机读格式显示(MARC)
- 000 03838cam a2200409 i 4500
- 008 200814s2021 njuab b 001 0 eng
- 020 __ |a 9780691172361: |c CNY479.70
- 260 __ |a Princeton ; |a Oxford : |b Princeton University Press, |c 2021.
- 035 __ |a (OCoLC)on1151104697
- 035 __ |a (OCoLC)1151104697
- 040 __ |a DLC |b eng |c DLC |e rda |d OCLCO |d OCLCF |d YDX |d OCLCO |d OCLCQ |d PAU |d AAA |d IL4J6 |d OCLCO |d M$K |d UtOrBLW
- 050 00 |a AZ186 |b .K37 2021
- 082 00 |a 001.3072/1 |2 23
- 100 1_ |a Karsdorp, Folgert, |e author.
- 245 10 |a Humanities data analysis : |b case studies with Python / |c Folgert Karsdorp, Mike Kestemont & Allen Riddell.
- 300 __ |a xi, 337 pages : |b illustrations (some color), maps (some color) ; |c 27 cm
- 336 __ |a text |b txt |2 rdacontent
- 337 __ |a unmediated |b n |2 rdamedia
- 338 __ |a volume |b nc |2 rdacarrier
- 504 __ |a Includes bibliographical references and index.
- 505 0_ |a Introduction -- Parsing and manipulating structured data -- Exploring texts using the vector space model -- Processing tabular data -- Statistics essentials : who reads novels? -- Introduction to probability -- Narrating with maps -- Stylometry and the voice of Hildegard -- A topic model of United States Supreme Court opinions, 1900-2000 -- Epilogue. Good enough practices.
- 520 __ |a "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"-- |c Provided by publisher
- 650 _0 |a Humanities |x Research |x Methodology.
- 650 _0 |a Social sciences |x Research |x Methodology.
- 650 _0 |a Quantitative research |x Data processing.
- 650 _0 |a Python (Computer program language)
- 700 1_ |a Kestemont, Mike, |d 1985- |e author.
- 700 1_ |a Riddell, Allen, |e author.