May 1, 2024
Updated May 11, 2025
21 minute read
Data gathering, at its core, is the systematic process of collecting and measuring information on targeted variables. This process allows individuals and organizations to answer relevant questions, test hypotheses, evaluate outcomes, and ultimately make informed decisions. It is a fundamental activity across virtually all fields of study and professional endeavors, from the physical and social sciences to humanities and business. While the specific techniques and objectives may differ from one discipline to another, the overarching goal remains consistent: to capture credible evidence that enables robust analysis and leads to reliable conclusions. This foundational step is crucial whether you are conducting academic research, aiming to improve business operations, or seeking to understand societal trends.
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Reading list
We've selected 16 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
Data Gathering.
Foundational text for understanding the different approaches to research design, including qualitative, quantitative, and mixed methods. It provides a comprehensive overview of the research process, which is essential background knowledge for data gathering. It is commonly used as a textbook in academic institutions and valuable reference tool for students and researchers alike.
Bridges the gap between statistics and data science, providing essential statistical concepts and demonstrating their application using R and Python. It is highly relevant for understanding the quantitative aspects of data gathering and analysis. It serves as a useful reference tool and is suitable for those with some programming experience.
This practical guide focuses on technical data gathering from the web using Python. It is highly relevant for those interested in automated data collection from online sources. It serves as a useful reference and provides hands-on examples for implementing web scraping techniques.
Focuses on the critical aspect of data quality, which is paramount in data gathering. It covers concepts, methodologies, and techniques for ensuring high-quality data. It directly addresses a key challenge in data gathering and valuable reference.
This in-depth sourcebook cornerstone for qualitative data analysis, which is intricately linked with qualitative data gathering. While focused on analysis, it provides crucial context on how to work with and make sense of qualitative data once collected. It valuable reference for researchers.
Considered a classic, this book foundational text on the concept of privacy in the context of data collection and surveillance. While older, its insights into the tension between data gathering and individual privacy remain highly relevant today. It provides essential historical and theoretical background.
Focuses on qualitative research methods and the process of collecting data in the field. It is particularly relevant for understanding data gathering in human-centered design and social science contexts. It provides practical guidance and helps solidify an understanding of qualitative data collection techniques.
Provides a comprehensive guide to conducting survey research, a common method of quantitative data gathering. It covers the design, implementation, and analysis of surveys. It valuable resource for understanding the specifics of this data collection method.
Explores the intersection of data science and feminism, highlighting how data can perpetuate or challenge existing inequalities. It offers a critical lens on data gathering and its societal implications, aligning with contemporary topics in the field. It is valuable for understanding the social and ethical dimensions of data.
Discusses the design of algorithms that are mindful of social and ethical considerations. While focused on algorithms, it has implications for how data is gathered and used in algorithmic systems. It is relevant to contemporary discussions on fairness and bias in data and AI.
This handbook addresses the practical challenges of working with imperfect data, a common issue after data gathering. It provides guidance on cleaning, formatting, and effectively using data that may contain errors or inconsistencies. It useful reference for anyone dealing with real-world data.
Guides students through the process of conducting social science research, including the crucial step of data gathering. It is particularly helpful for undergraduate and graduate students undertaking research projects. It provides practical advice and helps solidify understanding of research methodologies.
Delves into the ethical considerations surrounding data management, including data gathering. It is essential for understanding the responsible collection and handling of data. It adds a crucial ethical dimension to the technical aspects of data gathering.
Explores the art and science of prediction in a data-rich world, emphasizing the importance of distinguishing between meaningful signals and random noise. It provides valuable insights into how to approach data with a critical eye, which is relevant to the analysis phase that follows data gathering. It popular and accessible read.
Offers a critical perspective on how data is created and utilized, providing important context for data gathering. It encourages readers to think about the origins and potential biases in data. It is valuable for gaining a broader understanding of the data landscape.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/zwnmuj/data
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