Derleme
BibTex RIS Kaynak Göster

A Comparative Analysis of Classical Statistics and Data Science in Academic and Scientific Research

Yıl 2025, Cilt: 12 Sayı: 2, 263 - 289, 31.12.2025
https://doi.org/10.51725/etad.1617560

Öz

This review explores the evolving relationship between classical statistics and data science in academic and scientific research. Classical statistics offers a rigorous foundation for hypothesis testing, inferential analysis, and structured data interpretation. In contrast, data science incorporates computational tools, such as machine learning and big data analytics, to handle complex, high-volume, and unstructured data. The paper highlights key methodological differences and areas of overlap between the two fields, particularly in relation to model interpretation, predictive accuracy, and decision-making. It proposes a hybrid analytical approach that combines the theoretical depth of classical statistics with the scalability and flexibility of data science. This integrated perspective enhances the reliability, applicability, and efficiency of data analysis across various research settings. By synthesizing relevant literature and practices, the article contributes to ongoing discussions on methodological integration and offers practical insights for researchers and policymakers addressing contemporary data challenges.

Kaynakça

  • Agresti, A., & Finlay, B. (2009). Statistical methods for the social sciences (4th ed.). Boston, MA: Pearson.
  • Binns, R., Veale, M., Shadbolt, N., & O’Hara, K. (2018). The role of algorithmic accountability in ensuring the ethical use of data. ACM Transactions on Internet Technology, 18(3), 1–23.
  • Borgman, C. L. (2015). Big data, little data, no data: Scholarship in the networked world. Cambridge, MA: MIT.
  • Boyd, S., & Vandenberghe, L. (2018). Convex optimization. Cambridge, UK: Cambridge University.
  • Casella, G., & Berger, R. L. (2021). Statistical inference (2nd ed.). Boston, MA: Cengage Learning.
  • Clark, T., Woodley, R., & Halas, D. (1962). Bilimsel yayınlarda etik. İstanbul: Jeopolitik.
  • Dasu, T., & Johnson, M. E. (2003). Exploratory data mining and data cleaning. Hoboken, NJ: Wiley-Interscience.
  • Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
  • Few, S. (2012). The visual display of quantitative information. Berkeley, CA: Perceptual Edge.
  • Field, A. (2013). Discovering statistics using IBM SPSS statistics. Los Angeles, CA: SAGE.
  • Freedman, D., Pisani, R., & Purves, R. (2007). Statistics (4th ed.). New York, NY: W. W. Norton & Company.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT.
  • Gualtieri, M. (2016). Real-time data analytics: How to use big data to transform your business. Cambridge, MA: Forrester Research.
  • Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics. New York, NY: McGraw-Hill.
  • Hagan, T. (2017). The interdisciplinary nature of data science. Data Science Journal, 16(1), 1–13.
  • Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Burlington, MA: Morgan Kaufmann.
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. New York, NY: Springer.
  • Jain, A. K. (2010). Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31(8), 651–666.
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. New York, NY: Springer.
  • Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12.
  • Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. New York, NY: Springer.
  • McKinney, W. (2017). Python for data analysis: Data wrangling with pandas, NumPy, and IPython. Sebastopol, CA: O’Reilly Media.
  • Miller, J. (2010). Data mining and data warehousing: A comprehensive guide for IT professionals. Hoboken, NJ: Wiley.
  • Moore, D. S., McCabe, G. P., & Craig, B. A. (2012). Introduction to the practice of statistics. New York, NY: W. H. Freeman and Company.
  • Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York, NY: NYU.
  • O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. New York, NY: Crown.
  • Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. Sebastopol, CA: O’Reilly Media.
  • Repko, A. F. (2012). Interdisciplinary research: Process and theory. Los Angeles, CA: SAGE.
  • Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Boston, MA: Pearson.
  • Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572.
  • Silver, N. (2012). The signal and the noise: Why so many predictions fail—but some don’t. New York, NY: Penguin.
  • Strang, G. (2016). Introduction to linear algebra (5th ed.). Wellesley, MA: Wellesley-Cambridge.
  • Sweeney, L. (2013). Discrimination in online ad delivery. In Proceedings of the 2013 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1–9). ACM.
  • Tufte, E. R. (2001). The visual display of quantitative information. Cheshire, CT: Graphics.
  • Van Rossum, G., & Drake, F. L. (2009). Python 3 reference manual. Scotts Valley, CA: CreateSpace.
  • Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Boston, MA: Cengage Learning.
  • Yau, N. (2011). Visualize this: The FlowingData guide to design, visualization, and statistics. Hoboken, NJ: Wiley.
  • Yau, N. (2013). Data points: Visualization that means something. Hoboken, NJ: Wiley.
  • Zikopoulos, P., Eaton, C., DeRoos, D., Deutsch, S., & Lapis, G. (2012). Understanding big data: Analytics for enterprise class Hadoop and streaming data. New York, NY: McGraw-Hill.

