Digital library
Textbooks, references and open learning resources.
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Programming with Java
Ashik Bhuiyan; Md Amiruzzaman · The Pennsylvania Alliance for Design of Open Textbooks (PA-ADOPT) · 2025
Programming with Java is a beginner-friendly eTextbook that introduces readers to the fundamentals of Java, one of the most widely used programming languages. It covers key concepts like syntax, data types, control structures, and object-oriented programming, offering clear explanations and practical examples. The chapters build progressively, making the material accessible even to those with little or no programming background. The book also explores important topics such as exception handling, file I/O, and basic data structures, providing a strong foundation for further computer science studies. With interactive exercises and end-of-chapter projects, readers can apply their knowledge through real-world applications. This eTextbook equips students with the skills they need to confidently take on advanced programming challenges and pursue successful careers in software development.
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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.
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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.
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Research Data Management in the Canadian Context
Emily Carlisle-Johnston; Danielle Dennie; Émilie Fortin; Elizabeth Hill · Western University · 2023
Research Data Management is a term for all the things that researchers do to structure, organize and maintain data before, during and after doing research. RDM is also an emerging discipline that is concerned with researching and developing ways to manage research data more effectively. But what is research data? Where is the push towards formal Research Data Management coming from? What are the requirements of good data management? Research Data Management in the Canadian Context: A Guide for Practitioners and Learners looks at these questions and more, all with a focus on Canadian guidelines, regulations and infrastructure.
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An Open Guide to Data Structures and Algorithms
Paul Bible; Lucas Moser; Mia Scarlato · PALNI · 2023
This textbook serves as a gentle introduction for undergraduates to theoretical concepts in data structures and algorithms in computer science while providing coverage of practical implementation (coding) issues. The field of computer science (CS) supports a multitude of essential technologies in science, engineering, and communication as a social medium. The varied and interconnected nature of computer technology permeates countless career paths making CS a popular and growing major program. Mastery of the science behind computer science relies on an understanding of the theory of algorithms and data structures. These concepts underlie the fundamental tradeoffs that dictate performance in terms of speed, memory usage, and programming complexity that separate novice programmers from professional practitioners.
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The Shallow and the Deep: A biased introduction to neural networks and old school machine learning
Michael Biehl · University of Groningen Press · 2023
The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus lies on classical machine learning techniques, with a bias towards classification and regression. Other learning paradigms and many recent developments in, for instance, Deep Learning are not addressed or only briefly touched upon. Biehl argues that having a solid knowledge of the foundations of the field is essential, especially for anyone who wants to explore the world of machine learning with an ambition that goes beyond the application of some software package to some data set. Therefore, The Shallow and the Deep places emphasis on fundamental concepts and theoretical background. This also involves delving into the history and pre-history of neural networks, where the foundations for most of the recent developments were laid. These notes aim to demystify machine learning and neural networks without losing the appreciation for their impressive power and versatility.
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Programming Persistent Memory: A Comprehensive Guide for Developers
Stteve Scargall · Apress · 2020
Beginning and experienced programmers will use this comprehensive guide to persistent memory programming. You will understand how persistent memory brings together several new software/hardware requirements, and offers great promise for better performance and faster application startup times—a huge leap forward in byte-addressable capacity compared with current DRAM offerings. This revolutionary new technology gives applications significant performance and capacity improvements over existing technologies. It requires a new way of thinking and developing, which makes this highly disruptive to the IT/computing industry. The full spectrum of industry sectors that will benefit from this technology include, but are not limited to, in-memory and traditional databases, AI, analytics, HPC, virtualization, and big data. Programming Persistent Memory describes the technology and why it is exciting the industry. It covers the operating system and hardware requirements as well as how to create development environments using emulated or real persistent memory hardware. The book explains fundamental concepts; provides an introduction to persistent memory programming APIs for C, C++, JavaScript, and other languages; discusses RMDA with persistent memory; reviews security features; and presents many examples. Source code and examples that you can run on your own systems are included. What You’ll Learn Understand what persistent memory is, what it does, and the value it brings to the industry Become familiar with the operating system and hardware requirements to use persistent memory Know the fundamentals of persistent memory programming: why it is different from current programming methods, and what developers need to keep in mind when programming for persistence Look at persistent memory application development by example using the Persistent Memory Development Kit (PMDK) Design and optimize data structures for persistent memory Study how real-world applications are modified to le
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Computer Systems Security: Planning for Success
Ryan Tolboom · Ryan Tolboom
The text, labs, and review questions in this book are designed as an introduction to the applied topic of computer security. With these resources students will learn ways of preventing, identifying, understanding, and recovering from attacks against computer systems. This text also presents the evolution of computer security, the main threats, attacks and mechanisms, applied computer operation and security protocols, main data transmission and storage protection methods, cryptography, network systems availability, recovery, and business continuation procedures.
