{"id":19233,"date":"2022-02-17T13:10:01","date_gmt":"2022-02-17T12:10:01","guid":{"rendered":"https:\/\/stage-fp.webenv.pl\/blog\/?p=19233"},"modified":"2025-11-07T11:39:49","modified_gmt":"2025-11-07T10:39:49","slug":"data-science-vs-machine-learning-for-your-business","status":"publish","type":"post","link":"https:\/\/www.future-processing.com\/blog\/data-science-vs-machine-learning-for-your-business\/","title":{"rendered":"Data Science vs. Machine Learning: how they make businesses more effective?"},"content":{"rendered":"\n

However, even then, they cannot be regarded as totally separate, since machine learning can be considered a data science technique. In this article, you will learn what these two fields are all about, and how they are interrelated.<\/p>\n\n\n

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Artificial Intelligence vs Machine Learning vs Deep Learning vs Data Science vs Data Mining<\/figcaption>\n <\/figure>\n \n
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\"\" Artificial Intelligence vs Machine Learning vs Deep Learning vs Data Science vs Data Mining<\/figcaption>\n <\/figure>\n <\/div>\n <\/div>\n<\/div>\n <\/div>\n\n\n\n


What is Data Science?<\/h2>\n\n\n\n


Data science definition<\/h3>\n\n\n
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\n Data science is a multidisciplinary field in which the goal is to extract valuable information from data. It\u2019s geared towards helping organisations make better decisions and predictions, build effective strategies, and develop more advanced products. This requires domain expertise, a proficient knowledge of mathematics, and adequate programming skills. <\/div>\n <\/div>\n <\/div>\n\n\n
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Data science vs. statistics<\/h3>\n\n\n\n

Statistics is a field of mathematic that involves collecting, analysing, presenting and interpreting quantitative data.<\/strong> Statisticians focus on significance testing, diagnostic plotting, and normality distribution, etc. Data science, on the other hand, is a field which uses scientific methods and the newest technologies<\/a> to extract knowledge from these data sets<\/strong> in various forms. It evolved naturally from academic statistics, providing us with automation, the use of different programming languages (such as Python), and allowing us to leverage machine-learning libraries (such as TensorFlow).
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Of course, statisticians and data scientists have a lot in common. For example, they are both based in mathematics<\/strong>, they both analyse trends, and they both make predictions <\/strong>and prepare the results of their research for consumption by non-technical users. The biggest difference between them lies in the utilisation of new technologies.<\/p>\n\n\n\n


What is big data?<\/h3>\n\n\n\n

Big Data refers to data that is generated rapidly and produced continuously in huge volumes<\/strong>, and which is often available in real-time. The amount of data collected is definitely too large and complex to be stored or processed by traditional tools. <\/p>\n\n\n\n

That\u2019s why one of the biggest challenges in big data development is the efficient digestion of data \u2013 and this is where data science comes into play.<\/p>\n\n\n

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Data scientists apply machine learning algorithms to huge amounts of different types of data (like numbers, pictures, videos, texts, audio files, and much more) in order to create intelligent systems that can mimic the cognitive actions of human brains<\/strong> in order to derive meaningful insights from an increasingly complex flood of information.<\/p>\n\n\n\n


What is Machine Learning?<\/h2>\n\n\n\n


Machine learning basics<\/h3>\n\n\n\n

Machine learning (ML) <\/strong>refers to a part of artificial intelligence (AI) <\/strong>that focuses on computer algorithms that are able to learn and improve their own accuracy gradually and automatically through experience, without any human assistance or being programmed to do so. <\/p>\n\n\n

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\n ML algorithms are designed to identify patterns in data and help people make better predictions and decisions. <\/div>\n <\/div>\n <\/div>\n\n\n\n


How can machine learning be applied \u2013 7 examples
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  1. Image & speech recognition <\/strong>\u2013 for face recognition in crime detection or speech-to-text processing in translations.

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  2. Behaviour and trends prospecting <\/strong>\u2013 for sales and marketing purposes, in order to create more accurate buyer personae and prepare for future demands.

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  3. Fraud detection<\/strong> \u2013 machine learning algorithms are able to behave like hundreds of security experts who are all working on the same issue simultaneously, efficiently, and with a high degree of accuracy to detect abnormalities and protect your systems.

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  4. Injury predictions<\/strong> \u2013 ML algorithms can observe patterns in the muscle movements of athletes and send alerts to their coaches in the case of any anomalies.

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  5. Traffic prediction<\/strong> \u2013 based on both real-time and historical traffic patterns, ML algorithms can provide delivery men with optimal routes to travel between multiple addresses.

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  6. Mental illness detection<\/strong> \u2013 based on content published by users on social media, ML algorithms can be trained to identify different feelings and emotional states through their posts and identify disorders (like depression, autism or schizophrenia).

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  7. Virtual personal assistant<\/strong> \u2013 these algorithms can also provide you with daily updates, schedule appointments, take notes and help optimise your weekly activities.<\/li>\n<\/ol>\n\n\n\n


    The core difference between data science and machine learning<\/h3>\n\n\n\n

    Data science studies data and focuses on extracting meaning from it<\/a>, while machine learning refers to a set of tools, technologies and methods for building models that are able to learn on their own without human intervention. Machine learning is often leveraged by data scientists, however, this is not always necessary \u2013 it all depends on your goals.<\/p>\n\n\n\n


    Predictive analytics vs prescriptive analytics<\/h3>\n\n\n\n

    There are also two more concepts that are inseparably linked to data science and machine learning which we should look into now: predictive analytics and prescriptive analytics. The difference between them lies in the outcomes of their analyses. The first one provides you with raw information<\/strong> on what could happen in the future, so you can come up with an appropriate plan based on those predictions. The latter provides you with a few different action plans that are ready for implementation<\/strong>, which you can compare, select and then apply immediately. This option is definitely more advanced and can significantly accelerate business decision-making processes.<\/p>\n\n\n\n


    Data Science & Machine Learning techniques<\/h2>\n\n\n\n

    And last but not least \u2013 the techniques. Both data science and machine learning use a number of various technologies and operational methods. However, as I mentioned above, ML also refers to a set of techniques that is often leveraged by data scientists <\/strong>(apart from others, such as linear regression, decision trees or dimensionality reduction, etc.).
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    Machine learning itself often uses two types of techniques:<\/strong>
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