Introduction To Quantitative Social Science With Python

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Introduction to Quantitative Social Science with Python

Author : Weiqi Zhang
Publisher : CRC Press
ISBN 13 : 104014831X
Total Pages : 356 pages
Book Rating : 4.0/5 (41 download)

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Book Synopsis Introduction to Quantitative Social Science with Python by : Weiqi Zhang

Download or read book Introduction to Quantitative Social Science with Python written by Weiqi Zhang and published by CRC Press. This book was released on 2024-11-01 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Departing from traditional methodologies of teaching data analysis, this book presents a dual-track learning experience, with both Executive and Technical Tracks, designed to accommodate readers with various learning goals or skill levels. Through integrated content, readers can explore fundamental concepts in data analysis while gaining hands-on experience with Python programming, ensuring a holistic understanding of theory and practical application in Python. Emphasizing the practical relevance of data analysis in today's world, the book equips readers with essential skills for success in the field. By advocating for the use of Python, an open-source and versatile programming language, we break down financial barriers and empower a diverse range of learners to access the tools they need to excel. Whether you're a novice seeking to grasp the foundational concepts of data analysis or a seasoned professional looking to enhance your programming skills, this book offers a comprehensive and accessible guide to mastering the art and science of data analysis in social science research. Key Features: Dual-track learning: Offers both Executive and Technical Tracks, catering to readers with varying levels of conceptual and technical proficiency in data analysis. Includes comprehensive quantitative methodologies for quantitative social science studies. Seamless integration: Interconnects key concepts between tracks, ensuring a smooth transition from theory to practical implementation for a comprehensive learning experience. Emphasis on Python: Focuses on Python programming language, leveraging its accessibility, versatility, and extensive online support to equip readers with valuable data analysis skills applicable across diverse domains.

Quantitative Social Science

Author : Kosuke Imai
Publisher : Princeton University Press
ISBN 13 : 0691191093
Total Pages : 464 pages
Book Rating : 4.6/5 (911 download)

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Book Synopsis Quantitative Social Science by : Kosuke Imai

Download or read book Quantitative Social Science written by Kosuke Imai and published by Princeton University Press. This book was released on 2021-03-16 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a "translation" of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place"--

Programming with Python for Social Scientists

Author : Phillip D. Brooker
Publisher : SAGE
ISBN 13 : 1526486342
Total Pages : 370 pages
Book Rating : 4.5/5 (264 download)

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Book Synopsis Programming with Python for Social Scientists by : Phillip D. Brooker

Download or read book Programming with Python for Social Scientists written by Phillip D. Brooker and published by SAGE. This book was released on 2019-12-09 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: As data become ′big′, fast and complex, the software and computing tools needed to manage and analyse them are rapidly developing. Social scientists need new tools to meet these challenges, tackle big datasets, while also developing a more nuanced understanding of - and control over - how these computing tools and algorithms are implemented. Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge. It guides you through the full research process, from question to publication, including: the fundamentals of why and how to do your own programming in social scientific research, questions of ethics and research design, a clear, easy to follow ′how-to′ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more. Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.

Data Analysis for Social Science

Author : Elena Llaudet
Publisher : Princeton University Press
ISBN 13 : 0691199434
Total Pages : 256 pages
Book Rating : 4.6/5 (911 download)

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Book Synopsis Data Analysis for Social Science by : Elena Llaudet

Download or read book Data Analysis for Social Science written by Elena Llaudet and published by Princeton University Press. This book was released on 2022-11-29 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior knowledge of statistics or coding. It starts with straightforward, simple data analysis and culminates with multivariate linear regression models, focusing more on the intuition of how the math works rather than the math itself. The book makes extensive use of data visualizations, diagrams, pictures, cartoons, etc., to help students understand and recall complex concepts, provides an easy to follow, step-by-step template of how to conduct data analysis from beginning to end, and will be accompanied by supplemental materials in the appendix and online for both students and instructors"--

