Digital library
Textbooks, references and open learning resources.
External
An Introduction to Matlab and Mathcad
Troy Siemers · Virginia Military Institute
In the contents: 10: Mathcad Introduction · 11: Mathcad Entering Equations · 13: Mathcad Functions
External
Mathematics for Computer Science
Eric Lehman, F. Thomson Leighton, & Alberty R. Meyer · Google and Massachusetts Institute of Technology
In the contents: 04: Mathematical Data Types
External
Math, Numerics, and Programming (for Mechanical Engineers)
Masayuki Yano, James Douglass Penn, George Konidaris, & Anthony T Patera · Massachusetts Institute of Technology
This text introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches.
External
Discrete Mathematics for Computer Science
Jin He · Fresno City College
In the contents: 04: Mathematical Induction · 4.01: Introduction to Mathematical Induction · 4.02: Proof by Using Mathematical Induction
External
Discrete Mathematics for Computer Science
Mark A. Fitch · University of Alaska Anchorage
In the contents: 2.04: Mathematical Proof · 4.02: Mathematical Relations
External
Discrete Math with SageMath: Learn math with open-source software
Zunaid Ahmed; Hellen Colman; Samuel Lubliner · Zunaid Ahmed, Hellen Colman, Samuel Lubliner · 2025
Our book is intended to provide concise and complete instructions on how to use Sage to solve problems in Discrete Math. It is appropriate for a first or second year undergraduate course for math and computer science majors. Our goal is to streamline the learning process with SageMath. This approach helps students focus more on mathematics and reduces the friction of learning how to code. Our resources are designed for all math students, regardless of programming experience. The novelty of our project is that the textbook is authored by students under faculty supervision. With Open Pedagogy as a guiding praxis, we present this student-created textbook as a more effective alternative in promoting student learning and engagement. Students present the contents in new and inventive ways, from the overall voice of the textbook to the actual examples drawn from their and their peers' lives. The text began as a support textbook for our undergraduate Discrete Math course developed at Wright College, one of the City Colleges of Chicago. Despite the fabulous capabilities of SageMath, students in Discrete Math were often frustrated by the lack of specific documentation geared towards beginning undergrad students in Discrete Math. We developed an OER for our students to use this mathematical software effectively.
External
Discrete Mathematics for Information Technology
Simon Crothers · Kwantlen Polytechnic University · 2026
Discrete Mathematics for Information Technology is an applied, student-focused introduction to the mathematical foundations that underpin modern computing. Designed for learners in information technology and business contexts, this textbook bridges theory and practice by connecting core concepts, such as number systems, data representation, logic, proofs, relations, algorithms, Boolean algebra, and graph theory, to real-world applications in programming, networking, databases, and systems design. Each chapter is structured with clear explanations, worked examples, case studies, and programming-oriented exercises that reinforce both conceptual understanding and practical skills. Emphasis is placed on developing analytical thinking, problem-solving, and the ability to model and reason about digital systems with precision and confidence. As an open educational resource, the text is adaptable and accessible, supporting a wide range of teaching and learning environments. By integrating mathematical rigor with authentic IT scenarios, this book equips students with the essential tools needed to succeed in modern, data-driven and technology-focused careers.
External
An Introduction to Python Jupyter Notebooks for College Math Teachers
Paul Isihara; Claire Wagner; Peter Jantsch; Thomas VanDrunen · Paul Isihara, Claire Wagner, Peter Jantsch, and Thomas VanDrunen · 2024
This book is an introduction to the use of Python Jupyter Notebooks (JNBs) for college math teachers and their students. The book is an evolving work in progress, but we hope this 1st Edition may already be quite useful to a college math teacher who is interested to use Jupyter Notebooks in their courses. Each section of the book has been written in the form of a Jupyter Notebook and can be downloaded using the download button at the top menu bar. In some cases, additional data files or image files may need to be downloaded as well. All the files are available in the src folder of the github repository https://github.com/timothyprojectGiG/JB_Math_Textbook Each chapter contains a JNB lab with solutions. Experienced teachers can modify these labs and create new labs tailored to their courses. The chapters were written by different authors/authorship teams, and as such, vary in style. Each chapter essentially can be read independently of the others as long as one has the pre-requisite mathematical knowledge. Some effort has been made to standardize the chapter formats, and the index may help the reader find specific topics of interest. There is virtually no limit to what can be done with JNBs, and we hope this work serves as a useful starting point for teachers and students to enrich and enliven the study of mathematics using this tool. The JNBs used to create this book contain special markdown code which are needed to produce features such as indexing, referencing, and highlight boxes in the Jupyter Book. Some editing of a downloaded JNB may may make it more suitable for classroom use.
External
Mathematical Modeling and Simulation with MATLAB
Sheldon Lee; Megan Buzby · University of Alaska Southeast · 2021
This textbook attempts to provide you with an overview of the commonly used basic mathematical models, as well as a wide range of applications. It offers a perspective that brings you back to the modeling process and the assumptions that go into it.
External
An Introduction to Matlab and Mathcad
Troy Siemers · APEX Calculus · 2011
This textbook, or really a “coursebook” for a college freshman-level class, has been updated for Spring 2014 and provides an introduction to programming and problem solving using both Matlab and Mathcad. We provide a balanced selection of introductory exercises and real-world problems (i.e. no “contrived” problems). We include many examples and screenshots to guide the reader. We assume no prior knowledge of Matlab or Mathcad.
