Digital library
Textbooks, references and open learning resources.
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Introduction to Computer Science
OpenStax · LibreTexts
In the contents: 01: Introduction to Computer Science · 1.01: Computer Science · 1.02: Computer Science across the Disciplines
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Think Complexity: Exploring Complexity Science with Python
Allen B. Downey · Olin College
In the contents: 01: Complexity Science · 1.01: The changing criteria of science
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Mathematics for Computer Science
Eric Lehman, F. Thomson Leighton, & Alberty R. Meyer · Google and Massachusetts Institute of Technology
In the contents: 6.05: Induction in Computer Science
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Principles of Data Science
OpenStax · LibreTexts
In the contents: 01: What Are Data and Data Science · 1.01: What Is Data Science · 1.02: Data Science in Practice
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The Crystal Ball - Instruction Manual I: Introduction to Data Science
Stephen Davies · University of Mary Washington
In the contents: 1.01: Defining Data Science
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CIS211: Support for Foundations of Data Science for All
Clare Nguyen · De Anza College
This course is a review of core prerequisite skills, competencies, and concepts needed whenstudying data science, intended for students who are concurrently enrolled in Data Science for All.
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DSCI 500B Essential Probability Theory for Data Science (Kuter)
Kristin Kuter · Saint Mary's College
LibreTexts
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Computer Science from the Bottom Up
Ian Wienand · Red Hat
Computer science is often taught from the "top down"; applications, high level programming, software design and development theory, possibly data structures. Students will probably be exposed to binary, hopefully binary logic, possibly even some low level concepts such as registers, opcodes and the like at a superficial level. This book aims to move in completely the opposite direction, working from operating systems fundamentals through to how those applications are complied and executed.
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Discrete Mathematics for Computer Science
Jin He · Fresno City College
LibreTexts
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Discrete Mathematics for Computer Science
Mark A. Fitch · University of Alaska Anchorage
In class materials for an introduction to logic, proof, enumeration, relations, and graph theory for lower division undergraduate students of computer science and computer systems engineering. These materials were designed to introduce concepts of logic and discrete mathematics via carefully picked problems that test understanding of definitions. Another source of exercises is required at this time.
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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).
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Conducting Mixed-Methods Research: From Classical Social Sciences to the Age of Big Data and Analytics
Viswanath Venkatesh; Susan (Sue) Brown; Yulia Sullivan · Virginia Tech Libraries · 2023
Scholars in the social sciences are increasingly expected to incorporate both quantitative and qualitative techniques and methods into their research. The growth of “mixed-methods” research is evident in social science disciplines ranging from psychology and management to marketing and information systems. This book is designed to provide principles, strategies, and guidance specifically for researchers in these disciplines so that they can use mixed-methods research more effectively. In thirteen chapters, Conducting Mixed-Methods Research takes readers through the research process, from defining research questions to writing articles using a mixed-methods approach. For those who are well trained in either qualitative or quantitative methods, the book shows them how to think about the purposes of mixed-methods research, design mixed-methods studies, and develop meta-inferences by integrating findings from both methods. Throughout, the discussion is grounded in examples taken from published research, carefully chosen to highlight best practices, thus opening a window into a broad body of mixed-methods research applications.
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Operating Systems and Infrastructure in Data Science
Josef Spillner · vdf Hochschulverlag · 2023
Modern data scientists work with a number of tools and operating system facilities in addition to online platforms. Mastering these in combination to manage their data and to deploy software, models and data as ready-to-use online services as well as to perform data science and analysis tasks is in the focus of Operating Systems and Infrastructure in Data Science. Readers will come to understand the fundamental concepts of operating systems and to explore plenty of tools in hands-on tasks and thus gradually develop the skills necessary to compose them for programming in the large, an essential capability in their later career. The book guides students through semester studies, acts as reference knowledge base and aids in acquiring the necessary knowledge, skills and competences especially in self-study settings. A unique feature of the book is the associated access to Edushell, a live environment to practice operating systems and infrastructure tasks.
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Culturally Responsive Computing: An Introduction into Computer Science, Security, and Technology
Devan Walton · ROTEL · 2024
"Culturally Responsive Computing" is an essential textbook that explores the intricate relationship between technology and global societies. It gives students and professionals a deep understanding of how cultural contexts shape and are shaped by computing practices, from algorithm design to user interface development. This book equips readers with the knowledge and skills to create inclusive, ethical, and culturally sensitive technological solutions for our diverse world.
