{"id":38574,"date":"2022-12-20T07:55:38","date_gmt":"2022-12-19T20:55:38","guid":{"rendered":"https:\/\/www.institutedata.com\/?p=38574"},"modified":"2022-12-20T07:59:55","modified_gmt":"2022-12-19T20:59:55","slug":"exploring-the-applications-of-mathematics-and-statistics-in-machine-learning-and-ai","status":"publish","type":"post","link":"https:\/\/www.institutedata.com\/nz\/blog\/exploring-the-applications-of-mathematics-and-statistics-in-machine-learning-and-ai\/","title":{"rendered":"Exploring the applications of mathematics and statistics in machine learning and AI"},"content":{"rendered":"
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A very common question when considering a\u00a0career change\u00a0to\u00a0data science\u00a0and machine learning is, how important is maths in\u00a0data science\u00a0and machine learning?<\/p>\n

Mathematics is a\u00a0tool used\u00a0to understand how we function in this world. Bertrand Russell, the philosopher who proved through deduction logic that one plus one equals two, stated that the true spirit of delight is to be found in mathematics as surely as in poetry.<\/p>\n

This article is going to answer your questions regarding the relationship between mathematics and data science. Once you have explored the\u00a0applications of mathematics and statistics\u00a0in machine learning and AI, you will discover the exciting\u00a0career prospects\u00a0that are in store for you.<\/p>\n

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  1. How are mathematics and statistics used in machine learning and AI?<\/b><\/li>\n<\/ol>\n

    Mathematics and statistics\u00a0are fundamental concepts in\u00a0machine learning\u00a0and\u00a0AI. Mathematics is essentially a numerical method of expressing ideas. Statistics is a more abstract form of communicating ideas. The simplest way to define\u00a0artificial intelligence\u00a0(AI) is machine learning from experience.\u00a0Machine learning\u00a0goes a step further to formulate decisions with the results from its experience.<\/p>\n

    The application of mathematics and statistics underpins the coding within machines. Data scientists are tasked with wrangling and extracting insight from data using mathematical models. Then, machine learning algorithms use this data to recount a story through further data analysis. Stakeholders can use these predictions and data patterns to make more informed business decisions.<\/p>\n

    Mathematics and statistics are key tools in data science, machine learning and AI to extract insight and uncover hidden patterns in the data. Once you have developed a conceptual and practical understanding of mathematics and statistics for data science and AI, you will be able to produce more innovative solutions and methods of handling data and visualising your findings.<\/p>\n

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    1. You may already have the level of mathematics skills required to upskill in data science<\/b><\/li>\n<\/ol>\n

      A number of professionals already have the level of maths required to easily upskill in data science.<\/p>\n

      Accountants:<\/p>\n

      Accountants have the\u00a0mathematics and statistics\u00a0knowledge required for a\u00a0career change\u00a0to\u00a0data science.\u00a0Your skills in logic and problem-solving will definitely assist you with understanding the underlying concepts of\u00a0machine learning.<\/p>\n

      Actuaries:<\/p>\n

      Data science\u00a0is transforming the insurance industry and the way in which actuaries predict financial risks. There is an extensive\u00a0level of maths required\u00a0in order to calculate and contain risk. Actuaries collate all the raw statistical data in order to present quantitative data.<\/p>\n

      Insurance Underwriters:<\/p>\n

      Similar to actuaries, insurance underwriters have the\u00a0level of maths required\u00a0to\u00a0quickly\u00a0upskill\u00a0to\u00a0data science. Underwriters use maths constantly in their work in order to accurately generate rates for risk, and manage the capacity levels and risk loss ratios of individual risk.<\/p>\n

      Financial Analysts:<\/p>\n

      Financial analysts have a number of\u00a0transferrable skills\u00a0to understand the essentials of machine learning. Financial analysts\u00a0solve problems\u00a0with\u00a0tools used\u00a0in\u00a0data science\u00a0such as\u00a0mathematical models.<\/p>\n

      Statisticians:<\/p>\n

      Statistics is a fundamental component of\u00a0machine learning\u00a0and statisticians can\u00a0easily upskill\u00a0with their existing knowledge. The only way to represent data is with a statistical framework. Data scientists work with statistics to optimise the performance of machines with the final outcome of interpreting data.<\/p>\n

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      1. The current applications of mathematics and statistics in data science<\/b><\/li>\n<\/ol>\n

        Mathematics and statistics\u00a0play a central role in\u00a0data science. A data scientist who cannot grasp maths is similar to a musician who cannot play a musical instrument well. You can only really go so far with limited skills. While many articles state that maths is not important and not needed, that argument is challenged by examples of the current\u00a0applications of mathematics and statistics\u00a0in\u00a0data science:<\/p>\n