Quantitative Social Science

Author : Kosuke Imai
Publisher : Princeton University Press
ISBN 13 : 0691175462
Total Pages : 430 pages
Book Rating : 4.6/5 (911 download)

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Book Synopsis Quantitative Social Science by : Kosuke Imai

Download or read book Quantitative Social Science written by Kosuke Imai and published by Princeton University Press. This book was released on 2018-02-09 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introductory textbook on data analysis and statistics written especially for students in the social sciences and allied fields Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it—or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results—it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior. Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science Provides hands-on instruction using R programming, not paper-and-pencil statistics Includes more than forty data sets from actual research for students to test their skills on Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises Offers a solid foundation for further study Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides Looking for a more accessible introduction? Consider Data Analysis for Social Science by Elena Llaudet and Kosuke Imai, which teaches from scratch and step-by-step the fundamentals of survey research, predictive models, and causal inference. It covers descriptive statistics, the difference-in-means estimator, simple linear regression, and multiple linear regression.

Introduction to Data Science for Social and Policy Research

Author : Jose Manuel Magallanes Reyes
Publisher : Cambridge University Press
ISBN 13 : 1107117410
Total Pages : 317 pages
Book Rating : 4.1/5 (71 download)

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Book Synopsis Introduction to Data Science for Social and Policy Research by : Jose Manuel Magallanes Reyes

Download or read book Introduction to Data Science for Social and Policy Research written by Jose Manuel Magallanes Reyes and published by Cambridge University Press. This book was released on 2017-09-21 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive guide provides a step-by-step approach to data collection, cleaning, formatting, and storage, using Python and R.

Humanities Data Analysis

Author : Folgert Karsdorp
Publisher : Princeton University Press
ISBN 13 : 0691172366
Total Pages : 352 pages
Book Rating : 4.6/5 (911 download)

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Book Synopsis Humanities Data Analysis by : Folgert Karsdorp

Download or read book Humanities Data Analysis written by Folgert Karsdorp and published by Princeton University Press. This book was released on 2021-01-12 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to data-intensive humanities research using the Python programming language The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment. The book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Then, drawing from real-world, publicly available data sets that cover a variety of scholarly domains, the book delves into detailed case studies. Focusing on textual data analysis, the authors explore such diverse topics as network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Exercises and resources for further reading are provided at the end of each chapter. An ideal resource for humanities students and scholars aiming to take their Python skills to the next level, Humanities Data Analysis illustrates the benefits that quantitative methods can bring to complex research questions. Appropriate for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python Applicable to many humanities disciplines, including history, literature, and sociology Offers real-world case studies using publicly available data sets Provides exercises at the end of each chapter for students to test acquired skills Emphasizes visual storytelling via data visualizations

A Gentle Introduction to Effective Computing in Quantitative Research

Author : Harry J. Paarsch
Publisher : MIT Press
ISBN 13 : 0262333996
Total Pages : 777 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis A Gentle Introduction to Effective Computing in Quantitative Research by : Harry J. Paarsch

Download or read book A Gentle Introduction to Effective Computing in Quantitative Research written by Harry J. Paarsch and published by MIT Press. This book was released on 2016-05-06 with total page 777 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to using modern software effectively in quantitative research in the social and natural sciences. This book offers a practical guide to the computational methods at the heart of most modern quantitative research. It will be essential reading for research assistants needing hands-on experience; students entering PhD programs in business, economics, and other social or natural sciences; and those seeking quantitative jobs in industry. No background in computer science is assumed; a learner need only have a computer with access to the Internet. Using the example as its principal pedagogical device, the book offers tried-and-true prototypes that illustrate many important computational tasks required in quantitative research. The best way to use the book is to read it at the computer keyboard and learn by doing. The book begins by introducing basic skills: how to use the operating system, how to organize data, and how to complete simple programming tasks. For its demonstrations, the book uses a UNIX-based operating system and a set of free software tools: the scripting language Python for programming tasks; the database management system SQLite; and the freely available R for statistical computing and graphics. The book goes on to describe particular tasks: analyzing data, implementing commonly used numerical and simulation methods, and creating extensions to Python to reduce cycle time. Finally, the book describes the use of LaTeX, a document markup language and preparation system.