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Introduction to Data Science Using Python
Afrand Agah · The Pennsylvania Alliance for Design of Open Textbooks (PA-ADOPT) · 2024
This book contains two parts, the first is designed to be used in an introductory programming course for students looking to learn Python, without having any prior experience with programming. Basic programming concepts are discussed, explained, and illustrated with a Python program. Ample programming questions are provided for practice. The second part of the book utilizes machine-learning concepts and statistics to accomplish data-driven resolutions. Python programs are provided to apply scientific computing to conclude statistically driven results.
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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.
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Earth, Space, and Environmental Science Explorations with ArcGIS Pro
Wing Cheung · Wing Cheung
When I first started teaching Geographic Information Systems (GIS) over 15 years ago, there were only a handful of GIS tutorial books on the market. While a few of them focused on a particular theme, many of them were mainly technical publications with no unifying theme. These publications are perfect for teaching new users about basic GIS operations and continue to be invaluable to many users today. However, these publications consisted of tutorials focusing on vastly different real-world scenarios, and felt a little disjointed to integrate into a class that focuses on a specific field (e.g. Environmental Science, Geography, Urban Planning). Since then, I am happy to see that there has been a proliferation of publications that are not only focused on introducing the technology, but also provide an introduction to a specific field as well as GIS. It is with this consideration in mind that I wrote this book to not only introduce students to the tools in ArcGIS Pro, but also to provide a glimpse into some of the topics that may be covered in an introductory course in Geography, Environmental Science, or related disciplines. I aimed to achieve a balance of technical instruction and information about Earth, Space, and Environmental Sciences, to make this publication helpful for new GIS learners, as well as students who may be interested in learning about the applications of GIS in the Earth, Space, and Environmental Sciences. Before you begin with the book, please download and unzip the data for the book at https://tinyurl.com/ESESGIS. Each chapter will begin with a list of the technical objectives for the lab exercise, which is particularly useful to refer to should you need a refresher on how to perform a particular task at a later date. The list of technical objectives is followed by a short introduction that provides the context for the exercise. Subsequently, you will find the step-by-step directions for the exercises with questions embedded throughout (in yellow h
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Computer Science I
Chris Bourke · Chris Bourke · 2018
This textbook covers the traditional introductory Computer Science I topics but takes a unique approach. Topics are covered in a language-agnostic manner in the first part with supplemental parts that cover the same concepts in a specific language. The current version covers C, Java, and PHP. This textbook as been used in several Computer Science I sections over multiple years at the University of Nebraska-Lincoln
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Think Complexity: Exploring Complexity Science with Python
Allen Downey · Green Tea Press · 2018
Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you'll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics. Whether you're an intermediate-level Python programmer or a student of computational modeling, you'll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations. In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.
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Introduction to Computer Science
Dr. Jean-Claude Franchitti; Amal Alhosban; Mark Buckler; Joanna Gilberti · OpenStax, Rice University
Introduction to Computer Science provides a comprehensive foundation in core computer science concepts and principles, aligning with the scope and sequence of most introductory computer science courses. The offering serves as an engaging entry point for students pursuing diverse fields of study and employment, including computer science, business, engineering, data science, social sciences, and related disciplines. By addressing a broad learner audience—ranging from computer science majors to non-majors—the book offers a thorough introduction to computational thinking and its applications across multiple domains. Introduction to Computer Science is designed to be both interactive and practical, focusing on real-world applications that showcase how core computer science concepts can be used to solve complex problems. Students will explore foundational concepts, such as algorithms, data structures, computer systems organization, and software development, using an array of engaging, hands-on activities. Introduction to Computer Science also includes advanced topics such as machine learning, cybersecurity, and cloud computing, ensuring students have a solid foundation for continued study and future professional success. The offering integrates meaningful learning experiences through chapter-based scenarios, problem-solving exercises, embedded coding activities, and project-based assessments that encourage students to apply what they learn in authentic contexts. The material also includes an array of industry insights and explorations of emerging technology trends in order to provide a holistic approach to learning that extends beyond theory.