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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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Evidence-based Software Engineering
Derek Jones · Knowledge Software · 2020
This book discusses what is currently known about software engineering, based on an analysis of all the publicly available data. This aim is not as ambitious as it sounds, because there is not a great deal of data publicly available. The intent is to provide material that is useful to professional developers working in industry; until recently researchers in software engineering have been more interested in vanity work, promoted by ego and bluster. The material is organized in two parts, the first covering software engineering and the second the statistics likely to be needed for the analysis of software engineering data.
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The Crystal Ball Instruction Manual
Stephen Davies · University of Mary Washington · 2020
A perfect introduction to the exploding field of Data Science for the curious, first-time student. The author brings his trademark conversational tone to the important pillars of the discipline: exploratory data analysis, choices for structuring data, causality, machine learning principles, and introductory Python programming using open-source Jupyter Notebooks. This engaging read will allow any dedicated learner to build the skills necessary to contribute to the Data Science revolution, regardless of background.
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The Discipline of Organizing: 4th Professional Edition
Robert Glushko · University of California, Berkeley · 2013
We organize things, we organize information, we organize information about things, and we organize information about information. But even though “organizing” is a fundamental and ubiquitous challenge, when we compare these activities their contrasts are more apparent than their commonalities. We propose to unify many perspectives about organizing with the concept of an Organizing System, defined as an intentionally arranged collection of resources and the interactions they support. Every Organizing System involves a collection of resources, a choice of properties or principles used to describe and arrange resources, and ways of supporting interactions with resources. By comparing and contrasting how these activities take place in different contexts and domains, we can identify patterns of organizing. We can create a discipline of organizing in a disciplined way. The 4th edition builds a bridge between organizing and data science. It reframes descriptive statistics as organizing techniques, expands the treatment of classification to include computational methods, and incorporates many new examples of data-driven resource selection, organization, maintenance, and personalization. It introduces a new “data science” category of discipline-specific content, both in the chapter text and in endnotes, marked with [DS] in editions that contain endnotes.
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A Primer for Computational Biology
Shawn O'Neil · Oregon State University · 2017
A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work. The book is broken into three parts: Introduction to Unix/Linux: The command-line is the “natural environment” of scientific computing, and this part covers a wide range of topics, including logging in, working with files and directories, installing programs and writing scripts, and the powerful “pipe” operator for file and data manipulation. Programming in Python: Python is both a premier language for learning and a common choice in scientific software development. This part covers the basic concepts in programming (data types, if-statements and loops, functions) via examples of DNA-sequence analysis. This part also covers more complex subjects in software development such as objects and classes, modules, and APIs. Programming in R: The R language specializes in statistical data analysis, and is also quite useful for visualizing large datasets. This third part covers the basics of R as a programming language (data types, if-statements, functions, loops and when to use them) as well as techniques for large-scale, multi-test analyses. Other topics include S3 classes and data visualization with ggplot2.
