Bibliografía

BBG20

Peter C. Bruce, Andrew Bruce, and Peter Gedeck. Practical statistics for data scientists: 50+ essential concepts using R and Python. O'Reilly Media, Inc, Sebastopol, CA, second edition edition, 2020. ISBN 978-1-4920-7294-2. OCLC: on1158315601.

CCFO22

Jeffrey D. Camm, James J. Cochran, Michael J. Fry, and Jeffrey W. Ohlmann. Data visualization: exploring and explaining with data. Cengage Learning, Boston, 1e edition, 2022. ISBN 978-0-357-63134-8.

DI21

Jack Dougherty and Ilya Ilyankou. Hands-on data visualization: interactive storytelling from spreadsheets to code. O'Reilly Media, Inc, Boston, 2021. ISBN 978-1-4920-8600-0. OCLC: on1197722533.

Her06

José Juan Cáceres Hernández. Conceptos Basicos de Estadistica para Ciencias Sociales. Delta Publicaciones, 2006. ISBN 978-84-96477-43-8. Google-Books-ID: S3i_fndtcIEC.

HC15

Llinas Humberto and Rojas Álvarez Carlos. Estadística descriptiva y distribuciones de probabilidad. Universidad del Norte, January 2015. ISBN 978-958-741-915-3. Google-Books-ID: 43haDwAAQBAJ.

JF19

Fotis Jannidis and Julia Flanders. A gentle introduction to data modeling. In Julia Flanders and Fotis Jannidis, editors, The shape of data in the digital humanities: modeling texts and text-based resources, Digital research in the arts and humanities, pages 26–95. Routledge, Taylor & Francis Group, London ; New York, 2019.

JR17

Patrick Juola and Stephen Ramsay. Six Septembers: mathematics for the humanist. Zea E-Books, Lincoln, 2017. ISBN 978-1-60962-111-7. OCLC: 1015817392.

MSCI13

Viktor Mayer-Schönberger, Kenneth Cukier, and Antonio Iriarte. Big data: la revolución de los datos masivos. Turner, Madrid, 2013. ISBN 978-84-15832-10-2. OCLC: 892201485.

McG22

Susan E. McGregor. Practical Python data wrangling and data quality. O'Reilly Media. Inc, Sebastopol, CA, 2022. ISBN 978-1-4920-9150-9.

McK18

Wes McKinney. Python for data analysis: data wrangling with pandas, NumPy, and IPython. O'Reilly Media, Inc, Sebastopol, California, second edition edition, 2018. ISBN 978-1-4919-5766-0. OCLC: ocn959595088.

Mor07

Franco Moretti. Graphs, maps, trees: abstract models for literary history. Verso, London, New York, 2007. ISBN 978-1-84467-185-4. OCLC: 845372315.

NK15

Cole Nussbaumer Knaflic. Storytelling with data: a data visualization guide for business professionals. Wiley, Hoboken, New Jersey, 2015. ISBN 978-1-119-00225-3. OCLC: ocn909318525.

NK17

Cole Nussbaumer Knaflic. Storytelling con datos. Visualización de datos para profesionales. Wiley y Anaya Multimedia, Madrid, 2017. ISBN 978-84-415-3930-3. OCLC: 1022571497.

Sch21

Jonathan A. Schwabish. Better data visualizations: a guide for scholars, researchers, and wonks. Columbia University Press, New York, 2021. ISBN 978-0-231-55015-4.

Spi21

David J Spiegelhalter. The art of statistics: how to learn from data. Basic Books, New York, NY, 2021. ISBN 978-1-5416-7570-4. OCLC: 1226564752.

UN20

Torgeir Uberg Nærland. The political significance of data visualization: Four key perspectives. In Martin Engebretsen and Helen Kennedy, editors, Data Visualization in Society, pages 63–74. Amsterdam University Press, Amsterdam, 2020. URL: https://doi.org/10.5117/9789463722902_ch04 (visited on 2022-08-19).

Who21

Casey Whorton. Applying Custom Functions to Groups of Data in Pandas. July 2021. URL: https://towardsdatascience.com/applying-custom-functions-to-groups-of-data-in-pandas-928d7eece0aa (visited on 2022-08-16).

Yan13

David Yanofsky. The chart Tim Cook doesn’t want you to see. September 2013. URL: https://qz.com/122921/the-chart-tim-cook-doesnt-want-you-to-see/ (visited on 2022-08-19).

StepanekJ20

Stepanek and Suresh John. Thinking in Pandas. Apress, S.l., 2020. ISBN 978-1-4842-5838-5. OCLC: 1181905701. URL: https://link.springer.com/10.1007/978-1-4842-5839-2 (visited on 2021-03-05).