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Data Analysis is a crucial aspect of various fields, including business, economics and science. Mathematical techniques play a vital role in data analysis, enabling researches to extract insights and meaningful patterns from complex data sets. This paper provides an overview of mathematical techniques used in data analysis, including statistical methods, linear algebra and machine learning algorithms. We discuss the applications of these techniques in real-world scenarios, highlighting their significance in decision-making and problem solving. The paper also explores future directions and potential applications of mathematical techniques in date analysis, emphasizing the importance of interdisciplinary approaches and collaboration between mathematicians statisticians and domain experts. By examining the role of mathematical techniques in data analysis. This paper aims to contribute to the ongoing discussion on the importance of mathematical literacy in the digital age.
Keywords:
Data analysis mathematical techniques, linear algebra, Machine learning algorithms.
Cite Article:
"Mathematical Techniques In Data Analysis", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.b355-b359, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506144.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator