TEJAS Journal of Technologies and Humanitarian Science

The art of Data Analysis: Review on Essential, Techniques and Methodologies

Dr. Shalini Lamba
National Post Graduate College, Lucknow, India

Author

Harsha Sahni
National Post Graduate College, Lucknow, India

Author

Aanya Sharma
National Post Graduate College, Lucknow, India

Author

📜 License:

This work is licensed under a Creative Commons Attribution 4.0 International License

• Share — Copy and Redistribute the material
• Adapt — Remix, Transform, and build upon the material
• The licensor cannot revoke these freedoms as long as you follow the license terms.

Abstract:

This paper provides a comprehensive overview of the key processes and methodologies in data analytics, with a focus on knowledge discovery in databases (KDD). It explores the evolution of data analytics and its importance in today's data-driven world. The paper discusses the significance of each KDD operator, including selection, preprocessing, transformation, data mining, and interpretation/evaluation, in shaping the outcomes of data analytics processes. Through a detailed examination of these operations, the paper highlights the essential role of data analytics in gathering, analyzing, and presenting insights derived from data. Furthermore, the paper explores the challenges and advancements in big data analytics, emphasizing the importance of understanding computational complexities, informationsecurity, and computational methods for effective data analysis.

References

[1] A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” Int. J. Inf. Manage., vol. 35, no. 2, 2015, doi: 10.1016/j.ijinfomgt.2014.10.007.

[2] K. Kelley, “What is data analysis? Methods, process and types explained,” Simplilearn, 2023.

[3] D. Jhonshon, “What is data analysis? Research, types, methods, techniques,” Guru99, 2021.

[4] V. Çetin and O. Yıldız, “A comprehensive review on data preprocessing techniques in data analysis,” Pamukkale Univ. J. Eng. Sci., vol. 28, no. 2, 2022, doi: 10.5505/pajes.2021.62687.

[5] C. W. Tsai, C. F. Lai, H. C. Chao, and A. V. Vasilakos, “Big data analytics: A survey,” J. Big Data, vol. 2, no. 1, 2015, doi: 10.1186/s40537-015-0030-3.

[6] D. Fife, “The eight steps of data analysis: A graphical framework to promote sound statistical analysis,” Perspect. Psychol. Sci., vol. 15, no. 4, 2020, doi: 10.1177/1745691620917333.

[7] S. R. Durugkar, R. Raja, K. K. Nagwanshi, and S. Kumar, “Introduction to data mining,” in Data Mining and Machine Learning Applications, 2022, doi: 10.1002/9781119792529.ch1.

[8] H. Masood et al., “Research process and steps involved in data analysis,” J. Xidian Univ., vol. 16, no. 3, 2022.

[9] A. Oussous, F. Z. Benjelloun, A. Ait Lahcen, and S. Belfkih, “Big data technologies: A survey,” J. King Saud Univ. Comput. Inf. Sci., vol. 30, no. 4, 2018, doi: 10.1016/j.jksuci.2017.06.001.

[10] H. Taherdoost, “Different types of data analysis: Methods and techniques in research projects,” Int. J. Acad. Res. Manage., vol. 9, no. 1, 2020.

[11] P. Bihani and S. T. Patil, “A comparative study of data analysis techniques,” Int. J. Emerg. Trends Technol. Comput. Sci., vol. 3, no. 2, 2014.

[12] M. Islam, “Data analysis: Types, process, methods, techniques and tools,” Int. J. Data Sci. Technol., vol. 6, no. 1, 2020, doi: 10.11648/j.ijdst.20200601.12.

[13] J. C. O’Neill, “Statistical techniques for data analysis,” Technometrics, vol. 47, no. 3, 2005, doi: 10.1198/tech.2005.s301.

[14] R. H. Hariri, E. M. Fredericks, and K. M. Bowers, “Uncertainty in big data analytics: Survey, opportunities, and challenges,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0206-3.

[15] J. Fan, F. Han, and H. Liu, “Challenges of big data analysis,” Natl. Sci. Rev., vol. 1, no. 2, 2014, doi: 10.1093/nsr/nwt032.

[16] F. Amalina et al., “Blending big data analytics: Review on challenges and a recent study,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2019.2923270.