Digital library
Textbooks, references and open learning resources.
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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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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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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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A First Course in Electrical and Computer Engineering
Louis Scharf · OpenStax CNX · 2009
This book was written for an experimental freshman course at the University of Colorado. The course is now an elective that the majority of our electrical and computer engineering students take in the second semester of their freshman year, just before their first circuits course. Our department decided to offer this course for several reasons: we wanted to pique student' interest in engineering by acquainting them with engineering teachers early in their university careers and by providing with exposure to the types of problems that electrical and computer engineers are asked to solve; we wanted students entering the electrical and computer engineering programs to be prepared in complex analysis, phasors, and linear algebra, topics that are of fundamental importance in our discipline; we wanted students to have an introduction to a software application tool, such as MATLAB, to complete their preparation for practical and efficient computing in their subsequent courses and in their professional careers; we wanted students to make early contact with advanced topics like vector graphics, filtering, and binary coding so that they would gain a more rounded picture of modern electrical and computer engineering. In order to introduce this course, we had to sacrifice a second semester of Pascal programming. We concluded that the sacrifice was worth making because we found that most of our students were prepared for high-level language computing after just one semester of programming. We believe engineering educators elsewhere are reaching similar conclusions about their own students and curriculums. We hope this book helps create a much needed dialogue about curriculum revision and that it leads to the development of similar introductory courses that encourage students to enter and practice our craft.Students electing to take this course have completed one semester of calculus, computer programming, chemistry, and humanities. Concurrently with this course, students take ph
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Blueprints: Creating, Describing, and Implementing Designs for Larger-scale Software Projects
Stephen Davies · University of Mary Washington · 2024
Blueprints is a concise yet comprehensive coverage of Object-Oriented Analysis and Design concepts, suitable for a second programming course in Computer Science. It introduces and teaches application development in a command-line environment, and assumes basic expertise with the Java programming language.
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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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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
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Signal Computing: Digital Signals in the Software Domain
Michael Stiber; Bilin Stiber; Eric Larson · Michael Stiber, Eric Larson · 2020
In this book, you will learn how digital signals are captured, represented, processed, communicated, and stored in computers. The specific topics we will cover include: physical properties of the source information (such as sound or images), devices for information capture (microphones, cameras), digitization, compression, digital signal representation (JPEG, MPEG), digital signal processing (DSP), signal analysis and feature extraction via re-representation as functions of frequency, and network communication. By the end of this book, you should understand the problems and solutions facing signal computing systems development in the areas of data structures and algorithms, data analytics, feature extraction, information retrieval, user interfaces, and communications.
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Think Bayes: Bayesian Statistics Made Simple
Allen Downey · Green Tea Press · 2012
Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.
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Open Data Structures: An Introduction
Pat Morin · Athabasca University Press · 2013
Offered as an introduction to the field of data structures and algorithms, Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs. Focusing on a mathematically rigorous approach that is fast, practical, and efficient, Morin clearly and briskly presents instruction along with source code. Analyzed and implemented in Java, the data structures presented in the book include stacks, queues, deques, and lists implemented as arrays and linked-lists; space-efficient implementations of lists; skip lists; hash tables and hash codes; binary search trees including treaps, scapegoat trees, and red-black trees; integer searching structures including binary tries, x-fast tries, and y-fast tries; heaps, including implicit binary heaps and randomized meldable heaps; graphs, including adjacency matrix and adjacency list representations; and B-trees. A modern treatment of an essential computer science topic, Open Data Structures is a measured balance between classical topics and state-of-the art structures that will serve the needs of all undergraduate students or self-directed learners.
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Physical Modeling in MATLAB
Allen Downey · Green Tea Press · 2021
Modeling and simulation are powerful tools for explaining the world, making predictions, designing things that work, and making them work better. Learning to use these tools can be difficult; this book is my attempt to make the experience as enjoyable and productive as possible. By reading this book — and working on the exercises — you will learn some programming, some modeling, and some simulation: With basic programming skills, you can create models for a wide range of physical systems. My goal is to help you develop these skills in a way you can apply immediately to real-world problems. This book presents the entire modeling process, including model selection, analysis, simulation, and validation. I explain this process in Chapter 1, and there are examples throughout the book. Simulation is an approach to modeling that uses computer programs to implement models and generate predictions. This book shows how simulations are used to run experiments, answer questions, and guide decision-making.