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The Unicode cookbook for linguists: Managing writing systems using orthography profiles
Steven Moran; Michael Cysouw · Language Science Press · 2018
This text is a practical guide for linguists, and programmers, who work with data in multilingual computational environments. We introduce the basic concepts needed to understand how writing systems and character encodings function, and how they work together at the intersection between the Unicode Standard and the International Phonetic Alphabet. Although these standards are often met with frustration by users, they nevertheless provide language researchers and programmers with a consistent computational architecture needed to process, publish and analyze lexical data from the world's languages. Thus we bring to light common, but not always transparent, pitfalls which researchers face when working with Unicode and IPA. Having identified and overcome these pitfalls involved in making writing systems and character encodings syntactically and semantically interoperable (to the extent that they can be), we created a suite of open-source Python and R tools to work with languages using orthography profiles that describe author- or document-specific orthographic conventions. In this cookbook we describe a formal specification of orthography profiles and provide recipes using open source tools to show how users can segment text, analyze it, identify errors, and to transform it into different written forms for comparative linguistics research.
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Think Data Structures: Algorithms and Information Retrieval in Java
Allen Downey · Green Tea Press · 2016
Data structures and algorithms are among the most important inventions of the last 50 years, and they are fundamental tools software engineers need to know. But in my opinion, most of the books on these topics are too theoretical, too big, and too bottom-up: Too theoretical: Mathematical analysis of algorithms is based on simplifying assumptions that limit its usefulness in practice. Many presentations of this topic gloss over the simplifications and focus on the math. In this book I present the most practical subset of this material and eliminate the rest. Too big: Most books on these topics are at least 500 pages, and some are more than 1000. By focusing on the topics I think are most useful for software engineers, I kept this book under 250 pages. Too bottom-up: Many data structures books focus on how data structures work (the implementations), with less about how to use them (the interfaces). In this book, I go “top down”, starting with the interfaces. Readers learn to use the structures in the Java Collections Framework before getting into the details of how they work. Finally, many present this material out of context and without motivation: it’s just one damn data structure after another! I try to alleviate the boredom by organizing the topics around an application—web search—that uses data structures extensively, and is an interesting and important topic in its own right. This application also motivates some topics that are not usually covered in an introductory data structures class, including persistent data structures, with Redis, and streaming algorithms. This book also presents basic aspects of software engineering practice, including version control and unit testing. Each chapter ends with an exercise that allows readers to apply what they have learned. Each exercise includes automated tests that check the solution. And for most exercises, I present my solution at the beginning of the next chapter. This book is intended for college students in computer
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Implementing a One Address CPU in Logisim
Charles Kann · A.T. Still University · 2016
Most computer users have an incorrect, but useful, cognitive metaphor for computers in which the user says (or types or clicks) something and a mystical, almost intelligent or magical, behavior happens. It is not a stretch to describe computer users as believing computers follow the laws of magic, where some magic incantation is entered, and the computer responds with an expected, but magical, behavior. This magic computer does not actually exist. In reality computer are machines, and every action a computer performs reduces to a set of mechanical operations. In fact the first complete definition of a working computer was a mechanical machine designed by Charles Babbage in 1834, and would have run on steam power. Probably the biggest success of Computer Science (CS) in the 20th century was the development of abstractions that hide the mechanical nature of computers. The fact that average people use computers without ever considering that they are mechanistic is a triumph of CS designers. This purpose of this monograph is to break the abstract understanding of a computer, and to explain a computer's behavior in completely in mechanistic terms. It will deal specifically with the Central Processing Unit (CPU) of the computer, as this is where the magic happens. All other parts of a computer can be seen as just providing information for the CPU to operate on. This monograph will deal with a specific type of CPU, a one-address CPU, and will explain this CPU using only standard gates, specifically AND, OR, NOT, NAND and XOR gates, and 4 basic Integrated Circuits (ICs), the Decoder, Multiplexer, Adder, and Flip Flop. All of these gates and components can be described as mechanical transformations of input data to output data, and the overall CPU can then be seen as a mechanical device.