Text Analysis in Python for Social Scientists

Author : Dirk Hovy
Publisher : Cambridge University Press
ISBN 13 : 110888301X
Total Pages : 104 pages
Book Rating : 4.1/5 (88 download)

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Book Synopsis Text Analysis in Python for Social Scientists by : Dirk Hovy

Download or read book Text Analysis in Python for Social Scientists written by Dirk Hovy and published by Cambridge University Press. This book was released on 2021-01-21 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text is everywhere, and it is a fantastic resource for social scientists. However, because it is so abundant, and because language is so variable, it is often difficult to extract the information we want. There is a whole subfield of AI concerned with text analysis (natural language processing). Many of the basic analysis methods developed are now readily available as Python implementations. This Element will teach you when to use which method, the mathematical background of how it works, and the Python code to implement it.

Introduction to Data Science

Author : Laura Igual
Publisher : Springer
ISBN 13 : 3319500171
Total Pages : 227 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Introduction to Data Science by : Laura Igual

Download or read book Introduction to Data Science written by Laura Igual and published by Springer. This book was released on 2017-02-22 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

Doing Computational Social Science

Author : John McLevey
Publisher : SAGE
ISBN 13 : 1529736706
Total Pages : 689 pages
Book Rating : 4.5/5 (297 download)

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Book Synopsis Doing Computational Social Science by : John McLevey

Download or read book Doing Computational Social Science written by John McLevey and published by SAGE. This book was released on 2021-12-15 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational approaches offer exciting opportunities for us to do social science differently. This beginner’s guide discusses a range of computational methods and how to use them to study the problems and questions you want to research. It assumes no knowledge of programming, offering step-by-step guidance for coding in Python and drawing on examples of real data analysis to demonstrate how you can apply each approach in any discipline. The book also: Considers important principles of social scientific computing, including transparency, accountability and reproducibility. Understands the realities of completing research projects and offers advice for dealing with issues such as messy or incomplete data and systematic biases. Empowers you to learn at your own pace, with online resources including screencast tutorials and datasets that enable you to practice your skills and get up to speed. For anyone who wants to use computational methods to conduct a social science research project, this book equips you with the skills, good habits and best working practices to do rigorous, high quality work.

Big Data and Social Science

Author : Ian Foster
Publisher : CRC Press
ISBN 13 : 1498751431
Total Pages : 493 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Big Data and Social Science by : Ian Foster

Download or read book Big Data and Social Science written by Ian Foster and published by CRC Press. This book was released on 2016-08-10 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.

Python Programming for Mathematics

Author : Julien Guillod
Publisher : CRC Press
ISBN 13 : 1040271987
Total Pages : 248 pages
Book Rating : 4.0/5 (42 download)

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Book Synopsis Python Programming for Mathematics by : Julien Guillod

Download or read book Python Programming for Mathematics written by Julien Guillod and published by CRC Press. This book was released on 2030-12-04 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python Programming for Mathematics focuses on the practical use of the Python language in a range of different areas of mathematics. Through fifty-five exercises of increasing difficulty, the book provides an expansive overview of the power of using programming to solve complex mathematical problems. This book is intended for undergraduate and graduate students who already have learned the basics of Python programming and would like to learn how to apply that programming skill in mathematics. Features Innovative style that teaches programming skills via mathematical exercises. Ideal as a main textbook for Python for Mathematics courses, or as a supplementary resource for Numerical Analysis and Scientific Computing courses.