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Customer Insights
Aila Khan; Mohammad Hossain; Sabreena Amin · Western Sydney University · 2023
Increasingly, the concept of marketing research is being replaced with the term ‘customer or consumer insights’. As decision-makers are bombarded with data, it is important to have an Insights Team or Department which can sift through it all and identify those nuggets of information that can meaningfully explain human behaviour. Such insights must translate into an informed business strategy for success. This book has been curated to ensure that the practice of data collection, analysis, and interpretation is presented from an industry perspective.
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Introduction to Financial Accounting
David Annand; Henry Dauderis; Tilly Jensen · Lyryx · 2024
Introduction to Financial Accounting, originally by D. Annand and H. Dauderis, is intended for a first course in introductory financial accounting. It has been extensively edited by Athabasca University and reflects current International Financial Reporting Standards (IFRS). A corporate approach is utilized, versus beginning with a sole proprietorship emphasis and then converting to a corporate approach. The textbook focuses on core introductory financial accounting topics that match pre-requisite requirements for students advancing to intermediate financial accounting. The textbook covers all essential topics, including: the accounting cycle; merchandising; assigning costs to inventory; cash and receivables; property, plant and equipment; debt financing; equity financing; statement of cash flows; financial statement analysis; and proprietorships and partnerships. The original Annand/Dauderis version of the textbook including .docx files and ancillary material remains available upon request to D. Annand (davida@athabascau.ca).
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Fundamentals of Infrastructure Management
Donald Coffelt; Chris Hendrickson · Donald Coffelt and Chris Hendrickson · 2017
The authors believe this free of charge book, Fundamentals of Infrastructure Management, will expand the impact of the material and help improve the practice of infrastructure management. By ‘free of charge,' we mean that the material can be freely obtained, but readers should devote time and effort to mastering the material. We have provided problem assignments for various chapters, and we strongly urge readers to undertake the problems as a learning experience. This book grew out of a decade of co-teaching a course entitled ‘Infrastructure Management' at Carnegie Mellon University. Our teaching philosophy was to prepare students for work in the field of infrastructure management. We believe that infrastructure management is a professional endeavor and an attractive professional career. The book is co-authored by two accomplished engineers - each representing professional practice, academic research and theoretical evaluation. Their collective strengths are presented throughout the text and serve to support both the practice of infrastructure management and a role for infrastructure management inquiry and search. Importantly, both co-authors have academic research interests (and a number of research publications) on various topics of infrastructure management. That said, the primary audience for this book is expected to be professionals intending to practice infrastructure management, and only secondarily individuals who intend to pursue a career of research in the area. The text draws examples and discusses a wide variety of infrastructure systems, including roadways, telecommunications, power generation, buildings and systems of infrastructure. We have found that some common fundamentals of asset management, analysis tools and informed decision-making are useful for a variety of such systems. Certainly, many infrastructure managers encounter a variety of infrastructure types during their professional careers. Moreover, due to the functional inter-dependencies of
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Financial Strategy for Public Managers
Sharon Kioko; Justin Marlowe · Rebus Community · 2016
Financial Strategy for Public Managers is a new generation textbook for financial management in the public sector. It offers a thorough, applied, and concise introduction to the essential financial concepts and analytical tools that today's effective public servants need to know. It starts “at the beginning” and assumes no prior knowledge or experience in financial management. Throughout the text, Kioko and Marlowe emphasize how financial information can and should inform every aspect of public sector strategy, from routine procurement decisions to budget preparation to program design to major new policy initiatives. They draw upon dozens of real-world examples, cases, and applied problems to bring that relationship between information and strategy to life. Unlike other public financial management texts, the authors also integrate foundational principles across the government, non-profit, and “hybrid/for-benefit” sectors. Coverage includes basic principles of accounting and financial reporting, preparing and analyzing financial statements, cost analysis, and the process and politics of budget preparation. The text also includes several large case studies appropriate for class discussion and/or graded assignments.
