NishMath

The intersection of mathematics and technology is proving to be a hot topic, with articles exploring how mathematical concepts underpin many aspects of data science and programming. Key areas of focus include the essential math needed for programming, highlighting the importance of Boolean algebra, number systems, and linear algebra for creating efficient and complex code. Linear algebra, specifically the application of matrices, was noted as vital for data transformations, computer vision algorithms, and machine learning, enabling tasks such as vector operations, matrix transformations, and understanding data representation. The relationship between data science and mathematics is described as complex but crucial, with mathematical tools being the foundation of data-driven decisions. Probability and statistics are also essential, acting as lenses to understand uncertainty and derive insights, covering descriptive statistics like mean, median, mode and the application of statistical models. Computer vision also relies on math concepts, with specific applications like optical character recognition using techniques like pattern recognition and deep learning. Optimization of computer vision models is also discussed, with a focus on making models smaller and faster using techniques like pruning and quantization.

ImgSrc: miro.medium.com

References:

• dipjyotimetia.medium.com - Essential Mathematics for Better Programming - 2d
• @jyotinigam2370 - Data Science and Math: A Rollercoaster Relationship - 2d
• @sivan.santhosh - Math for Datascience — Matrices - 2d

Classification:

• HashTags: DataScience Programming AppliedMath
• Company: Medium
• Target: tech
• Product: datascience
• Feature: applications
• Type: Research
• Severity: Medium