Veri Biliminin Çok Disiplinli Potansiyelinin Kilidini Açmak: Apriori Analizinden İçgörüler

Year 2024, EARLY VIEW, 1 - 1

Merve Nur Barun , Emrah Önder

https://doi.org/10.2339/politeknik.1432158

Abstract

Teknolojinin ve bilimin ilerlemesinde çok büyük öneme sahip olan veri bilimi alanında hangi çalışmaların gerçekleştirildiği ve hangi alanlarda çalışmaların eksik kaldığını belirlemek büyük önem taşımaktadır. Bu çalışma veri bilimi alanında çalışan araştırmacıların başka hangi alanlarda çalıştığını belirlemek ve analiz etmek için yapılmıştır. Bu kapsamda R ile iki farklı veri grubuna apriori analizi uygulanmıştır. İlk veri grubu Web of Science veri tabanınından data science anahtar kelimesini kullanmış olan SSCI, SCI, E-SCI yayınlanmış dergilerdeki makaleler elde edilmiştir. İkinci veri grubu Google Scholor’da Veri bilimi anahtar kelimesini kullanmış en çok atıf alan yazarların (316 yazar) listesinden seçilmiştir. Çalışmada toplam 2262 makale kullanılmıştır. Makalelerde 6533 tekil anahtar kelime olduğu gözlemlenmiştir. Elde edilen veri gruplarına R Studio programında veri madenciliği yöntemi olan Apriori analizi uygulanmıştır. Birliktelik kuralı çıktılarını belirlemek için destek, güven ve kaldıraç (ilginçlik) değerleri kullanılmıştır. Apriori analiz sonucuna göre veri bilimi ile kaldıraç değeri en yüksek konular karar ve politika belirlemesi, eğitimcilerin öğrenme yöntemlerini geliştirmesi, sağlık alanında meme kanseri tedavisi ve genetik bilimidir. Veri bilimi, evren bilimi (kozmoloji) ve ekoloji gibi daha birçok alanda önemli bir yere sahiptir. Bu durum veri biliminin multidisipliner bir alan olduğunu bir kez daha ortaya konmuştur.

Keywords

Veri Bilimi, Web of Science, Birliktelik Kuralları Analizi, Apriori Algoritması, Veri Madenciliği

Ethical Statement

Açık kaynak Web of Science ve Google Scholar platformlarından veriler elde edilmiştir.

Unlocking the Multidisciplinary Potential of Data Science: Insights from Apriori Analysis

Abstract

Data science holds paramount significance for the progress of technology and science. Consequently, it is imperative to discern the existing studies in data science and identify areas where research is deficient. For this reason, this study aims to identify, analyse other fields where researchers work in data science, and provide guidance for future research endeavours. The application of apriori analysis to two distinct data groups utilising the R Studio program is expounded in this article. The first data group comprises 2262 articles from SSCI, SCI, and E-SCI indexed journals, sourced from the Web of Science database using the keyword "data science." The second dataset is derived from a list of over 15,000 cited authors (316 authors) specialising in data science on Google Scholar. The study encompasses a total of 2262 articles and data from 316 authors. The articles encompass 6533 unique keywords. Employing apriori analysis, a data mining method, on the acquired datasets involves using support, confidence, and lift values to ascertain association rule outputs. The Apriori analysis results indicate that data science is pivotal in decision and policymaking, developing learning methods for educators, breast cancer treatment, and genetic science in the health domain. Furthermore, data science is significant in diverse fields such as cosmology and ecology. This outcome reaffirms the interdisciplinary nature of data science.

Keywords

Data Science, Web of Science, Association Rules Analysis, Apriori Algorithm, Data Mining

Ethical Statement

Data are obtained from open source Web of Science and Google Scholar platforms.

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