Abstract
SHS investigation development is considered from the geographical and historical viewpoint. 3 stages are described. Within Stage 1 the work was carried out in the Department of the Institute of Chemical Physics in Chernogolovka where the scientific discovery had been made. At Stage 2 the interest to SHS arose in different cities and towns of the former USSR. Within Stage 3 SHS entered the international scene. Now SHS processes and products are being studied in more than 50 countries.
Abstract
The field of data science relies heavily on mathematical analysis. A solid
foundation in certain branches of mathematics is essential for every data scientist
already working in the field or planning to enter it in the future. In whatever area we
focus on, data science, machine learning engineering, business intelligence
development, data architecture, or another area of expertise, it is important to examine
the several kinds of mathematical prerequisites and insights and how they're applied in
the field of data science. Machine learning algorithms, data analysis and analyzing
require mathematics. Mathematics is not the only qualification for a data science
education and profession but is often the most significant. Identifying and translating
business difficulties into mathematical ones are a crucial phase in a data scientist's
workflow. In this study, we describe the different areas of mathematics utilized in data
science to understand mathematics and data science together.
Keywords:
Baye's theorem, Classification, Computer programs, Data science, Linear algebra, Machine learning, Matrices, Normal distribution, Optimization, Regression, System of linear equations, Vectors.
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Authors:Bentham Science Books