as both an invitation to data science for mathematics majors and as a deeper excursion into mathematics for data science students.
Linear Algebra for Data Science, Machine Learning, and Signal Processing
, University of Michigan, Ann Arbor, , University of Michigan, Ann Arbor
Online ISBN: 9781009418164
Online publication date:
01 November 2024
Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization. Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems. Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors). It encourages active learning with engaging 'explore' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice. A suite of computational notebooks offers a hands-on learning experience for students. This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics.
Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization. Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems. Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors). It encourages active learning with engaging 'explore' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice. A suite of computational notebooks offers a hands-on learning experience for students. This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics.
Large-Scale Data Analytics with Python and Spark
A Hands-on Guide to Implementing Machine Learning Solutions
, University of Nottingham, , Public University of Navarre
Online ISBN: 9781009318242
Online publication date:
15 December 2023
Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments.
Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments.
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