Akademik ve Bilimsel Araştırmalarda Klasik İstatistik ve Veri Bilimi Üzerine Mukayeseli Bir Analiz

Yıl 2025, Cilt: 12 Sayı: 2, 263 - 289, 31.12.2025
https://doi.org/10.51725/etad.1617560

Öz

Bu derleme, akademik ve bilimsel araştırmalarda klasik istatistik ile veri bilimi arasındaki gelişen ilişkiyi incelemektedir. Klasik istatistik, hipotez testleri, çıkarımsal analiz ve yapılandırılmış veri yorumlaması için sağlam bir temel sunar. Buna karşılık, veri bilimi; makine öğrenmesi ve büyük veri analitiği gibi hesaplamalı araçları kullanarak karmaşık, yüksek hacimli ve yapılandırılmamış verileri işler. Makale, özellikle model yorumlama, öngörü doğruluğu ve karar verme süreçleri açısından iki alan arasındaki temel yöntemsel farklılıkları ve örtüşen noktaları vurgular. Klasik istatistiğin kuramsal derinliği ile veri biliminin ölçeklenebilirlik ve esnekliğini birleştiren hibrit bir analiz yaklaşımı önerilir. Bu bütünleşik bakış açısı, veri analizinin güvenilirliğini, uygulanabilirliğini ve verimliliğini farklı araştırma alanlarında artırmaktadır. İlgili literatür ve uygulamaların sentezi yoluyla makale, yöntemsel entegrasyon üzerine süregelen tartışmalara katkıda bulunmakta ve günümüzün veri odaklı sorunlarıyla ilgilenen araştırmacılar ile politika yapıcılara pratik içgörüler sunmaktadır.

Kaynakça

  • Agresti, A., & Finlay, B. (2009). Statistical methods for the social sciences (4th ed.). Boston, MA: Pearson.
  • Binns, R., Veale, M., Shadbolt, N., & O’Hara, K. (2018). The role of algorithmic accountability in ensuring the ethical use of data. ACM Transactions on Internet Technology, 18(3), 1–23.
  • Borgman, C. L. (2015). Big data, little data, no data: Scholarship in the networked world. Cambridge, MA: MIT.
  • Boyd, S., & Vandenberghe, L. (2018). Convex optimization. Cambridge, UK: Cambridge University.
  • Casella, G., & Berger, R. L. (2021). Statistical inference (2nd ed.). Boston, MA: Cengage Learning.
  • Clark, T., Woodley, R., & Halas, D. (1962). Bilimsel yayınlarda etik. İstanbul: Jeopolitik.
  • Dasu, T., & Johnson, M. E. (2003). Exploratory data mining and data cleaning. Hoboken, NJ: Wiley-Interscience.
  • Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
  • Few, S. (2012). The visual display of quantitative information. Berkeley, CA: Perceptual Edge.
  • Field, A. (2013). Discovering statistics using IBM SPSS statistics. Los Angeles, CA: SAGE.
  • Freedman, D., Pisani, R., & Purves, R. (2007). Statistics (4th ed.). New York, NY: W. W. Norton & Company.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT.
  • Gualtieri, M. (2016). Real-time data analytics: How to use big data to transform your business. Cambridge, MA: Forrester Research.
  • Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics. New York, NY: McGraw-Hill.
  • Hagan, T. (2017). The interdisciplinary nature of data science. Data Science Journal, 16(1), 1–13.
  • Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Burlington, MA: Morgan Kaufmann.
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. New York, NY: Springer.
  • Jain, A. K. (2010). Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31(8), 651–666.
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. New York, NY: Springer.
  • Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12.
  • Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. New York, NY: Springer.
  • McKinney, W. (2017). Python for data analysis: Data wrangling with pandas, NumPy, and IPython. Sebastopol, CA: O’Reilly Media.
  • Miller, J. (2010). Data mining and data warehousing: A comprehensive guide for IT professionals. Hoboken, NJ: Wiley.
  • Moore, D. S., McCabe, G. P., & Craig, B. A. (2012). Introduction to the practice of statistics. New York, NY: W. H. Freeman and Company.
  • Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York, NY: NYU.
  • O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. New York, NY: Crown.
  • Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. Sebastopol, CA: O’Reilly Media.
  • Repko, A. F. (2012). Interdisciplinary research: Process and theory. Los Angeles, CA: SAGE.
  • Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Boston, MA: Pearson.
  • Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572.
  • Silver, N. (2012). The signal and the noise: Why so many predictions fail—but some don’t. New York, NY: Penguin.
  • Strang, G. (2016). Introduction to linear algebra (5th ed.). Wellesley, MA: Wellesley-Cambridge.
  • Sweeney, L. (2013). Discrimination in online ad delivery. In Proceedings of the 2013 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1–9). ACM.
  • Tufte, E. R. (2001). The visual display of quantitative information. Cheshire, CT: Graphics.
  • Van Rossum, G., & Drake, F. L. (2009). Python 3 reference manual. Scotts Valley, CA: CreateSpace.
  • Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Boston, MA: Cengage Learning.
  • Yau, N. (2011). Visualize this: The FlowingData guide to design, visualization, and statistics. Hoboken, NJ: Wiley.
  • Yau, N. (2013). Data points: Visualization that means something. Hoboken, NJ: Wiley.
  • Zikopoulos, P., Eaton, C., DeRoos, D., Deutsch, S., & Lapis, G. (2012). Understanding big data: Analytics for enterprise class Hadoop and streaming data. New York, NY: McGraw-Hill.
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sistematik Felsefe (Diğer)
Bölüm Derleme
Yazarlar

Murat Şengöz 0000-0001-6597-0161

Gönderilme Tarihi 10 Ocak 2025
Kabul Tarihi 17 Kasım 2025
Yayımlanma Tarihi 31 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 12 Sayı: 2

Kaynak Göster

APA Şengöz, M. (2025). A Comparative Analysis of Classical Statistics and Data Science in Academic and Scientific Research. Eğitim ve Toplum Araştırmaları Dergisi, 12(2), 263-289. https://doi.org/10.51725/etad.1617560