March 29, 2024
Updated April 13, 2025
23 minute read
A Career Guide to Becoming a Social Scientist
Social science is the broad field dedicated to understanding human society and social relationships. Social scientists systematically study all aspects of human behavior, social structures, interactions, and cultural phenomena. They use rigorous research methods to explore everything from individual choices and small group dynamics to large-scale societal trends, political systems, and economic forces.
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Find a path to becoming a Social Scientist. Learn more at:
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Reading list
We've selected some books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Social Scientist.
Provides a comprehensive overview of statistical methods used in psychology. It covers topics such as descriptive statistics, inferential statistics, and multivariate analysis. It is an excellent resource for students and researchers who want to learn about the statistical methods used in psychological research.
This influential textbook presents a rigorous and accessible introduction to discrete mathematics, with a focus on applications in computer science. It covers topics such as counting, probability, and graph theory, and it is suitable for advanced undergraduate and graduate students.
This textbook provides a rigorous and comprehensive introduction to mathematics for computer science students. It covers topics such as logic, set theory, counting, and graph theory, with a focus on applications in computer science.
This classic textbook provides a comprehensive overview of discrete mathematics, covering topics such as logic, set theory, counting, and graph theory. It is suitable for both undergraduate and graduate students, and it has been widely used in computer science, mathematics, and engineering programs.
Provides a practical guide to using statistics in social research. It covers topics such as data collection, data analysis, and interpretation. It is an excellent resource for students and researchers who want to learn how to use statistics to answer research questions.
Challenges the traditional view of statistical significance and argues that many non-significant results are actually meaningful. Ioannidis provides a framework for evaluating the credibility of research findings and offers guidance on how to design and interpret studies.
This highly influential book introduces the concept of causality in statistics and provides a framework for causal inference. It covers various statistical methods for identifying and estimating causal effects, including graphical models and counterfactual reasoning. Suitable for advanced graduate students and researchers in statistics, data science, and other fields.
This textbook introduces fundamental statistical concepts and methods, emphasizing the interpretation and communication of results. It covers various statistical relationships, including correlation, regression, and analysis of variance. Suitable for introductory statistics courses at the undergraduate level.
This textbook provides a concise and accessible introduction to discrete mathematics for computer science students. It covers topics such as logic, set theory, counting, and graph theory, with a focus on applications in computer science.
This handbook provides practical guidance for researchers and practitioners on how to design, implement, and evaluate Citizen Science projects.
This textbook provides a comprehensive overview of sociology, introducing students to the major theoretical perspectives and research methods in the field.
This textbook provides a comprehensive overview of anthropology, introducing students to the major subfields and research methods in the field.
This textbook provides a comprehensive overview of economics, introducing students to the major theoretical perspectives and research methods in the field.
This comprehensive textbook introduces Bayesian data analysis, a statistical approach that incorporates prior knowledge and uncertainty into statistical inference. It covers various Bayesian methods for modeling relationships between variables, including hierarchical models and Markov chain Monte Carlo (MCMC) techniques. Suitable for advanced undergraduate and graduate students in statistics and data science.
Explores the innovative aspects of Citizen Science, focusing on its impact on open science, society, and policy.
Provides a practical guide to using Citizen Science for environmental management, covering topics such as data collection, analysis, and interpretation.
This report by the National Research Council provides a comprehensive overview of public participation in scientific research, including Citizen Science.
This handbook provides a comprehensive overview of social theory, covering a wide range of theoretical perspectives from classical to contemporary approaches.
This textbook focuses on regression analysis and generalized linear models, which are widely used statistical techniques for modeling relationships between variables. It covers various types of regression models, model selection, and diagnostic methods. Suitable for advanced undergraduate and graduate students in statistics, economics, and other fields.
This classic book provides a thorough treatment of correlation and regression analysis, emphasizing graphical methods for data visualization and understanding relationships. Suitable for advanced undergraduate and graduate students in statistics and data science.
Provides a practical introduction to machine learning algorithms and techniques for analyzing relationships between variables. It covers supervised and unsupervised learning methods, feature engineering, and model evaluation. Suitable for a wide range of readers, including beginners with no prior machine learning experience.
This specialized book provides a comprehensive overview of deep learning techniques for natural language processing (NLP). It covers various NLP tasks, such as text classification, language modeling, and machine translation, and discusses how to model relationships between variables in text data. Suitable for advanced undergraduate and graduate students in NLP and related fields.
This textbook provides a comprehensive overview of social statistics, covering a wide range of topics from descriptive statistics to inferential statistics.
For more information about how these books relate to this course, visit:
OpenCourser.com/career/yg84c1/social
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