Python Packages

Author : Tomas Beuzen
Publisher : CRC Press
ISBN 13 : 1000555127
Total Pages : 252 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Python Packages by : Tomas Beuzen

Download or read book Python Packages written by Tomas Beuzen and published by CRC Press. This book was released on 2022-04-20 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python Packages introduces Python packaging at an introductory and practical level that’s suitable for those with no previous packaging experience. Despite this, the text builds up to advanced topics such as automated testing, creating documentation, versioning and updating a package, and implementing continuous integration and deployment. Covering the entire Python packaging life cycle, this essential guide takes readers from package creation all the way to effective maintenance and updating. Python Packages focuses on the use of current and best-practice packaging tools and services like poetry, cookiecutter, pytest, sphinx, GitHub, and GitHub Actions. Features: The book’s source code is available online as a GitHub repository where it is collaborated on, automatically tested, and built in real time as changes are made; demonstrating the use of good reproducible and clear project workflows. Covers not just the process of creating a package, but also how to document it, test it, publish it to the Python Package Index (PyPI), and how to properly version and update it. All concepts in the book are demonstrated using examples. Readers can follow along, creating their own Python packages using the reproducible code provided in the text. Focuses on a modern approach to Python packaging with emphasis on automating and streamlining the packaging process using new and emerging tools such as poetry and GitHub Actions.

Introduction to Quantitative Social Science with Python

Author : Weiqi Zhang
Publisher :
ISBN 13 : 9781003327899
Total Pages : 0 pages
Book Rating : 4.3/5 (278 download)

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Book Synopsis Introduction to Quantitative Social Science with Python by : Weiqi Zhang

Download or read book Introduction to Quantitative Social Science with Python written by Weiqi Zhang and published by . This book was released on 2024-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Departing from traditional methodologies of teaching data analysis, this book presents a dual-track learning experience, with both Executive and Technical Tracks, designed to accommodate readers with various learning goals or skill levels. Through integrated content, readers can explore fundamental concepts in data analysis while gaining hands-on experience with Python programming, ensuring a holistic understanding of theory and practical application in Python. Emphasizing the practical relevance of data analysis in today's world, the book equips readers with essential skills for success in the field. By advocating for the use of Python, an open-source and versatile programming language, we break down financial barriers and empower a diverse range of learners to access the tools they need to excel. Whether you're a novice seeking to grasp the foundational concepts of data analysis or a seasoned professional looking to enhance your programming skills, this book offers a comprehensive and accessible guide to mastering the art and science of data analysis in social science research"--

Python for Data Analysis

Author : Wes McKinney
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491957611
Total Pages : 553 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Python for Data Analysis by : Wes McKinney

Download or read book Python for Data Analysis written by Wes McKinney and published by "O'Reilly Media, Inc.". This book was released on 2017-09-25 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Text Analysis with R

Author : Matthew L. Jockers
Publisher : Springer Nature
ISBN 13 : 3030396436
Total Pages : 283 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Text Analysis with R by : Matthew L. Jockers

Download or read book Text Analysis with R written by Matthew L. Jockers and published by Springer Nature. This book was released on 2020-03-30 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, Text Analysis with R provides a practical introduction to computational text analysis using the open source programming language R. R is an extremely popular programming language, used throughout the sciences; due to its accessibility, R is now used increasingly in other research areas. In this volume, readers immediately begin working with text, and each chapter examines a new technique or process, allowing readers to obtain a broad exposure to core R procedures and a fundamental understanding of the possibilities of computational text analysis at both the micro and the macro scale. Each chapter builds on its predecessor as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying. Text Analysis with R is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological toolkit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that readers simply cannot gather using traditional qualitative methods of close reading and human synthesis. This new edition features two new chapters: one that introduces dplyr and tidyr in the context of parsing and analyzing dramatic texts to extract speaker and receiver data, and one on sentiment analysis using the syuzhet package. It is also filled with updated material in every chapter to integrate new developments in the field, current practices in R style, and the use of more efficient algorithms.