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Introductory Business Statistics with Interactive Spreadsheets
Mohammad Mahbobi; Thomas Tiemann · BCcampus · 2010
Introductory Business Statistics with Interactive Spreadsheets – 1st Canadian Edition is an adaptation of Thomas K. Tiemann's book, Introductory Business Statistics. This new edition still contains the basic ideas behind statistics, such as populations, samples, the difference between data and information, and sampling distributions as well as information on descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics. New topics include the chi-square test and categorical variables, null and alternative hypotheses for the test of independence, simple linear regression model, least squares method, coefficient of determination, confidence interval for the average of the dependent variable, and prediction interval for a specific value of the dependent variable. This new edition also allows readers to learn the basic and most commonly applied statistical techniques in business in an interactive way — when using the web version — through interactive Excel spreadsheets. For each topic, a customized interactive template has been created within which selected values can be repeatedly changed to observe how the entire process, as well as the outcomes, are automatically adjusted. Also, in this adapted edition, the real-world examples throughout the text, and the information in general, have been revised to reflect Canadian content.
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Growth and Competitive Strategy in 3 Circles
Joe Urbany; James Davis · Saylor Foundation · 2012
The 3-Circle model was developed over the past several years, initially in strategic planning for a university graduate program and in an executive MBA course designed to integrate the concepts of marketing and competitive strategy. Over the course of time, the 3-Circle model has been successfully used by hundreds of organizations throughout the world in establishing and growing their market positions. Many of the case examples in this book demonstrating applications of the 3-Circle model applications are from executives who have attended executive education training at the University of Notre Dame. The development of competitive strategy is difficult because there are a lot of moving parts, as well as hundreds of frameworks, that might potentially guide the effort. Executives appreciate how the 3-Circle model simplifies the integration of customer, firm, and competitor analysis to generate growth strategies. It also provides a common language and process for understanding and explaining competitive advantage and for identifying profitable growth strategy.
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Developing New Products and Services
G. Lawrence Sanders · Saylor Foundation · 2012
Developing New Products and Services by Sanders is an outstanding contribution to market research. The book focuses on the upfront activities and ideas for new product and service development. A central theme of Developing New Products and Services is that there is, or should be, a constant struggle going on in every organization, business, and system between delivering feature-rich versions of products and services using extravagant engineering and delivering low-cost versions of products and services using frugal engineering. Students will come away with this notion and how to manifest it as a contributing employee at any company. A number of powerful concepts and tools are presented so your students can better understand how to facilitate new product development. For example, three templates are featured that facilitate new product and service development. The FAD (features, attributes, and design) template is used to identify the features and attributes that can be used for product and service differentiation. The Ten–Ten planning process contains two templates: an Organizational and Industry Analysis template and the Business Plan Overview template. These two templates coupled with the FAD template can be used to develop a full-blown business plan. In addition, Developing New Products and Services includes the following topics: entrepreneurship, technology and product life cycles, product and service versioning, product line optimization, creativity, lock-in real options, business valuation, and project management. Evaluate Developing New Products and Services by Sanders for your marketing courses today.
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Contract Doctrine, Theory & Practice
J.H. Verkerke · CALI's eLangdell® Press · 2014
This is the first in a series of Contracts casebooks. It was originally titled "Collaborative Teaching Materials for Contracts." The first semester of law school is mostly about learning to speak a new legal language (but emphatically not “legalese”), to formulate and evaluate legal arguments, to become comfortable with the distinctive style of legal analysis. We could teach these skills using almost any legal topic. But we begin the first-year curriculum with subjects that pervade the entire field of law. Contract principles have a long history and they form a significant part of the way that lawyers think about many legal problems. As you will discover when you study insurance law, employment law, family law, and dozens of other practice areas, your knowledge of contract doctrine and theory will be invaluable.