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Enterprise Pharo a Web Perspective
Square Bracket Associates · 2016
Enterprise Pharo is the third volume of the series, following Pharo by Example and Deep into Pharo. It covers enterprise libraries and frameworks, and in particular those useful for doing web development. The book is structured in five parts. The first part talks about simple web applications, starting with a minimal web application in chapter 1 on Teapot and then a tutorial on building a more complete web application in chapter 2. Part two deals with HTTP support in Pharo, talking about character encoding in chapter 3, about using Pharo as an HTTP Client (chapter 4) and server (chapter 5), and about using WebSockets (chapter 6).In the third part we discuss the handling of data for the application. Firstly we treat data that is in the form of comma-separated values (CSV) in chapter 7. Secondly and thirdly, we treat JSON (chapter 8) and its Smalltalk counterpart STON (chapter 9). Fourthly, serialization and deserialization of object graphs with Fuel is treated in chapter 10. Lastly, we discuss the Voyage persistence framework and persisting to MongoDB databases in chapter 11. Part four deals with the presentation layer. Chapter 12 shows how to use Mustache templates in Pharo, and chapter 13 talks about programmatic generationof CSS files. The documentation of applications could be written in Pillar, which is presented in chapter 14. How to generate PDF files from the application with Artefact is shown in chapter 15. The fifth part deals with deploying the web application. This is explained in chapter 16 that talks not only about how to build and run the application, but also other important topics like monitoring.
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Relational Databases and Microsoft Access 365
Ron McFadyen · Ron McFadyen · 2021
This text is a free introductory text that introduces MS Access and relational database design. The motivation is to support an introductory database system course which, to the student, is either a service course providing an introduction to database concepts, or, as a prerequisite for more advanced study in the field.
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Database Design
Adrienne Watt · BCcampus · 2014
This second edition of Database Design book covers the concepts used in database systems and the database design process. Topics include: The history of databases Characteristics and benefits of databases Data models Data modelling Classification of database management systems Integrity rules and constraints Functional dependencies Normalization Database development process New to this edition are more examples, highlighted and defined key terms, both throughout and at the end of each chapter, and end-of-chapter review exercises. Two new chapters have been added on SQL, along with appendices that include a data model example, sample ERD exercises and SQL lab with solutions.
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Python for Everybody: Exploring Data Using Python 3
Charles Severance · Charles Severance · 2016
I never seemed to find the perfect data-oriented Python book for my course, so I set out to write just such a book. Luckily at a faculty meeting three weeks before I was about to start my new book from scratch over the holiday break, Dr. Atul Prakash showed me the Think Python book which he had used to teach his Python course that semester. It is a well-written Computer Science text with a focus on short, direct explanations and ease of learning.The overall book structure has been changed to get to doing data analysis problems as quickly as possible and have a series of running examples and exercises about data analysis from the very beginning. Chapters 2–10 are similar to the Think Python book, but there have been major changes. Number-oriented examples and exercises have been replaced with data- oriented exercises. Topics are presented in the order needed to build increasingly sophisticated data analysis solutions. Some topics like try and except are pulled forward and presented as part of the chapter on conditionals. Functions are given very light treatment until they are needed to handle program complexity rather than introduced as an early lesson in abstraction. Nearly all user-defined functions have been removed from the example code and exercises outside of Chapter 4. The word “recursion”1 does not appear in the book at all. In chapters 1 and 11–16, all of the material is brand new, focusing on real-world uses and simple examples of Python for data analysis including regular expressions for searching and parsing, automating tasks on your computer, retrieving data across the network, scraping web pages for data, object-oriented programming, using web services, parsing XML and JSON data, creating and using databases using Structured Query Language, and visualizing data. The ultimate goal of all of these changes is a shift from a Computer Science to an Informatics focus is to only include topics into a first technology class that can be useful even if one choose