Authors: Dr.Pranesh Kulkarni ., Assistant Professor Department of Mathematics
Abstract: Mathematics is a fundamental component of Data Science, providing the theoretical foundations for many data analysis and Machine learning techniques. A breakdown of the fundamental math field required for data Science, Linear Algebra, Calculus, and Probability Theory. Through mathematics we can learn data analysis and visualization in this we learn plotting, charting and data storytelling.in this Article we discussed structuring and designing of mathematics in data science this provides a Comprehensive framework for understanding the mathematical foundations. Data analysis and visualizations, machine learning And modeling, and mathematical techniques used in data science. Being a data scientist is more than just using plug-and- play machine learning packages. Educators have to understand what the algorithm is actually doing first and foremost and know when and why to use it. The process to learn what the algorithms are doing is by studying the underlying mathematics. We know that Geometry and graph theory form essential pillars of data science, it providing tools to model, analyze, and visualize complex relationship. These mathematical concepts enable data scientists to efficiently uncover patterns, optimize systems, and efficiently represent intricate datasets. Now, I know “Big Data” and “Hadoop” have become a bit of a big deal in the data world and are being thrown around like a cool fad, but it feels like a lot of people still don’t really understand the concept behind it. In this article I’ve covered the why and what of Open-source software how does it all actually work? Data is essential for ML- enabled systems. Poor data will result in inaccurate predictions, which are referred to in the ML context as “garbage in, garbage out”. Hence, ML requires high-quality input data. From the viewpoint of RE, it is clear that data constitutes a new type of requirements Based on the Data Quality model defined in the standard ISO/IEC 25012, we elaborate on the data Perspective.
DOI: http://doi.org/10.5281/zenodo.15833884
