Programming With Python For Social Scientists
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Author |
: Phillip D. Brooker |
Publisher |
: SAGE |
Total Pages |
: 354 |
Release |
: 2019-12-09 |
ISBN-10 |
: 9781526486349 |
ISBN-13 |
: 1526486342 |
Rating |
: 4/5 (49 Downloads) |
Synopsis Programming with Python for Social Scientists by : Phillip D. Brooker
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.
Author |
: Frederick Kaefer |
Publisher |
: SAGE Publications |
Total Pages |
: 553 |
Release |
: 2020-08-06 |
ISBN-10 |
: 9781544377483 |
ISBN-13 |
: 1544377487 |
Rating |
: 4/5 (83 Downloads) |
Synopsis Introduction to Python Programming for Business and Social Science Applications by : Frederick Kaefer
Would you like to gather big datasets, analyze them, and visualize the results, all in one program? If this describes you, then Introduction to Python Programming for Business and Social Science Applications is the book for you. Authors Frederick Kaefer and Paul Kaefer walk you through each step of the Python package installation and analysis process, with frequent exercises throughout so you can immediately try out the functions you’ve learned. Written in straightforward language for those with no programming background, this book will teach you how to use Python for your research and data analysis. Instead of teaching you the principles and practices of programming as a whole, this application-oriented text focuses on only what you need to know to research and answer social science questions. The text features two types of examples, one set from the General Social Survey and one set from a large taxi trip dataset from a major metropolitan area, to help readers understand the possibilities of working with Python. Chapters on installing and working within a programming environment, basic skills, and necessary commands will get you up and running quickly, while chapters on programming logic, data input and output, and data frames help you establish the basic framework for conducting analyses. Further chapters on web scraping, statistical analysis, machine learning, and data visualization help you apply your skills to your research. More advanced information on developing graphical user interfaces (GUIs) help you create functional data products using Python to inform general users of data who don’t work within Python. First there was IBM® SPSS®, then there was R, and now there′s Python. Statistical software is getting more aggressive - let authors Frederick Kaefer and Paul Kaefer help you tame it with Introduction to Python Programming for Business and Social Science Applications.
Author |
: Dirk Hovy |
Publisher |
: Cambridge University Press |
Total Pages |
: 104 |
Release |
: 2021-01-21 |
ISBN-10 |
: 9781108883016 |
ISBN-13 |
: 110888301X |
Rating |
: 4/5 (16 Downloads) |
Synopsis Text Analysis in Python for Social Scientists by : Dirk Hovy
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.
Author |
: John V. Guttag |
Publisher |
: MIT Press |
Total Pages |
: 466 |
Release |
: 2016-08-12 |
ISBN-10 |
: 9780262529624 |
ISBN-13 |
: 0262529629 |
Rating |
: 4/5 (24 Downloads) |
Synopsis Introduction to Computation and Programming Using Python, second edition by : John V. Guttag
The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.
Author |
: Joakim Sundnes |
Publisher |
: |
Total Pages |
: 157 |
Release |
: 2020 |
ISBN-10 |
: 9783030503567 |
ISBN-13 |
: 3030503569 |
Rating |
: 4/5 (67 Downloads) |
Synopsis Introduction to Scientific Programming with Python by : Joakim Sundnes
This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.
Author |
: Josh Cutler |
Publisher |
: Springer Nature |
Total Pages |
: 213 |
Release |
: 2020-04-22 |
ISBN-10 |
: 9783030368265 |
ISBN-13 |
: 3030368262 |
Rating |
: 4/5 (65 Downloads) |
Synopsis Computational Frameworks for Political and Social Research with Python by : Josh Cutler
This book is intended to serve as the basis for a first course in Python programming for graduate students in political science and related fields. The book introduces core concepts of software development and computer science such as basic data structures (e.g. arrays, lists, dictionaries, trees, graphs), algorithms (e.g. sorting), and analysis of computational efficiency. It then demonstrates how to apply these concepts to the field of political science by working with structured and unstructured data, querying databases, and interacting with application programming interfaces (APIs). Students will learn how to collect, manipulate, and exploit large volumes of available data and apply them to political and social research questions. They will also learn best practices from the field of software development such as version control and object-oriented programming. Instructors will be supplied with in-class example code, suggested homework assignments (with solutions), and material for practical lab sessions.
Author |
: Jose Manuel Magallanes Reyes |
Publisher |
: Cambridge University Press |
Total Pages |
: 317 |
Release |
: 2017-09-21 |
ISBN-10 |
: 9781107117419 |
ISBN-13 |
: 1107117410 |
Rating |
: 4/5 (19 Downloads) |
Synopsis Introduction to Data Science for Social and Policy Research by : Jose Manuel Magallanes Reyes
This comprehensive guide provides a step-by-step approach to data collection, cleaning, formatting, and storage, using Python and R.
Author |
: Ian Foster |
Publisher |
: CRC Press |
Total Pages |
: 493 |
Release |
: 2016-08-10 |
ISBN-10 |
: 9781498751438 |
ISBN-13 |
: 1498751431 |
Rating |
: 4/5 (38 Downloads) |
Synopsis Big Data and Social Science by : Ian Foster
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.
Author |
: Dirk Hovy |
Publisher |
: Cambridge University Press |
Total Pages |
: 102 |
Release |
: 2022-03-17 |
ISBN-10 |
: 9781108963091 |
ISBN-13 |
: 1108963099 |
Rating |
: 4/5 (91 Downloads) |
Synopsis Text Analysis in Python for Social Scientists by : Dirk Hovy
Text contains a wealth of information about about a wide variety of sociocultural constructs. Automated prediction methods can infer these quantities (sentiment analysis is probably the most well-known application). However, there is virtually no limit to the kinds of things we can predict from text: power, trust, misogyny, are all signaled in language. These algorithms easily scale to corpus sizes infeasible for manual analysis. Prediction algorithms have become steadily more powerful, especially with the advent of neural network methods. However, applying these techniques usually requires profound programming knowledge and machine learning expertise. As a result, many social scientists do not apply them. This Element provides the working social scientist with an overview of the most common methods for text classification, an intuition of their applicability, and Python code to execute them. It covers both the ethical foundations of such work as well as the emerging potential of neural network methods.
Author |
: John McLevey |
Publisher |
: SAGE |
Total Pages |
: 689 |
Release |
: 2021-12-15 |
ISBN-10 |
: 9781529736700 |
ISBN-13 |
: 1529736706 |
Rating |
: 4/5 (00 Downloads) |
Synopsis Doing Computational Social Science by : John McLevey
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.