{"id":5815,"date":"2024-09-24T13:44:45","date_gmt":"2024-09-24T08:14:45","guid":{"rendered":"https:\/\/kodakco.com\/blog\/?p=5815"},"modified":"2024-11-29T15:00:47","modified_gmt":"2024-11-29T09:30:47","slug":"role-of-machine-learning-in-data-science","status":"publish","type":"post","link":"https:\/\/kodakco.com\/blog\/role-of-machine-learning-in-data-science\/","title":{"rendered":"The Role of Machine Learning in Data Science"},"content":{"rendered":"\n

Today people now regard analytics as one of the most progressive and useful sciences in the 21st century. It defines the process of studying data and making something out of it to deduce something from the details, set policies, and make discoveries. As a result, there is a stronghold of machine learning in data science.<\/p>\n\n\n\n

Machine Learning (ML) is a subfield of AI that helps the computer build predictions or make decisions by feeding data without programming the application for each assignment. It is also necessary to note that the combination of Machine Learning and Data Science<\/a> practiced during the last two decades has become an integral part of business, government, and organizational decision-making, problem-solving, optimization, and innovation.<\/p>\n\n\n\n

What is Data Science?<\/strong><\/h2>\n\n\n\n
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Data science brings together several fields, including math, statistics, computer science, and specific subject knowledge. The explosion of information from social media, sensors, business systems, etc. requires new ways to handle this flood of data. The past problems of handling data were unsuitable for managing the large volumes, speed, and diversification of today\u2019s big data. Therefore, this need led to the development of data science as a new discipline. Data Science is driven by data scientists henceforth, data scientists and machine learning go hand in hand as they are the people responsible for turning raw data into meaningful insights.<\/p>\n\n\n\n

What is Machine Learning?<\/strong><\/h2>\n\n\n\n
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Artificial intelligence or AI comprising machine learning is a method of data analysis that automatically builds analytical models. It is for the assumption that systems are capable of learning from the data and making decisions on their own. AI as a field itself started in the Twentieth century and the real progress of machine learning was made in the 1990s and 2000s due to improvements in technology, the power of computational devices, and the presence of Big Data. There are many impacts of machine learning in data science in terms of functioning with sophisticated characteristics in data as well as the potential for consideration proportionally by large datasets. Data Scientists have used these to take on problems largely considered interactable when approached with classical statistical methods alone.<\/p>\n\n\n\n

An Insight on the Development of Machine Learning in Data Science<\/strong><\/h2>\n\n\n\n

Machine learning has been so successful in data science, mainly because of a variety of techniques available to play with and figure out the patterns that can be broadly divided into three different areas which are Supervised Learning<\/strong>, Unsupervised Learning<\/strong>, and Reinforcement Learning<\/strong>. All of these methodologies are built for specific scenarios and operations, as well as various kinds of data.<\/p>\n\n\n\n