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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Principles of Data Science
Dr. Shaun V. Ault; Dr. Soohyun Nam Liao; Larry Musolino; Wisam Bukaita · OpenStax, Rice University
Principles of Data Science is intended to support one- or two-semester courses in data science. It is appropriate for data science majors and minors as well as students concentrating in business, finance, health care, engineering, the sciences, and a number of other fields where data science has become critically important. The authors have included a diverse mix of scenarios, examples, and data types for analysis and discussion purposes. These include both fictional contexts and real-world sources, such as the Federal Reserve Economic Database and Nasdaq. Data sets focus on a range of topics: business, science, social sciences. Applications include healthcare, physical sciences, demographics, policy, and finance. Data ethics and the emergence of artificial intelligence are covered deeply – both in their own chapters and as consistent threads throughout the course material. The authors and contributors have developed rich in-chapter example problems and extensive practice exercises that encourage students to apply concepts in a variety of situations. Technical illustrations and Python code support and supplement the principles and theory. The text also includes direct links to downloadable data sets and Python code, as well as guidance on how to use them.
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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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Library 1600: Introduction to College-Level Research
Library Instruction Services · Iowa State University
In the contents: 2.08: Articles and Databases
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Polynomial Interpolations on Time Scales
Svetlin G. Georgiev · Sorbonne University
Polynomial interpolation is a very useful tool employed in many areas of science in which a given set of data needs to be represented by a function, in particular, by a polynomial taking the given values at the points of the given data set.
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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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UCD GEL 56 Introduction to Geophysics
Magali Billen · University of California, Davis
Introduction to Geophysics provides a solid foundation in fundamental concepts from physics (diffusion, gravity, wave propagation), material science (rheology), scientific computing (basic programming, data analysis) and geophysics (plate tectonics, gravity, seismology, earthquakes) that are essential to multiple sub-topics in Earth and Planetary Sciences.
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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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A CURE for everyone: A guide to implementing Course-based Undergraduate Research Experiences
Jonathan J.-M. Calède · The Ohio State University
In this book, I undertake a review of the literature on Course-based Undergraduate Research Experiences (CUREs) and present original data from a survey of instructors at the Ohio State University. The main goal of this publication is to be a practical guide for teachers wishing to develop and implements this type of High-Impact Practice in their course.
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Corpus Linguistics: A Guide to the Methodology
Anatol Stefanowitsch · Freie Universität Berlin
In the contents: 01: The need for corpus data · 1.01: Arguments against corpus data · 1.03: Intuition data vs. corpus data
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Investigations in Climate Change
Neel Desai and Alicia Mullens · De Anza College
In the contents: 3.01: Climate Data · 3.02: Local Climate Data
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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