From social science to data science / Bernie Hogan.

By:

Hogan, Bernie [author]

Material type: Text

TextPublication details: Los Angeles : Sage Publications Ltd., 2023.Description: xxxiv, 361 pages : illustrations ; 24 cmISBN:

9781529707496

Subject(s):

Social sciences--Research--Data processing

LOC classification:

HA 29

Summary: "Built around the entire research process with a focus on ethics, this book equips you with the skills you need to successfully conduct a computational social science research project with Python. It shows you how to draw elements of social research, such as constructing a research question, together with coding and collecting data through wrangling and visualisation. Going beyond basic data science, it helps you take the next step into more advanced topics like time series analysis, natural language processing and social network analysis with confidence. Straightforward and practical, this book: • Provides a set of techniques for capturing, cleaning, merging and presenting online data to help you develop best practice in your own research. •Helps you access and extract APis while summarising important information. Showcases a diverse range of social data examples from across the web including Twitter, Wikipedia and Reddit. Accompanied by open access Jupyter notebooks and step-by-step coding instructions in Python, this book helps you scale up your existing skills to meet the demands of working with big data head on."

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Foreign Books	HRVVMC Library	Foreign Books	HA 29 H64 2023 (Browse shelf(Opens below))	Available

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Includes bibliographical references and index.

"Built around the entire research process with a focus on ethics, this book equips you with the skills you need to successfully conduct a computational social science research project with Python. It shows you how to draw elements of social research, such as constructing a research question, together with coding and collecting data through wrangling and visualisation.
Going beyond basic data science, it helps you take the next step into more advanced topics like time series analysis, natural language processing and social network analysis with confidence. Straightforward and practical, this book:
• Provides a set of techniques for capturing, cleaning, merging and presenting online data to help you develop best practice in your own research.
•Helps you access and extract APis while summarising important information.
Showcases a diverse range of social data examples from across the web including
Twitter, Wikipedia and Reddit. Accompanied by open access Jupyter notebooks and step-by-step coding instructions in Python, this book helps you scale up your existing skills to meet the demands of working with big data head on."

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