Digital library
Textbooks, references and open learning resources.
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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 · 03: Three Kinds of Atomic Data · 3.01: Atomic Data
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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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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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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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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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Computer Networks - A Systems Approach
Peterson and Davie · LibreTexts
In the contents: 07: End-to-End Data · 7.02: Multimedia Data
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Open Data Structures - An Introduction
Pat Morin · Carleton University
In the contents: 1.07: List of Data Structures · 13: Data Structures for Integers
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The Stories Behind Social Statistics: Data Analysis, Interpretation, and Communication
Ezra Temko · Southern Illinois University Edwardsville
In the contents: 01: Data Datasets SPSS and Frequency Distribution Tables
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Database Design 2e
Adrienne Watt · British Columbia Institute of Technology
In the contents: 1.01: Before the Advent of Database Systems · 1.03: Characteristics and Benefits of a Database · 1.04: Types of Data Models
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Applied Event-Related Potential Data Analysis
Steven J Luck · University of California, Davis
In the contents: 1.02: Installing the Software and Downloading the Data · 02: Processing the Data from One Participant in the ERP CORE N400 Experiment · 2.01: Data For This Chapter
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Relational Databases and Microsoft Access
Ron McFadyen · University of Winnipeg
In the contents: 01: Relational Databases and MS Access · 1.01: Relational Databases · 08: Mapping an ERD to a Relational Database
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Data Structure and Algorithms
Harrison Njoroge · African Virtual University
In the contents: 03: Basic Data Structures and Abstract Data Types · 3.01: Activity 1 - Data Structures · 3.02: Activity 2 - Abstract Data Type
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GIS Commons: An Introductory Textbook on Geographic Information Systems
Michael Schmandt · Sacramento State University
In the contents: 1.06: Data Model · 02: Data Input · 2.02: Phase 1- GIS Database Design
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Data Structures
Wikipedia · LibreTexts
This book is about the creation and analysis of efficient data structures. To understand the material in this book you should be comfortable enough in a programming language to be capable of working with and writing your own variables, arithmetic expressions, if-else conditions, loops, subroutines (also known as functions), pointers (also known as references or object handles), structures (also known as records or classes), simple input and output, and simple recursion.
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Think Data Structures - Algorithms and Information Retrieval in Java
Allen B. Downey · Olin College
In the contents: 3.04: Linked Data Structures · 8.01: Data structure selection · 14.04: Redis data types
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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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Oceans of Data
Tasha Gownaris · Gettysburg College
In the contents: 01: Our Oceans and Oceanographic Data · 1.04: How are data about the ocean collected · 1.05: How are oceanographic data displayed