Mastering data visualization with Python: practical tips for researchersopen access
- Authors
- Han, Soyul; Kwak, Il-Youp
- Issue Date
- Dec-2023
- Publisher
- 대한내시경로봇외과학회
- Keywords
- Big data; Data visualization; Matplotlib; Seaborn; Python
- Citation
- Journal of Minimally Invasive Surgery, v.26, no.4, pp 167 - 175
- Pages
- 9
- Journal Title
- Journal of Minimally Invasive Surgery
- Volume
- 26
- Number
- 4
- Start Page
- 167
- End Page
- 175
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/71276
- DOI
- 10.7602/jmis.2023.26.4.167
- ISSN
- 2234-778X
2234-5248
- Abstract
- Big data have revolutionized the way data are processed and used across all fields. In the past, research was primarily conducted with a focus on hypothesis confirmation using sample data. However, in the era of big data, this has shifted to gaining insights from the collected data. Visualizing vast amounts of data to derive insights is crucial. For instance, leveraging big data for visualization can help identify and predict characteristics and patterns related to various infectious diseases. When data are presented in a visual format, patterns within the data become clear, making it easier to comprehend and provide deeper insights. This study aimed to comprehensively discuss data visualization and the various techniques used in the process. It also sought to enable researchers to directly use Python programs for data visualization. By providing practical visualization exercises on GitHub, this study aimed to facilitate their application in research endeavors.
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