External
Open Data Structures - An Introduction
Pat Morin · Carleton University
In the contents: 1.03: Mathematical Background
External
MAT 1160: Data & Chance
Andy D. Jones · Prince George's Community College
This textbook supports a semester‑long statistics and probability course for future elementary and middle school teachers. It includes ideas from general education statistics but doesn't emphasize inferential statistics. It focuses on concepts essential for teaching math in the lower grades and covers topics beyond the primary curriculum, including interpreting assessment measures, understanding statistics used in professional practice, and developing informed perspectives about data and chance.
External
Foundations of Computation
Carol Critchlow & David J. Eck · Hobart and William Smith Colleges
In the contents: 1.08: Mathematical Induction
External
Data Analysis in the Psychological Sciences: A Practical, Applied, Multimedia Approach
Leyre Castro; J. Toby Mordkoff · University of Iowa Libraries · 2023
This open resources textbook contains 10 Units that describe and explain the main concepts in statistical analysis of psychological data. In addition to conceptual descriptions and explanations of the basic analyses for descriptive statistics, this textbook also explains how to conduct those analyses with common statistical software (Excel) and open-source free software (R).
External
Linear Regression Using R: An Introduction to Data Modeling
David Lilja · University of Minnesota Libraries Publishing · 2016
Linear Regression Using R: An Introduction to Data Modeling presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models. Key modeling and programming concepts are intuitively described using the R programming language. All of the necessary resources are freely available online.
External
Process Improvement using Data
Kevin Dunn · Kevin Dunn · 2026
There is no other free, coherent text that covers what engineers and scientists actually do with process data (visualization, regression, designed experiments, process monitoring, and multivariate / latent-variable methods) in one volume. Most textbooks pick one of those topics and go deep. Practitioners need all of them, and need to see how they fit together, because real industrial problems don't respect chapter boundaries. Process Improvement using Data was written to fill that gap, and has been continuously refined in industry-facing classrooms and in industrial practice since 2010. It is suitable for upper-undergraduate or introductory-graduate courses, and for self-study by working engineers and data scientists with a basic statistics background.
External
Basic Engineering Data Collection and Analysis
Steve Vardeman; J. Marcus Jobe · Iowa State University · 2001
In Basic Engineering Data Collection and Analysis, Stephen B. Vardeman and J. Marcus Jobe stress the practical over the theoretical. Step by step, students get real engineering data and scenario examples along with chapter-long case studies that illustrate concepts in realistic, thoroughly detailed situations. This approach encourages students to work through the material by carrying out data collection and analysis projects from problem formulation through the preparation of professional technical reports—just as if they were on the job.
External
The Crystal Ball Instruction Manual
Stephen Davies · University of Mary Washington · 2020
Welcome to Volume Two of the Crystal Ball series: Foundations for Data Science! I titled the first volume “Introduction to Data Science” because it led you through a dip-your-toes-in-the-water experience. You took a brief tour through the various elements in this diverse field and got a feel for what it was all about. Since you’re still reading, this means you’re still interested, and ready to explore the next level. In Foundations, we’ll solidify your growing knowledge so that you have a firm base on which to build everything else. Future volumes in this series will cover applications, advanced techniques, and some special data types that require special treatment. But it all ultimately rests on the skill set you’ll have built in Volumes One and Two of this series.
External
Mastering Enterprise Networks
Mathew Heath Van Horn · Embry-Riddle Aeronautical University · 2024
Mastering Enterprise Networks, is a comprehensive guide to building, defending, and attacking enterprise networks. It covers a wide range of topics, from network fundamentals to advanced security concepts. The book is well-organized and easy to follow, making it a valuable resource for both beginners and experienced network professionals. One of the strengths of this book is its focus on hands-on learning. The book includes 50 chapters of labs that allow readers to practice the concepts they have learned. These labs are a great way to reinforce your understanding of enterprise networks while developing practical skills.
External
Prealgebra via Python Programming: First Steps to Perform Large Scale Computational Tasks in the Sciences and Engineerings
Sergio Rojas · Sergio Rojas · 2018
This book was written for students and instructors who want to learn how to use a computer for other than the most common uses, such as web browsing, document creation, or paying bills online. This book is for anyone who wants to perform computational tasks that they design. In other words, if you wish to learn how to program a computer, this book is for you. Because prealgebra is a subject that practically everyone is supposed to learn in grade school, it provides a platform to introduce basic computer programming concepts. Consequently, this book should also be of interest to students in middle or high school who want to learn how to program, and who are willing to invest the time and effort in learning a programming language that they could continue using throughout their schooling and in their professional life. Similarly, this book could also be of interest to pre-service and in-service mathematics teachers wishing to have at their disposal a complementary tool to assist in fostering understanding, competency, and interest in mathematics among their students. This book can be integrated with the teachers’ curriculum as way to tackle non-traditional math problems using an inexpensive modern computer language. By the end of the book, a reader will have learned enough to be able to write a preliminary, step-by-step one variable equation solver that can be expanded in the future to use with more complex equations. In other words, by the end of the book, you will be able to write code that programs their machines to solve equations. This code is foundational and readers are ecouraged to learn on their own how to build on it to suit their mathematics learning needs.