Abstract

Data Science has become a key area of research, providing new insights and solutions in areas ranging from business analytics to scientific research. The article discusses the role and significance of linear algebra methods for data analysis in data science. Linear algebra plays a key role in Data Science, providing powerful tools for analyzing, processing and modeling data. The article provides an overview of the main methods of linear algebra and their application in Data Science. Basic concepts such as vectors, matrices, and then operations on them used for data manipulation are considered. We explore how these concepts find their application in data analysis and processing. Linear regression methods, singular value decomposition, and other linear algebrabased methods are covered. An overview of some projects and problems carried out using linear algebra methods is given. The main Python libraries for performing operations on vectors and matrices are also presented

Keywords

data analysis and processing; Data Science; NumPy; Google Colab; artificial intelligence

References

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